USE OF HISTOGRAM EQUALIZATION IN IMAGE PROCESSING FOR IMAGE ENHANCEMENT

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "USE OF HISTOGRAM EQUALIZATION IN IMAGE PROCESSING FOR IMAGE ENHANCEMENT"

Transcription

1 USE OF HISTOGRAM EQUALIZATION IN IMAGE PROCESSING FOR IMAGE ENHANCEMENT Sapana S. Bagade M.E,Computer Engineering, Sipna s C.O.E.T,Amravati, Amravati,India Vijaya K. Shandilya Assistant Proffessor,Computer Science & Engg Dept, Sipna s C.O.E.T,Amravati, Amravati,India Abstract Digital Image Processing is a rapidly evolving field with the growing applications in science & engineering. Image Processing holds the possibility of developing an ultimate machine that could perform visual functions of all living beings. The image processing is a visual task, the foremost step is to obtain an image i.e. image acquisition then enhancement and finally to process. In this paper there are details for image enhancement for the purpose of image processing. Image enhancement is basically improving the digital image quality. Image histogram is helpful in image enhancement. The histogram in the context of image processing is the operation by which the occurrences of each intensity value in the image is shown and Histogram equalization is the technique by which the dynamic range of the histogram of an image is increased. Keywords- Image processing, image enhancement, image histogram, Histogram equalization 1. Introduction Image processing is in many cases concerned with taking one array of pixels as input and producing another array of pixels as output which in some way represents an improvement to the original array. The term Digital Image Processing generally refers to the processing of a two-dimensional picture by a digital computer i.e. altering an existing image in the desired manner. For example, this processing may remove noise, improve the contrast of the image, remove blurring caused by movement of the camera during image acquisition, it may correct for geometrical distortions caused by the lens. Before going for image processing image enhancement is necessary. We will not be considering every image processing and enhancement technique in this section but we will see the enhancement of image through image histogram or better way histogram equalization. If an image is low contrast and dark, we wish to improve its contrast and brightness. The histogram equalization improve all parts of the image when the original image is irregularly illuminated. The enhancement techniques are employed in order to increase the contrast of an image. Therefore, the distinction of features in the scene can be easily performed by visualization. This will augment the efficiency of image classification and interpretation. Generally, an image can be enhanced by spreading out the range of scene illumination. This procedure is called contrast stretch. If the ranges of gray values are uniformly prolonged, the process will be called linear contrast stretch. The disadvantage of the linear contrast stretch is that a number of gray levels are equally assigned to the unusually appeared gray levels as to the often appeared gray levels. This effect still causes the ambiguous distinction of the similar features. To overcome the mention defect, the process of histogram equalization is applied. The process tries to assigned more number of gray levels to the frequency appeared gray levels. The enhanced image obtained from the global area histogram equalization will cause an effect of intensity saturation in darkness area and whiteness areas. The color image enhancement will be obtained by encoding the color of red, green and blue to three different spectral images. 2. Image Processing Technique As has just been established, a number of factors can adversely affect image quality. With the use of image enhancement techniques, the difference in sensitivity between image can be decreased. A number of image processing techniques, in addition to enhancement techniques, can be applied to improve the data usefulness. Techniques include convolution edge detection, mathematics, filters, trend removal, and image analysis. The various image enhancements and image processing techniques will be introduced in this section. Computer software programs are available, including some or all of the following programs: Enhancement programs make information more visible. Histogram equalization-redistributes the intensities of the image of the entire range of possible intensities (usually 256 gray-scale levels). 6

2 Unsharp masking-subtracts smoothed image from the original image to emphasize intensity changes. Convolution programs are 3-by-3 masks operating on pixel neighborhoods. Highpass filter-emphasizes regions with rapid intensity changes. Lowpass filter-smoothes images, blurs regions with rapid changes 2.1 Image Enhancement Technique Image enhancement techniques improve the quality of an image a perceived by a human. These techniques are most useful because many images when examined on a color display give inadequate information for image interpretation. There is no conscious effort to improve the fidelity of the image with regard to some ideal form of the image. There exists a wide variety of techniques for improving image quality. The contrast stretch, density slicing, edge enhancement, and spatial filtering are the more commonly used techniques. Image enhancement is attempted after the image is corrected for geometric and radiometric distortions. Image enhancement methods are applied separately to each band of a multispectral image. Digital techniques have been found to be most satisfactory than the photographic technique for image enhancement, because of the precision and wide variety of digital processes. Contrast Contrast generally refers to the difference in luminance or grey level values in an image and is an important characteristic. It can be defined as the ratio of the maximum intensity to the minimum intensity over an image. Contrast ratio has a strong bearing on the resolving power and detects ability of an image. Larger this ratio, more easy it is to interpret the image. Contrast Enhancement Contrast enhancement techniques expand the range of brightness values in an image so that the image can be efficiently displayed in a manner desired by the analyst. The density values in a scene are literally pulled farther apart, that is, expanded over a greater range. The effect is to increase the visual contrast between two areas of different uniform densities. This enables the analyst to discriminate easily between areas initially having a small difference in density. Non-Linear Contrast Enhancement In these methods, the input and output data values follow a non-linear transformation. The general form of the non-linear contrast enhancement is defined by y = f (x), where x is the input data value and y is the output data value. The non-linear contrast enhancement techniques have been found to be useful for enhancing the color contrast between the nearly classes and subclasses of a main class. A type of non linear contrast stretch involves scaling the input data logarithmically. This enhancement has greatest impact on the brightness values found in the darker part of histogram. It could be reversed to enhance values in brighter part of histogram by scaling the input data using an inverse log function. Histogram equalization is another non-linear contrast enhancement technique. In this technique, histogram of the original image is redistributed to produce a uniform population density. This is obtained by grouping certain adjacent grey values. Thus the number of grey levels in the enhanced image is less than the number of grey levels in the original image 3. Histogram Histogram equalization is a widely used scheme for contrast enhancement in a variety of applications due to its simple function and effectiveness. One possible drawback of the histogram equalization is that it can change the mean brightness of an image significantly as a consequence of histogram flattening. Clearly, this is not a desirable property when preserving the original mean brightness of a given image is necessary. As an effort to overcome such drawback for extending the applications of the histogram equalization in consumer electronic products, bi-histogram equalization has been proposed by the author which is capable of preserving the mean brightness of an image while it performs contrast enhancement. The essence of the bi-histogram equalization is to utilize independent histogram equalizations separately over two subimages obtained by decomposing the input image based on its mean. A simplified version of the bi-histogram equalization is proposed, which is referred to as the quantized bi-histogram equalization. The proposed algorithm provides a much simpler hardware (H/W) structure than the bi-histogram equalization since it is based on the cumulative density function of a quantized image. Thus, the realization of bihistogram equalization in H/W is feasible, which leads to versatile applications in the field of consumer electronics 3.1 What is Histogram The histogram in the context of image processing is the operation by which the occurrences of each intensity value in the image is shown. Normally, the histogram is a graph showing the number of pixels in an image at each different intensity value found in that image. For an 8-bit grayscale image there are 256 different possible intensities, and so the histogram will graphically display 256 numbers showing the distribution of pixels amongst those grayscale values. Histogram modification is a classical method for 7

3 image enhancement, especially histogram equalization. Histogram equalization method is a self-acting process since it does not request any information, just only the probability of each intensity level of image. However, the enhanced image is obtained by the global area histogram equalization will cause an effect of intensity saturation in some areas. 3.2 What is Histogram Equalization Histogram equalization is the technique by which the dynamic range of the histogram of an image is increased. Histogram equalization assigns the intensity values of pixels in the input image such that the output image contains a uniform distribution of intensities. It improves contrast and the goal of histogram equalization is to obtain a uniform histogram. This technique can be used on a whole image or just on a part of an image. means 0, 1, 2, 3,..., 255. That is how new intensity levels are calculated for the previous intensity levels. The next step is to replace the previous intensity level with the new intensity level. This is accomplished by putting the value of Oi in the image for all the pixels, where Oi represents the new intensity value, whereas i represents the previous intensity level. To understand the working of the histogram equalization, take the example of the following image: the dynamic range of image intensities is shown by the following histogram: Histogram equalization redistributes intensity distributions. If the histogram of any image has many peaks and valleys, it will still have peaks and valley after equalization, but peaks and valley will be shifted. Because of this, "spreading" is a better term than "flattening" to describe histogram equalization. In histogram equalization, each pixel is assigned a new intensity value based on its previous intensity level. 3.3 General Working The histogram equalization is operated on an image in three step: 1). Histogram Formation 2). New Intensity Values calculation for each Intensity Levels 3). Replace the previous Intensity values with the new intensity values For the first step see the article on histogram. In step 2, new intensity values are calculated for each intensity level by applying the following equation: Fig1: Dark image The meaning of Max. Intensity Levels maximum intensity level which a pixel can get. For example, if the image is in the grayscale domain, then the count is 255. And if the image is of size 256x256 then, the No. of pixels is And the expression is the bracket means that the no. of pixels having the intensity below the output intensity level or equal to it. For example, if we are calculating the output intensity level for 1 input intensity level, then it means that the no. of pixels in the image having the intensity below or equal to 1 means 0 and 1. If we are calculating the output intensity level for 5 input intensity level, then the it means that the no. of pixels in the image having the intensity below or equal to 5 means 0, 1, 2, 3, 4, 5. Thus, if we are calculating the output intensity level for 255 input intensity level, then the it means that the no. of pixels in the image having the intensity below or equal to 255 Fig4: High contrast image 8

4 3.4 Histogram Equalization: Regional Adaptive The Histogram Equalization: Regional Adaptive algorithm, a high-pass filter, enhances the contrast in an image by reevaluating the gray-scale, or intensity, value of each pixel based on the values of nearby pixels in the same region. Background Features in some images require a larger range of intensity values than are displayed. Histogram equalization is a method to improve the contrast of an area in an image by distributing an equal number of pixels across the range of intensities in the image. This algorithm tabulates the histogram for each region, then assigns the pixel to the new histogram level. This algorithm acts in regions of an image, dividing the image into m rows and n columns. Each region is the source when remapping the histogram, adapting the value of the pixel to its location in the histogram of the region. Although, depending on image type, there are some minor variations in how this algorithm proceeds, generally the algorithm first calculates the bin width of each region. It then processes each region by judging each pixel's location within this new histogram. Regardless of pixel value, the algorithm remaps the brightest pixel in each region to the brightest value in the histogram (i.e., 256) and remaps the darkest pixel to the lowest value in the histogram (i.e., 0) of the pixels' neighboring region, recording the new values as pixelintensity values. The algorithm then processes each of the remaining regions in the original, or source, image in the same way and builds a new, or reference, image using the new pixel-intensity values. Performing histogram equalization makes it possible to see minor variations within portions of the reference image that appeared nearly uniform in the source image. Arithmetically, the process in a region is rather simple: where N i is the intensity of the ith pixel, and T is the total number of pixels in the region. The reference image using the new histogram is similar to the source image; however, in areas where the source image had similar values, the reference image is enhanced. The reference histogram now stretches over a greater proportion of the possible image intensities The histogram for the source image is very compact in structure; that is, it has a great number of darker pixels, but very few brighter pixels. In fact, the bottom-most intensity values make up almost all of the pixels' values in the image. Note, however, in the histogram of the reference image, the look-up table is more evenly spread than that of the histogram for the source image. Also, areas of low intensity and low contrast in the source image were correspondingly remapped in the reference image to areas of higher contrast and generally overall brighter values. Notice that different low areas of the reference image are more distinguishable depending on the number of rows and columns that divide the image. The contrast changes are due to which pixel is considered the brightest and which the darkest in a particular region. The brightest pixel in a 1 x 1 separated image is the brightest of the image. However, when the image is divided into more regions, the brightest pixel in the region gets remapped as one of the brightest pixels in the image regardless of its absolute intensity in the image. Likewise, the darkest pixel in the region is remapped as one of the darkest in the image regardless of its absolute intensity. The histogram equalization process can enforce certain characteristics about the image called clipping. As the algorithm processes the image, it counts out the number of pixels of certain intensity. Clipping cuts that counting short; it stores the excess pixels of a particular intensity and redistributes that number equally among all intensities. For instance, if a particular intensity must be clipped at 100 and the count is 145, the algorithm redistributes 45 pixels to other brightness. It adds to the count of each intensity level an equal number of pixels if there are enough excess, but it attempts to spread the excess over as many pixels as possible. Gamma correction Gamma correction operation performs nonlinear brightness adjustment. Brightness for darker pixels is increased, but it is almost the same for bright pixels. As result more details are visible. 4. Conclusion Histogram equalization is powerful method for image enhancement and it will increase the contrast of image. The enhanced image will give the full dynamic range of histogram. However, histogram equalization process tries to merge the adjacent gray levels together in order to force the uniformity of number of pixels in each appeared gray levels. Consequently, the intensity saturation will be presented in darkness regions and whiteness region. Histogram equalization assigns the intensity values of pixels in the input image such that the output image contains a uniform distribution of intensities. It improves contrast and obtain a uniform histogram. This technique can be used on a whole image or just on a part of an image. 5. References [1]. Bob Cromwell, "Localized Contrast Enhancement: Histogram Equalization," paper on his web site at 9

5 [2]. John Russ, The Image Processing Handbook (Boca Raton, Florida: CRC Press LLC, 2003). [3]. J. Alex Stark, "Adaptive Image Contrast Enhancement Using Generalizations of Histogram Equalization," in IEEE Transactions on Image Processing (May 2000). [4]. http// gramandnormalizedhistograminc.aspx [5]. Dr A D Marshall, Vision Systems, Vision_lecture_caller.tex. [6]. ROHAN KAR IMAGE PROCESSING OPTICAL CHARACTER RECOGNITION Seminar Report 2004 Monsoon [7]. http// ed.org/educationresources/communitycollege/radiography/adv ancedtechniques/real_time_radiography/imageprocessingtechn iques.htm [8]. Minakshi Kumar DIGITAL IMAGE PROCESSING, Photogrammetry and Remote Sensing Division Indian Institute of Remote Sensing, Dehra Dun [9]. V.V. Starovoitov, D.I Samal, D.V. Briliuk Image Enhancement for Face Recognition,Submitted to International Conference on Iconics, 2003, St.Petersburg, Russia. [10]. YuWang; QianChen; BaeominZhang; Image enhancement based on equal area dualistic sub-image histogram equalization method 06 August 2002 IEEE Consumer Electronics Society 10

Survey on Image Contrast Enhancement Techniques

Survey on Image Contrast Enhancement Techniques Survey on Image Contrast Enhancement Techniques Rashmi Choudhary, Sushopti Gawade Department of Computer Engineering PIIT, Mumbai University, India Abstract: Image enhancement is a processing on an image

More information

Histogram Equalization: A Strong Technique for Image Enhancement

Histogram Equalization: A Strong Technique for Image Enhancement , pp.345-352 http://dx.doi.org/10.14257/ijsip.2015.8.8.35 Histogram Equalization: A Strong Technique for Image Enhancement Ravindra Pal Singh and Manish Dixit Dept. of Comp. Science/IT MITS Gwalior, 474005

More information

Contrast Enhancement Techniques using Histogram Equalization: A Survey

Contrast Enhancement Techniques using Histogram Equalization: A Survey Research Article International Journal of Current Engineering and Technology E-ISSN 2277 4106, P-ISSN 2347-5161 2014 INPRESSCO, All Rights Reserved Available at http://inpressco.com/category/ijcet Contrast

More information

Review and Analysis of Image Enhancement Techniques

Review and Analysis of Image Enhancement Techniques International Journal of Information & Computation Technology. ISSN 0974-2239 Volume 4, Number 6 (2014), pp. 583-590 International Research Publications House http://www. irphouse.com Review and Analysis

More information

DIGITAL IMAGE PROCESSING (COM-3371) Week 2 - January 14, 2002

DIGITAL IMAGE PROCESSING (COM-3371) Week 2 - January 14, 2002 DIGITAL IMAGE PROCESSING (COM-3371) Week 2 - January 14, 22 Topics: Human eye Visual phenomena Simple image model Image enhancement Point processes Histogram Lookup tables Contrast compression and stretching

More information

NON UNIFORM BACKGROUND REMOVAL FOR PARTICLE ANALYSIS BASED ON MORPHOLOGICAL STRUCTURING ELEMENT:

NON UNIFORM BACKGROUND REMOVAL FOR PARTICLE ANALYSIS BASED ON MORPHOLOGICAL STRUCTURING ELEMENT: IJCE January-June 2012, Volume 4, Number 1 pp. 59 67 NON UNIFORM BACKGROUND REMOVAL FOR PARTICLE ANALYSIS BASED ON MORPHOLOGICAL STRUCTURING ELEMENT: A COMPARATIVE STUDY Prabhdeep Singh1 & A. K. Garg2

More information

Digital Image Processing

Digital Image Processing Digital Image Processing Part 2: Image Enhancement Digital Image Processing Course Introduction in the Spatial Domain Lecture AASS Learning Systems Lab, Teknik Room T26 achim.lilienthal@tech.oru.se Course

More information

CoE4TN4 Image Processing. Chapter 3: Intensity Transformation and Spatial Filtering

CoE4TN4 Image Processing. Chapter 3: Intensity Transformation and Spatial Filtering CoE4TN4 Image Processing Chapter 3: Intensity Transformation and Spatial Filtering Image Enhancement Enhancement techniques: to process an image so that the result is more suitable than the original image

More information

Preparing Remote Sensing Data for Natural Resources Mapping (image enhancement, rectifications )

Preparing Remote Sensing Data for Natural Resources Mapping (image enhancement, rectifications ) Preparing Remote Sensing Data for Natural Resources Mapping (image enhancement, rectifications ) Why is this important What are the major approaches Examples of digital image enhancement Follow up exercises

More information

A Survey on Image Contrast Enhancement

A Survey on Image Contrast Enhancement A Survey on Image Contrast Enhancement Kunal Dhote 1, Anjali Chandavale 2 1 Department of Information Technology, MIT College of Engineering, Pune, India 2 SMIEEE, Department of Information Technology,

More information

Fuzzy Statistics Based Multi-HE for Image Enhancement with Brightness Preserving Behaviour

Fuzzy Statistics Based Multi-HE for Image Enhancement with Brightness Preserving Behaviour International Journal of Engineering and Management Research, Volume-3, Issue-3, June 2013 ISSN No.: 2250-0758 Pages: 47-51 www.ijemr.net Fuzzy Statistics Based Multi-HE for Image Enhancement with Brightness

More information

HISTOGRAM EXPANSION-A TECHNIQUE OF HISTOGRAM EQULIZATION

HISTOGRAM EXPANSION-A TECHNIQUE OF HISTOGRAM EQULIZATION HISTOGRAM EXPANSION-A TECHNIQUE OF HISTOGRAM EQULIZATION Jasdeep Kaur 1, Nancy 2, Nishu 3, Ramneet Kaur 4 1,2,3, 4 M.Tech, Guru Nanak Dev Engg College, Ludhiana Abstract In this paper I have described

More information

Digital Image Processing

Digital Image Processing Digital Image Processing 1 Patrick Olomoshola, 2 Taiwo Samuel Afolayan 1,2 Surveying & Geoinformatic Department, Faculty of Environmental Sciences, Rufus Giwa Polytechnic, Owo. Nigeria Abstract: This paper

More information

Image Processing for feature extraction

Image Processing for feature extraction Image Processing for feature extraction 1 Outline Rationale for image pre-processing Gray-scale transformations Geometric transformations Local preprocessing Reading: Sonka et al 5.1, 5.2, 5.3 2 Image

More information

Image Enhancement contd. An example of low pass filters is:

Image Enhancement contd. An example of low pass filters is: Image Enhancement contd. An example of low pass filters is: We saw: unsharp masking is just a method to emphasize high spatial frequencies. We get a similar effect using high pass filters (for instance,

More information

1.Discuss the frequency domain techniques of image enhancement in detail.

1.Discuss the frequency domain techniques of image enhancement in detail. 1.Discuss the frequency domain techniques of image enhancement in detail. Enhancement In Frequency Domain: The frequency domain methods of image enhancement are based on convolution theorem. This is represented

More information

High Dynamic Range Imaging

High Dynamic Range Imaging High Dynamic Range Imaging 1 2 Lecture Topic Discuss the limits of the dynamic range in current imaging and display technology Solutions 1. High Dynamic Range (HDR) Imaging Able to image a larger dynamic

More information

A simple Technique for contrast stretching by the Addition, subtraction& HE of gray levels in digital image

A simple Technique for contrast stretching by the Addition, subtraction& HE of gray levels in digital image Volume 6, No. 5, May - June 2015 International Journal of Advanced Research in Computer Science RESEARCH PAPER Available Online at www.ijarcs.info A simple Technique for contrast stretching by the Addition,

More information

Contrast Enhancement Using Bi-Histogram Equalization With Brightness Preservation

Contrast Enhancement Using Bi-Histogram Equalization With Brightness Preservation Contrast Enhancement Using Bi-Histogram Equalization With Brightness Preservation 1 Gowthami Rajagopal, 2 K.Santhi 1 PG Student, Department of Electronics and Communication K S Rangasamy College Of Technology,

More information

Effective Contrast Enhancement using Adaptive Gamma Correction and Weighting Distribution Function

Effective Contrast Enhancement using Adaptive Gamma Correction and Weighting Distribution Function e t International Journal on Emerging Technologies (Special Issue on ICRIET-2016) 7(2): 299-303(2016) ISSN No. (Print) : 0975-8364 ISSN No. (Online) : 2249-3255 Effective Contrast Enhancement using Adaptive

More information

DIGITAL IMAGE PROCESSING Quiz exercises preparation for the midterm exam

DIGITAL IMAGE PROCESSING Quiz exercises preparation for the midterm exam DIGITAL IMAGE PROCESSING Quiz exercises preparation for the midterm exam In the following set of questions, there are, possibly, multiple correct answers (1, 2, 3 or 4). Mark the answers you consider correct.

More information

Mod. 2 p. 1. Prof. Dr. Christoph Kleinn Institut für Waldinventur und Waldwachstum Arbeitsbereich Fernerkundung und Waldinventur

Mod. 2 p. 1. Prof. Dr. Christoph Kleinn Institut für Waldinventur und Waldwachstum Arbeitsbereich Fernerkundung und Waldinventur Histograms of gray values for TM bands 1-7 for the example image - Band 4 and 5 show more differentiation than the others (contrast=the ratio of brightest to darkest areas of a landscape). - Judging from

More information

Digital Image Processing

Digital Image Processing Digital Image Processing Lecture # 5 Image Enhancement in Spatial Domain- I ALI JAVED Lecturer SOFTWARE ENGINEERING DEPARTMENT U.E.T TAXILA Email:: ali.javed@uettaxila.edu.pk Office Room #:: 7 Presentation

More information

A Study for Applications of Histogram in Image Enhancement

A Study for Applications of Histogram in Image Enhancement The International Journal of Engineering and Science (IJES) Volume 6 Issue 6 Pages PP 59-63 2017 ISSN (e): 2319 1813 ISSN (p): 2319 1805 A Study for Applications of in Image Enhancement Harpreet Kaur 1,

More information

Bi-Level Weighted Histogram Equalization with Adaptive Gamma Correction

Bi-Level Weighted Histogram Equalization with Adaptive Gamma Correction International Journal of Computational Engineering Research Vol, 04 Issue, 3 Bi-Level Weighted Histogram Equalization with Adaptive Gamma Correction Jeena Baby 1, V. Karunakaran 2 1 PG Student, Department

More information

Image Enhancement in Spatial Domain

Image Enhancement in Spatial Domain Image Enhancement in Spatial Domain 2 Image enhancement is a process, rather a preprocessing step, through which an original image is made suitable for a specific application. The application scenarios

More information

Filtering. Image Enhancement Spatial and Frequency Based

Filtering. Image Enhancement Spatial and Frequency Based Filtering Image Enhancement Spatial and Frequency Based Brent M. Dingle, Ph.D. 2015 Game Design and Development Program Mathematics, Statistics and Computer Science University of Wisconsin - Stout Lecture

More information

Image Enhancement using Histogram Approach

Image Enhancement using Histogram Approach Image Enhancement using Histogram Approach Shivali Arya Institute of Engineering and Technology Jaipur Krishan Kant Lavania Arya Institute of Engineering and Technology Jaipur Rajiv Kumar Gurgaon Institute

More information

EFFICIENT CONTRAST ENHANCEMENT USING GAMMA CORRECTION WITH MULTILEVEL THRESHOLDING AND PROBABILITY BASED ENTROPY

EFFICIENT CONTRAST ENHANCEMENT USING GAMMA CORRECTION WITH MULTILEVEL THRESHOLDING AND PROBABILITY BASED ENTROPY EFFICIENT CONTRAST ENHANCEMENT USING GAMMA CORRECTION WITH MULTILEVEL THRESHOLDING AND PROBABILITY BASED ENTROPY S.Gayathri 1, N.Mohanapriya 2, B.Kalaavathi 3 1 PG student, Computer Science and Engineering,

More information

Various Image Enhancement Techniques for Skin Cancer Detection Using Mobile App

Various Image Enhancement Techniques for Skin Cancer Detection Using Mobile App Various Image Enhancement Techniques for Skin Cancer Detection Using Mobile App Manasvi Kalra Genesis Futuristic Technologies Ltd. Noida-201307, India manasvi.k@genesis-in.com Abstract This paper aims

More information

Figure 1 HDR image fusion example

Figure 1 HDR image fusion example TN-0903 Date: 10/06/09 Using image fusion to capture high-dynamic range (hdr) scenes High dynamic range (HDR) refers to the ability to distinguish details in scenes containing both very bright and relatively

More information

Image Processing Lecture 4

Image Processing Lecture 4 Image Enhancement Image enhancement aims to process an image so that the output image is more suitable than the original. It is used to solve some computer imaging problems, or to improve image quality.

More information

Image Enhancement (from Chapter 13) (V6)

Image Enhancement (from Chapter 13) (V6) Image Enhancement (from Chapter 13) (V6) Astronomical images often span a wide range of brightness, while important features contained in them span a very narrow range of brightness. Alternatively, interesting

More information

Enhance Image using Dynamic Histogram and Data Hiding Technique

Enhance Image using Dynamic Histogram and Data Hiding Technique _ Enhance Image using Dynamic Histogram and Data Hiding Technique 1 D.Bharadwaja, 2 Y.V.N.Tulasi 1 Department of CSE, Gudlavalleru Engineering College, Email: bharadwaja599@gmail.com 2 Department of CSE,

More information

Digital Image Processing

Digital Image Processing Digital Image Processing Digital Imaging Fundamentals Christophoros Nikou cnikou@cs.uoi.gr Images taken from: R. Gonzalez and R. Woods. Digital Image Processing, Prentice Hall, 2008. Digital Image Processing

More information

Digital Image Fundamentals. Digital Image Processing. Human Visual System. Contents. Structure Of The Human Eye (cont.) Structure Of The Human Eye

Digital Image Fundamentals. Digital Image Processing. Human Visual System. Contents. Structure Of The Human Eye (cont.) Structure Of The Human Eye Digital Image Processing 2 Digital Image Fundamentals Digital Imaging Fundamentals Christophoros Nikou cnikou@cs.uoi.gr Those who wish to succeed must ask the right preliminary questions Aristotle Images

More information

Digital Image Processing

Digital Image Processing Digital Image Processing Digital Imaging Fundamentals Christophoros Nikou cnikou@cs.uoi.gr Images taken from: R. Gonzalez and R. Woods. Digital Image Processing, Prentice Hall, 2008. Digital Image Processing

More information

Image Processing by Bilateral Filtering Method

Image Processing by Bilateral Filtering Method ABHIYANTRIKI An International Journal of Engineering & Technology (A Peer Reviewed & Indexed Journal) Vol. 3, No. 4 (April, 2016) http://www.aijet.in/ eissn: 2394-627X Image Processing by Bilateral Image

More information

Spatial Domain Processing and Image Enhancement

Spatial Domain Processing and Image Enhancement Spatial Domain Processing and Image Enhancement Lecture 4, Feb 18 th, 2008 Lexing Xie EE4830 Digital Image Processing http://www.ee.columbia.edu/~xlx/ee4830/ thanks to Shahram Ebadollahi and Min Wu for

More information

A Study of Slanted-Edge MTF Stability and Repeatability

A Study of Slanted-Edge MTF Stability and Repeatability A Study of Slanted-Edge MTF Stability and Repeatability Jackson K.M. Roland Imatest LLC, 2995 Wilderness Place Suite 103, Boulder, CO, USA ABSTRACT The slanted-edge method of measuring the spatial frequency

More information

Contrast Image Correction Method

Contrast Image Correction Method Contrast Image Correction Method Journal of Electronic Imaging, Vol. 19, No. 2, 2010 Raimondo Schettini, Francesca Gasparini, Silvia Corchs, Fabrizio Marini, Alessandro Capra, and Alfio Castorina Presented

More information

PRACTICAL IMAGE AND VIDEO PROCESSING USING MATLAB

PRACTICAL IMAGE AND VIDEO PROCESSING USING MATLAB PRACTICAL IMAGE AND VIDEO PROCESSING USING MATLAB OGE MARQUES Florida Atlantic University *IEEE IEEE PRESS WWILEY A JOHN WILEY & SONS, INC., PUBLICATION CONTENTS LIST OF FIGURES LIST OF TABLES FOREWORD

More information

from: Point Operations (Single Operands)

from:  Point Operations (Single Operands) from: http://www.khoral.com/contrib/contrib/dip2001 Point Operations (Single Operands) Histogram Equalization Histogram equalization is as a contrast enhancement technique with the objective to obtain

More information

8.2 IMAGE PROCESSING VERSUS IMAGE ANALYSIS Image processing: The collection of routines and

8.2 IMAGE PROCESSING VERSUS IMAGE ANALYSIS Image processing: The collection of routines and 8.1 INTRODUCTION In this chapter, we will study and discuss some fundamental techniques for image processing and image analysis, with a few examples of routines developed for certain purposes. 8.2 IMAGE

More information

Contrast Enhancement using Improved Adaptive Gamma Correction With Weighting Distribution Technique

Contrast Enhancement using Improved Adaptive Gamma Correction With Weighting Distribution Technique Contrast Enhancement using Improved Adaptive Gamma Correction With Weighting Distribution Seema Rani Research Scholar Computer Engineering Department Yadavindra College of Engineering Talwandi sabo, Bathinda,

More information

Basic Digital Image Processing. The Structure of Digital Images. An Overview of Image Processing. Image Restoration: Line Drop-outs

Basic Digital Image Processing. The Structure of Digital Images. An Overview of Image Processing. Image Restoration: Line Drop-outs Basic Digital Image Processing A Basic Introduction to Digital Image Processing ~~~~~~~~~~ Rev. Ronald J. Wasowski, C.S.C. Associate Professor of Environmental Science University of Portland Portland,

More information

Image Enhancement in Spatial Domain: A Comprehensive Study

Image Enhancement in Spatial Domain: A Comprehensive Study 17th Int'l Conf. on Computer and Information Technology, 22-23 December 2014, Daffodil International University, Dhaka, Bangladesh Image Enhancement in Spatial Domain: A Comprehensive Study Shanto Rahman

More information

IMAGE ENHANCEMENT IN SPATIAL DOMAIN

IMAGE ENHANCEMENT IN SPATIAL DOMAIN A First Course in Machine Vision IMAGE ENHANCEMENT IN SPATIAL DOMAIN By: Ehsan Khoramshahi Definitions The principal objective of enhancement is to process an image so that the result is more suitable

More information

An Advanced Contrast Enhancement Using Partially Overlapped Sub-Block Histogram Equalization

An Advanced Contrast Enhancement Using Partially Overlapped Sub-Block Histogram Equalization IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 11, NO. 4, APRIL 2001 475 An Advanced Contrast Enhancement Using Partially Overlapped Sub-Block Histogram Equalization Joung-Youn Kim,

More information

Enhancement of Multispectral Images and Vegetation Indices

Enhancement of Multispectral Images and Vegetation Indices Enhancement of Multispectral Images and Vegetation Indices ERDAS Imagine 2016 Description: We will use ERDAS Imagine with multispectral images to learn how an image can be enhanced for better interpretation.

More information

Efficient Contrast Enhancement Using Adaptive Gamma Correction and Cumulative Intensity Distribution

Efficient Contrast Enhancement Using Adaptive Gamma Correction and Cumulative Intensity Distribution Efficient Contrast Enhancement Using Adaptive Gamma Correction and Cumulative Intensity Distribution Yi-Sheng Chiu, Fan-Chieh Cheng and Shih-Chia Huang Department of Electronic Engineering, National Taipei

More information

Image Enhancement And Analysis Of Thermal Images Using Various Techniques Of Image Processing

Image Enhancement And Analysis Of Thermal Images Using Various Techniques Of Image Processing Image Enhancement And Analysis Of Thermal Images Using Various Techniques Of Image Processing *Ms. Shweta Tyagi **Hemant Amhia (M.E. student Deptt. of Electrical Engineering, JEC Jabalpur) ( Asstt.Professor,

More information

Graphics and Image Processing Basics

Graphics and Image Processing Basics EST 323 / CSE 524: CG-HCI Graphics and Image Processing Basics Klaus Mueller Computer Science Department Stony Brook University Julian Beever Optical Illusion: Sidewalk Art Julian Beever Optical Illusion:

More information

CS 89.15/189.5, Fall 2015 ASPECTS OF DIGITAL PHOTOGRAPHY COMPUTATIONAL. Image Processing Basics. Wojciech Jarosz

CS 89.15/189.5, Fall 2015 ASPECTS OF DIGITAL PHOTOGRAPHY COMPUTATIONAL. Image Processing Basics. Wojciech Jarosz CS 89.15/189.5, Fall 2015 COMPUTATIONAL ASPECTS OF DIGITAL PHOTOGRAPHY Image Processing Basics Wojciech Jarosz wojciech.k.jarosz@dartmouth.edu Domain, range Domain vs. range 2D plane: domain of images

More information

CONTRAST ENHANCEMENT WITH CONSIDERING VISUAL EFFECTS BASED ON GRAY-LEVEL GROUPING

CONTRAST ENHANCEMENT WITH CONSIDERING VISUAL EFFECTS BASED ON GRAY-LEVEL GROUPING Journal of Marine Science and Technology DOI:.69/JMST--66- This article has been peer reviewed and accepted for publication in JMST but has not yet been copyediting, typesetting, pagination and proofreading

More information

Digital Image Processing. Lecture # 4 Image Enhancement (Histogram)

Digital Image Processing. Lecture # 4 Image Enhancement (Histogram) Digital Image Processing Lecture # 4 Image Enhancement (Histogram) 1 Histogram of a Grayscale Image Let I be a 1-band (grayscale) image. I(r,c) is an 8-bit integer between 0 and 255. Histogram, h I, of

More information

Image Deblurring and Noise Reduction in Python TJHSST Senior Research Project Computer Systems Lab

Image Deblurring and Noise Reduction in Python TJHSST Senior Research Project Computer Systems Lab Image Deblurring and Noise Reduction in Python TJHSST Senior Research Project Computer Systems Lab 2009-2010 Vincent DeVito June 16, 2010 Abstract In the world of photography and machine vision, blurry

More information

Image Enhancement Techniques Based on Histogram Equalization

Image Enhancement Techniques Based on Histogram Equalization International Journal of Advances in Electrical and Electronics Engineering 69 ISSN: 2319-1112 Image Enhancement Techniques Based on Histogram Equalization Rahul Jaiswal 1, A.G. Rao 2, H.P. Shukla 3 1

More information

Towards Real-time Hardware Gamma Correction for Dynamic Contrast Enhancement

Towards Real-time Hardware Gamma Correction for Dynamic Contrast Enhancement Towards Real-time Gamma Correction for Dynamic Contrast Enhancement Jesse Scott, Ph.D. Candidate Integrated Design Services, College of Engineering, Pennsylvania State University University Park, PA jus2@engr.psu.edu

More information

IMAGE PROCESSING PAPER PRESENTATION ON IMAGE PROCESSING

IMAGE PROCESSING PAPER PRESENTATION ON IMAGE PROCESSING IMAGE PROCESSING PAPER PRESENTATION ON IMAGE PROCESSING PRESENTED BY S PRADEEP K SUNIL KUMAR III BTECH-II SEM, III BTECH-II SEM, C.S.E. C.S.E. pradeep585singana@gmail.com sunilkumar5b9@gmail.com CONTACT:

More information

An Efficient Color Image Segmentation using Edge Detection and Thresholding Methods

An Efficient Color Image Segmentation using Edge Detection and Thresholding Methods 19 An Efficient Color Image Segmentation using Edge Detection and Thresholding Methods T.Arunachalam* Post Graduate Student, P.G. Dept. of Computer Science, Govt Arts College, Melur - 625 106 Email-Arunac682@gmail.com

More information

Local Adaptive Contrast Enhancement for Color Images

Local Adaptive Contrast Enhancement for Color Images Local Adaptive Contrast for Color Images Judith Dijk, Richard J.M. den Hollander, John G.M. Schavemaker and Klamer Schutte TNO Defence, Security and Safety P.O. Box 96864, 2509 JG The Hague, The Netherlands

More information

Color Reproduction. Chapter 6

Color Reproduction. Chapter 6 Chapter 6 Color Reproduction Take a digital camera and click a picture of a scene. This is the color reproduction of the original scene. The success of a color reproduction lies in how close the reproduced

More information

Image acquisition. In both cases, the digital sensing element is one of the following: Line array Area array. Single sensor

Image acquisition. In both cases, the digital sensing element is one of the following: Line array Area array. Single sensor Image acquisition Digital images are acquired by direct digital acquisition (digital still/video cameras), or scanning material acquired as analog signals (slides, photographs, etc.). In both cases, the

More information

Image Enhancement Using Histogram Equalization and Histogram Specification on Different Color Spaces

Image Enhancement Using Histogram Equalization and Histogram Specification on Different Color Spaces Image Enhancement Using Histogram Equalization and Histogram Specification on Different Color Spaces Pankaj Kumar Roll. 109CS0596 A thesis submitted in partial fulfillment for the degree of Bachelor of

More information

Novel Histogram Processing for Colour Image Enhancement

Novel Histogram Processing for Colour Image Enhancement Novel Histogram Processing for Colour Image Enhancement Jiang Duan and Guoping Qiu School of Computer Science, The University of Nottingham, United Kingdom Abstract: Histogram equalization is a well-known

More information

Performance Evaluation of Edge Detection Techniques for Square Pixel and Hexagon Pixel images

Performance Evaluation of Edge Detection Techniques for Square Pixel and Hexagon Pixel images Performance Evaluation of Edge Detection Techniques for Square Pixel and Hexagon Pixel images Keshav Thakur 1, Er Pooja Gupta 2,Dr.Kuldip Pahwa 3, 1,M.Tech Final Year Student, Deptt. of ECE, MMU Ambala,

More information

Color Transformations

Color Transformations Color Transformations It is useful to think of a color image as a vector valued image, where each pixel has associated with it, as vector of three values. Each components of this vector corresponds to

More information

ROBOT VISION. Dr.M.Madhavi, MED, MVSREC

ROBOT VISION. Dr.M.Madhavi, MED, MVSREC ROBOT VISION Dr.M.Madhavi, MED, MVSREC Robotic vision may be defined as the process of acquiring and extracting information from images of 3-D world. Robotic vision is primarily targeted at manipulation

More information

SYDE 575: Introduction to Image Processing. Adaptive Color Enhancement for Color vision Deficiencies

SYDE 575: Introduction to Image Processing. Adaptive Color Enhancement for Color vision Deficiencies SYDE 575: Introduction to Image Processing Adaptive Color Enhancement for Color vision Deficiencies Color vision deficiencies Statistics show that color vision deficiencies affect 8.7% of the male population

More information

Using Curves and Histograms

Using Curves and Histograms Written by Jonathan Sachs Copyright 1996-2003 Digital Light & Color Introduction Although many of the operations, tools, and terms used in digital image manipulation have direct equivalents in conventional

More information

Lecture 4: Spatial Domain Processing and Image Enhancement

Lecture 4: Spatial Domain Processing and Image Enhancement I2200: Digital Image processing Lecture 4: Spatial Domain Processing and Image Enhancement Prof. YingLi Tian Sept. 27, 2017 Department of Electrical Engineering The City College of New York The City University

More information

IMAGE PROCESSING: AREA OPERATIONS (FILTERING)

IMAGE PROCESSING: AREA OPERATIONS (FILTERING) IMAGE PROCESSING: AREA OPERATIONS (FILTERING) N. C. State University CSC557 Multimedia Computing and Networking Fall 2001 Lecture # 13 IMAGE PROCESSING: AREA OPERATIONS (FILTERING) N. C. State University

More information

Image Denoising Using Statistical and Non Statistical Method

Image Denoising Using Statistical and Non Statistical Method Image Denoising Using Statistical and Non Statistical Method Ms. Shefali A. Uplenchwar 1, Mrs. P. J. Suryawanshi 2, Ms. S. G. Mungale 3 1MTech, Dept. of Electronics Engineering, PCE, Maharashtra, India

More information

Digital Image Fundamentals and Image Enhancement in the Spatial Domain

Digital Image Fundamentals and Image Enhancement in the Spatial Domain Digital Image Fundamentals and Image Enhancement in the Spatial Domain Mohamed N. Ahmed, Ph.D. Introduction An image may be defined as 2D function f(x,y), where x and y are spatial coordinates. The amplitude

More information

Image analysis. CS/CME/BioE/Biophys/BMI 279 Oct. 31 and Nov. 2, 2017 Ron Dror

Image analysis. CS/CME/BioE/Biophys/BMI 279 Oct. 31 and Nov. 2, 2017 Ron Dror Image analysis CS/CME/BioE/Biophys/BMI 279 Oct. 31 and Nov. 2, 2017 Ron Dror 1 Outline Images in molecular and cellular biology Reducing image noise Mean and Gaussian filters Frequency domain interpretation

More information

Illumination based Sub Image Histogram Equalization: A Novel Method of Image Contrast Enhancement

Illumination based Sub Image Histogram Equalization: A Novel Method of Image Contrast Enhancement Illumination based Sub Image Histogram Equalization: A Novel Method of Image Contrast Enhancement Sangeeta Rani Deptt of ECE, IGDTUW, Delhi Ashwini Kumar Deptt of ECE, IGDTUW, Delhi Kuldeep Singh Central

More information

Index of Command Functions

Index of Command Functions Index of Command Functions version 2.3 Command description [keyboard shortcut]:description including special instructions. Keyboard short for a Windows PC: the Control key AND the shortcut key. For a MacIntosh:

More information

Practical Content-Adaptive Subsampling for Image and Video Compression

Practical Content-Adaptive Subsampling for Image and Video Compression Practical Content-Adaptive Subsampling for Image and Video Compression Alexander Wong Department of Electrical and Computer Eng. University of Waterloo Waterloo, Ontario, Canada, N2L 3G1 a28wong@engmail.uwaterloo.ca

More information

Color Sensitive Adaptive Gamma Correction for Image Color and Contrast Enhancement

Color Sensitive Adaptive Gamma Correction for Image Color and Contrast Enhancement RESEARCH ARTICLE OPEN ACCESS Color Sensitive Adaptive Gamma Correction for Image Color and Contrast Enhancement Asha M1, Jemimah Simon2 1Asha M Author is currently pursuing M.Tech (Information Technology)

More information

December 28, Dr. Praveen Sankaran (Department of ECE NIT Calicut DIP)

December 28, Dr. Praveen Sankaran (Department of ECE NIT Calicut DIP) Dr. Praveen Sankaran Department of ECE NIT Calicut December 28, 2012 Winter 2013 December 28, 2012 1 / 18 Outline 1 Piecewise-Linear Functions Review 2 Histogram Processing Winter 2013 December 28, 2012

More information

Histograms and Color Balancing

Histograms and Color Balancing Histograms and Color Balancing 09/14/17 Empire of Light, Magritte Computational Photography Derek Hoiem, University of Illinois Administrative stuff Project 1: due Monday Part I: Hybrid Image Part II:

More information

Dynamic Range. H. David Stein

Dynamic Range. H. David Stein Dynamic Range H. David Stein Dynamic Range What is dynamic range? What is low or limited dynamic range (LDR)? What is high dynamic range (HDR)? What s the difference? Since we normally work in LDR Why

More information

MODULE 4 LECTURE NOTES 4 DENSITY SLICING, THRESHOLDING, IHS, TIME COMPOSITE AND SYNERGIC IMAGES

MODULE 4 LECTURE NOTES 4 DENSITY SLICING, THRESHOLDING, IHS, TIME COMPOSITE AND SYNERGIC IMAGES MODULE 4 LECTURE NOTES 4 DENSITY SLICING, THRESHOLDING, IHS, TIME COMPOSITE AND SYNERGIC IMAGES 1. Introduction Digital image processing involves manipulation and interpretation of the digital images so

More information

Image Processing COS 426

Image Processing COS 426 Image Processing COS 426 What is a Digital Image? A digital image is a discrete array of samples representing a continuous 2D function Continuous function Discrete samples Limitations on Digital Images

More information

Digital Image Processing CSL 783 REPORT

Digital Image Processing CSL 783 REPORT Digital Image Processing CSL 783 REPORT Assignment 1: Image Enhancement using Histogram Processing Jagjeet Singh Dhaliwal (2008CS50212) Kshiteej S. Mahajan (2008CS50214) Introduction In this assignment

More information

A Review on Image Enhancement Technique for Biomedical Images

A Review on Image Enhancement Technique for Biomedical Images A Review on Image Enhancement Technique for Biomedical Images Pankaj V.Gosavi 1, Prof. V. T. Gaikwad 2 M.E (Pursuing) 1, Associate Professor 2 Dept. Information Technology 1, 2 Sipna COET, Amravati, India

More information

AN ADAPTIVE MORPHOLOGICAL FILTER FOR DEFECT DETECTION IN EDDY

AN ADAPTIVE MORPHOLOGICAL FILTER FOR DEFECT DETECTION IN EDDY AN ADAPTIVE MORPHOLOGICAL FILTER FOR DEFECT DETECTION IN EDDY CURRENT AIRCRAFT WHEEL INSPECTION Shu Gao, Lalita Udpa Department of Electrical Engineering and Computer Engineering Iowa State University

More information

Chapter 2: Digital Image Fundamentals. Digital image processing is based on. Mathematical and probabilistic models Human intuition and analysis

Chapter 2: Digital Image Fundamentals. Digital image processing is based on. Mathematical and probabilistic models Human intuition and analysis Chapter 2: Digital Image Fundamentals Digital image processing is based on Mathematical and probabilistic models Human intuition and analysis 2.1 Visual Perception How images are formed in the eye? Eye

More information

2 Human Visual Characteristics

2 Human Visual Characteristics 3rd International Conference on Multimedia Technology(ICMT 2013) Study on new gray transformation of infrared image based on visual property Shaosheng DAI 1, Xingfu LI 2, Zhihui DU 3, Bin ZhANG 4 and Xinlin

More information

Face Detection System on Ada boost Algorithm Using Haar Classifiers

Face Detection System on Ada boost Algorithm Using Haar Classifiers Vol.2, Issue.6, Nov-Dec. 2012 pp-3996-4000 ISSN: 2249-6645 Face Detection System on Ada boost Algorithm Using Haar Classifiers M. Gopi Krishna, A. Srinivasulu, Prof (Dr.) T.K.Basak 1, 2 Department of Electronics

More information

Determining MTF with a Slant Edge Target ABSTRACT AND INTRODUCTION

Determining MTF with a Slant Edge Target ABSTRACT AND INTRODUCTION Determining MTF with a Slant Edge Target Douglas A. Kerr Issue 2 October 13, 2010 ABSTRACT AND INTRODUCTION The modulation transfer function (MTF) of a photographic lens tells us how effectively the lens

More information

Improving Image Quality by Camera Signal Adaptation to Lighting Conditions

Improving Image Quality by Camera Signal Adaptation to Lighting Conditions Improving Image Quality by Camera Signal Adaptation to Lighting Conditions Mihai Negru and Sergiu Nedevschi Technical University of Cluj-Napoca, Computer Science Department Mihai.Negru@cs.utcluj.ro, Sergiu.Nedevschi@cs.utcluj.ro

More information

Prof. Vidya Manian Dept. of Electrical and Comptuer Engineering

Prof. Vidya Manian Dept. of Electrical and Comptuer Engineering Image Processing Intensity Transformations Chapter 3 Prof. Vidya Manian Dept. of Electrical and Comptuer Engineering INEL 5327 ECE, UPRM Intensity Transformations 1 Overview Background Basic intensity

More information

How to capture the best HDR shots.

How to capture the best HDR shots. What is HDR? How to capture the best HDR shots. Processing HDR. Noise reduction. Conversion to monochrome. Enhancing room textures through local area sharpening. Standard shot What is HDR? HDR shot What

More information

Module 6 STILL IMAGE COMPRESSION STANDARDS

Module 6 STILL IMAGE COMPRESSION STANDARDS Module 6 STILL IMAGE COMPRESSION STANDARDS Lesson 16 Still Image Compression Standards: JBIG and JPEG Instructional Objectives At the end of this lesson, the students should be able to: 1. Explain the

More information

Image acquisition. Midterm Review. Digitization, line of image. Digitization, whole image. Geometric transformations. Interpolation 10/26/2016

Image acquisition. Midterm Review. Digitization, line of image. Digitization, whole image. Geometric transformations. Interpolation 10/26/2016 Image acquisition Midterm Review Image Processing CSE 166 Lecture 10 2 Digitization, line of image Digitization, whole image 3 4 Geometric transformations Interpolation CSE 166 Transpose these matrices

More information

Preprocessing on Digital Image using Histogram Equalization: An Experiment Study on MRI Brain Image

Preprocessing on Digital Image using Histogram Equalization: An Experiment Study on MRI Brain Image Preprocessing on Digital Image using Histogram Equalization: An Experiment Study on MRI Brain Image Musthofa Sunaryo 1, Mochammad Hariadi 2 Electrical Engineering, Institut Teknologi Sepuluh November Surabaya,

More information

Transform. Processed original image. Processed transformed image. Inverse transform. Figure 2.1: Schema for transform processing

Transform. Processed original image. Processed transformed image. Inverse transform. Figure 2.1: Schema for transform processing Chapter 2 Point Processing 2.1 Introduction Any image processing operation transforms the grey values of the pixels. However, image processing operations may be divided into into three classes based on

More information