CoE4TN4 Image Processing. Chapter 4 Filtering in the Frequency Domain
|
|
- Basil Jacobs
- 5 years ago
- Views:
Transcription
1 CoE4TN4 Image Processing Chapter 4 Filtering in the Frequency Domain
2 Fourier Transform Sections 4.1 to 4.5 will be done on the board 2
3 2D Fourier Transform 3
4 2D Sampling and Aliasing 4
5 2D Sampling and Aliasing Perfect reconstruction of a bank-limited image from a set of its samples requires 2-D convolution in the spatial domain with a sinc function. To reduce aliasing it is a good idea to blur an image before shrinking or sampling 5
6 2D Sampling and Aliasing 6
7 2D Sampling and Aliasing In images with strong edge content, the effects of aliasing are called jaggies 7
8 2D Sampling and Aliasing Moire patterns happens in sampling scenes with periodic or nearly perodic components Scanning of media prints such as newspaper 8
9 2D Sampling and Aliasing Newspapers and other printed materials use halftone dots Halftone: black dots or ellipses whose sizes are used to simulate gray tones 9
10 2D Sampling and Aliasing 10
11 2D DFT 11
12 2D DFT 12
13 Fourier Transform Fourier Transform (FT) of a 2-D signal: 13
14 Fourier Transform Properties of Fourier Transform (FT) of a 2-D real, signal: 14
15 Fourier Transform It is common to multiply input image by (-1) x+y prior to computing the Fourier Transform. This shift the center of the FT to (M/2,N/2) 15
16 Fourier Transform 16
17 2D DFT 17
18 2D DFT 18
19 Discrete Fourier transform When working with discrete Fourier transform, we have to keep in mind the periodicity of functions involved. This periodicity is a byproduct of the way in which discrete FT (DFT) is defined Using DFT allows us to perform convolution in the frequency domain but the functions are treated as periodic. 19
20 2D DFT 20
21 2D DFT 21
22 2D DFT 22
23 Frequency domain filtering 1. Multiply the input image by (-1) x+y to center the transform Compute the DFT of the image from step 1 2. Multiply the result by the transfer function of the filter (centered) 3. Take the inverse transform. 4. Multiply the result by (-1) x+y 23
24
25 H(u,v)is 0 at the center of the transform and 1 elsewhere 25
26 Low frequencies: slowly varying components in an image High frequencies: caused by sharp transitions in intensity such as edges and noise
27 Steps for filtering in frequency domain For input image f(x,y) of size MxN obtain the padding parameters P and Q Form a padded image fp(x,y) of size PxQ by appending the necessary number of zeros to f(x,y) Multiply fp(x,y) by (-1) x+y to center the Fourier transform Compute DFT of f, F(u,v) Generate a real, symmetric filter H(u,v) of size PxQ. Form product G(u,v)=H(u,v)F(u,v) Obtain the processed image: g p (x, y) ={real(f 1 (G(u, v)))}( Obtain the final processed result by extracting the MxN region from the top left quadrant of gp(x,y) 1) x+y 27
28 G(u,v)=F(u,v)H(u,v) Frequency domain filtering Based on convolution theorem: g(x,y)=h(x,y)*f(x,y) f(x,y) is the input image g(x,y) is the processed image h(x,y): impulse response or point spread function Based on the form of H(u,v), the output image exhibits some features of f(x,y) 28
29 Convolution h(x,y) f(x,y) Frequency domain filtering Implementing a mask w(x,y) f(x,y) 1) Flip 2) Shift, multiply, add 29
30 Frequency domain filtering Convolution with a filter and implementing a mask are very similar The only difference is the flipping operation If the impulse response of the filter is symmetric about the origin the two operations are the same. Instead of filtering in the frequency domain, we can approximate the impulse response of the filter by a mask, and use the mask in the spatial domain. 30
31 31
32 Frequency domain filtering Edges and sharp transitions (e.g., noise) in the gray levels of an image contribute significantly to high-frequency content of FT. Low frequency in the Fourier transform of an image are responsible for the general gray-level appearance of the image over smooth areas. Blurring (smoothing) is achieved by attenuating range of high frequency components of FT. We consider 3 types of lowpass filters: ideal, Butterworth and Gaussian 32
33 Ideal low-pass filter Ideal: all the frequencies inside a circle of radius D 0 are passed and all the frequencies outside this circle are completely removed. H H u u v M/2-D 0 M/2+D 0 33
34 34
35 35
36 Ideal low-pass filter Effects of ideal low-pass filtering: blurring and ringing 36
37 Ideal low-pass filter The radii of the concentric rings in h(x,y) are proportional to 1/D 0 D0 1/D0 Less blurring, Less ringing (amplitude of rings drop) D0 1/D0 More blurring, More ringing (amplitude of rings increase) 37
38 Butterworth Lowpass Filters Smooth transfer function, no sharp discontinuity, no clear cutoff frequency. 38
39 Butterworth Lowpass Filters 39
40 Butterworth Lowpass Filters 40
41 Gaussian Lowpass Filters Smooth transfer function, smooth impulse response, no ringing 41
42 Gaussian Lowpass Filters 42
43 Applications of Lowpass Filters 43
44 Applications of Lowpass Filters 44
45 High-pass filtering Image sharpening can be achieved by a high-pass filtering process. H hp (u,v)=1-h lp (u,v) Ideal: Butterworth: Gaussian: 45
46 High-pass filtering 46
47 High-pass filtering 47
48 High-pass filtering 48
49 High-pass filtering 49
50 High-pass filtering 50
51 Laplacian in Frequency Domain 51
52 Laplacian in Frequency Domain 52
53 53
54 Unsharp Masking, High-boost Filtering Unsharp masking: f hp (x,y)=f(x,y)-f lp (x,y) H hp (u,v)=1-h lp (u,v) High-boost filtering: f hb (x,y)=af(x,y)-f lp (x,y) f hb (x,y)=(a-1)f(x,y)+f hp (x,y) H hb (u,v)=(a-1)+h hp (u,v) 54
55 Homomorphic filtering In some images, the quality of the image has reduced because of non-uniform illumination Homomorphic filtering can be used to perform illumination correction We can view an image f(x,y) as a product of two components: This equation cannot be used directly in order to operate separately on the frequency components of illumination and reflectance 55
56 Homomorphic filtering 56
57 Homomorphic filtering The key to the approach is that separation of the illumination and reflectance components is achieved. The homomorphic filter can then operate on these components separately Illumination component of an image generally has slow variations, while the reflectance component vary abruptly By removing the low frequencies (highpass filtering) the effects of illumination can be removed 57
58 Homomorphic filtering 58
59 Selective Filtering Bandreject filters remove or attenuate a band of frequencies 59
60 Selective Filtering 60
61 Selective Filtering 61
62 A bandpass filter performs the opposite of a bandreject filter. H bp (u,v)=1-h br (u,v) Bandpass and band reject filters A notch filter rejects (or passes) frequencies in predefined neighborhood about a center frequency Due to symmetry of the FT, notch filters must appear in symmetric pairs about the origin in order to obtain meaningful results 62
63 Periodic noise reduction 63
64 Periodic noise reduction 64
65 65
TDI2131 Digital Image Processing
TDI131 Digital Image Processing Frequency Domain Filtering Lecture 6 John See Faculty of Information Technology Multimedia University Some portions of content adapted from Zhu Liu, AT&T Labs. Most figures
More information1.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 informationFourier Transforms and the Frequency Domain
Fourier Transforms and the Frequency Domain Lecture 11 Magnus Gedda magnus.gedda@cb.uu.se Centre for Image Analysis Uppsala University Computer Assisted Image Analysis 04/27/2006 Gedda (Uppsala University)
More informationDigital Image Processing
Digital Image Processing Filtering in the Frequency Domain (Application) Christophoros Nikou cnikou@cs.uoi.gr University of Ioannina - Department of Computer Science and Engineering 2 Periodicity of the
More information8. Lecture. Image restoration: Fourier domain
8. Lecture Image restoration: Fourier domain 1 Structured noise 2 Motion blur 3 Filtering in the Fourier domain ² Spatial ltering (average, Gaussian,..) can be done in the Fourier domain (convolution theorem)
More informationFrequency Domain Enhancement
Tutorial Report Frequency Domain Enhancement Page 1 of 21 Frequency Domain Enhancement ESE 558 - DIGITAL IMAGE PROCESSING Tutorial Report Instructor: Murali Subbarao Written by: Tutorial Report Frequency
More informationDigital Image Processing. Filtering in the Frequency Domain (Application)
Digital Image Processing Filtering in the Frequency Domain (Application) Christophoros Nikou cnikou@cs.uoi.gr University of Ioannina - Department of Computer Science 2 Periodicity of the DFT The range
More informationLecture #10. EECS490: Digital Image Processing
Lecture #10 Wraparound and padding Image Correlation Image Processing in the frequency domain A simple frequency domain filter Frequency domain filters High-pass, low-pass Apodization Zero-phase filtering
More informationCoE4TN4 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 informationMidterm Review. Image Processing CSE 166 Lecture 10
Midterm Review Image Processing CSE 166 Lecture 10 Topics covered Image acquisition, geometric transformations, and image interpolation Intensity transformations Spatial filtering Fourier transform and
More informationDigital Image Processing. Image Enhancement: Filtering in the Frequency Domain
Digital Image Processing Image Enhancement: Filtering in the Frequency Domain 2 Contents In this lecture we will look at image enhancement in the frequency domain Jean Baptiste Joseph Fourier The Fourier
More informationFiltering in the spatial domain (Spatial Filtering)
Filtering in the spatial domain (Spatial Filtering) refers to image operators that change the gray value at any pixel (x,y) depending on the pixel values in a square neighborhood centered at (x,y) using
More informationTransforms and Frequency Filtering
Transforms and Frequency Filtering Khalid Niazi Centre for Image Analysis Swedish University of Agricultural Sciences Uppsala University 2 Reading Instructions Chapter 4: Image Enhancement in the Frequency
More informationImage Enhancement. DD2423 Image Analysis and Computer Vision. Computational Vision and Active Perception School of Computer Science and Communication
Image Enhancement DD2423 Image Analysis and Computer Vision Mårten Björkman Computational Vision and Active Perception School of Computer Science and Communication November 15, 2013 Mårten Björkman (CVAP)
More informationFourier Transform. Any signal can be expressed as a linear combination of a bunch of sine gratings of different frequency Amplitude Phase
Fourier Transform Fourier Transform Any signal can be expressed as a linear combination of a bunch of sine gratings of different frequency Amplitude Phase 2 1 3 3 3 1 sin 3 3 1 3 sin 3 1 sin 5 5 1 3 sin
More informationDIGITAL IMAGE PROCESSING UNIT III
DIGITAL IMAGE PROCESSING UNIT III 3.1 Image Enhancement in Frequency Domain: Frequency refers to the rate of repetition of some periodic events. In image processing, spatial frequency refers to the variation
More informationImage 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 informationImage Enhancement using Histogram Equalization and Spatial Filtering
Image Enhancement using Histogram Equalization and Spatial Filtering Fari Muhammad Abubakar 1 1 Department of Electronics Engineering Tianjin University of Technology and Education (TUTE) Tianjin, P.R.
More informationSmoothing frequency domain filters
Smoothing frequency domain filters Ideal Lowpass Filter (ILPF) ILPF is the simplest lowpass filter that cuts off all high frequency components of the DFT that are at a distance greater than a specified
More informationPractical Image and Video Processing Using MATLAB
Practical Image and Video Processing Using MATLAB Chapter 10 Neighborhood processing What will we learn? What is neighborhood processing and how does it differ from point processing? What is convolution
More informationTable of contents. Vision industrielle 2002/2003. Local and semi-local smoothing. Linear noise filtering: example. Convolution: introduction
Table of contents Vision industrielle 2002/2003 Session - Image Processing Département Génie Productique INSA de Lyon Christian Wolf wolf@rfv.insa-lyon.fr Introduction Motivation, human vision, history,
More informationLAB MANUAL SUBJECT: IMAGE PROCESSING BE (COMPUTER) SEM VII
LAB MANUAL SUBJECT: IMAGE PROCESSING BE (COMPUTER) SEM VII IMAGE PROCESSING INDEX CLASS: B.E(COMPUTER) SR. NO SEMESTER:VII TITLE OF THE EXPERIMENT. 1 Point processing in spatial domain a. Negation of an
More informationIMAGE 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 informationSmoothing frequency domain filters
Smoothing frequency domain filters Ideal Lowpass Filter (ILPF) ILPF is the simplest lowpass filter that cuts off all high frequency components of the DFT that are at a distance greater than a specified
More informationDigital 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 informationA.V.C. COLLEGE OF ENGINEERING DEPARTEMENT OF CSE CP7004- IMAGE PROCESSING AND ANALYSIS UNIT 1- QUESTION BANK
A.V.C. COLLEGE OF ENGINEERING DEPARTEMENT OF CSE CP7004- IMAGE PROCESSING AND ANALYSIS UNIT 1- QUESTION BANK STAFF NAME: TAMILSELVAN K UNIT I SPATIAL DOMAIN PROCESSING Introduction to image processing
More informationDigital Image Processing. Frequency Domain Filtering
Digital Image Processing Frequency Domain Filtering DFT Matlab demo clear all; close all; a=imread('testpat1.png');b=imdouble(a); figure;imshow(b); Fb = fft(b);fbshift=fftshift(fb); figure;imshow(log(abs(fbshift)+0.00000001),[]);
More informationFiltering. 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 informationקורס גרפיקה ממוחשבת 2008 סמסטר ב' Image Processing 1 חלק מהשקפים מעובדים משקפים של פרדו דוראנד, טומס פנקהאוסר ודניאל כהן-אור
קורס גרפיקה ממוחשבת 2008 סמסטר ב' Image Processing 1 חלק מהשקפים מעובדים משקפים של פרדו דוראנד, טומס פנקהאוסר ודניאל כהן-אור What is an image? An image is a discrete array of samples representing a continuous
More informationSubband coring for image noise reduction. Edward H. Adelson Internal Report, RCA David Sarnoff Research Center, Nov
Subband coring for image noise reduction. dward H. Adelson Internal Report, RCA David Sarnoff Research Center, Nov. 26 1986. Let an image consisting of the array of pixels, (x,y), be denoted (the boldface
More informationImage Filtering. Median Filtering
Image Filtering Image filtering is used to: Remove noise Sharpen contrast Highlight contours Detect edges Other uses? Image filters can be classified as linear or nonlinear. Linear filters are also know
More informationImage Enhancement. Image Enhancement
SPATIAL FILTERING g h * h g FREQUENCY DOMAIN FILTERING G H. F F H G Copright RMR / RDL - 999. PEE53 - Processamento Digital de Imagens LOW PASS FILTERING attenuate or eliminate high-requenc components
More informationImage Processing. What is an image? קורס גרפיקה ממוחשבת 2008 סמסטר ב' Converting to digital form. Sampling and Reconstruction.
Amplitude 5/1/008 What is an image? An image is a discrete array of samples representing a continuous D function קורס גרפיקה ממוחשבת 008 סמסטר ב' Continuous function Discrete samples 1 חלק מהשקפים מעובדים
More informationImage 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 informationFilters. Materials from Prof. Klaus Mueller
Filters Materials from Prof. Klaus Mueller Think More about Pixels What exactly a pixel is in an image or on the screen? Solid square? This cannot be implemented A dot? Yes, but size matters Pixel Dots
More informationIMAGE PROCESSING (RRY025) THE CONTINUOUS 2D FOURIER TRANSFORM
IMAGE PROCESSING (RRY5) THE CONTINUOUS D FOURIER TRANSFORM INTRODUCTION A vital tool in image processing. Also a prototype of other image transforms, cosine, Wavelet etc. Applications Image Filtering -
More informationImage Smoothening and Sharpening using Frequency Domain Filtering Technique
Volume 5, Issue 4, April (17) Image Smoothening and Sharpening using Frequency Domain Filtering Technique Swati Dewangan M.Tech. Scholar, Computer Networks, Bhilai Institute of Technology, Durg, India.
More informationProf. 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 informationAutomatic processing to restore data of MODIS band 6
Automatic processing to restore data of MODIS band 6 --Final Project for ECE 533 Abstract An automatic processing to restore data of MODIS band 6 is introduced. For each granule of MODIS data, 6% of the
More informationECE 484 Digital Image Processing Lec 10 - Image Restoration I
ECE 484 Digital Image Processing Lec 10 - Image Restoration I Zhu Li Dept of CSEE, UMKC Office: FH560E, Email: lizhu@umkc.edu, Ph: x 2346. http://l.web.umkc.edu/lizhu slides created with WPS Office Linux
More informationIMAGE 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 informationDigital Image Processing 3/e
Laboratory Projects for Digital Image Processing 3/e by Gonzalez and Woods 2008 Prentice Hall Upper Saddle River, NJ 07458 USA www.imageprocessingplace.com The following sample laboratory projects are
More informationDIGITAL 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(i) Understanding of the characteristics of linear-phase finite impulse response (FIR) filters
FIR Filter Design Chapter Intended Learning Outcomes: (i) Understanding of the characteristics of linear-phase finite impulse response (FIR) filters (ii) Ability to design linear-phase FIR filters according
More informationThe Fourier Transform
The Fourier Transform Introduction to Digital Signal Processing (886457) 6 1 / 56 Contents Introduction Fourier Transforms One-dimensional DFT Two-dimensional DFT Fourier Transforms Function in Octave
More information2D Discrete Fourier Transform
2D Discrete Fourier Transform In these lecture notes the figures have been removed for copyright reasons. References to figures are given instead, please check the figures yourself as given in the course
More informationImage Enhancement in spatial domain. Digital Image Processing GW Chapter 3 from Section (pag 110) Part 2: Filtering in spatial domain
Image Enhancement in spatial domain Digital Image Processing GW Chapter 3 from Section 3.4.1 (pag 110) Part 2: Filtering in spatial domain Mask mode radiography Image subtraction in medical imaging 2 Range
More informationDigital Imaging Systems for Historical Documents
Digital Imaging Systems for Historical Documents Improvement Legibility by Frequency Filters Kimiyoshi Miyata* and Hiroshi Kurushima** * Department Museum Science, ** Department History National Museum
More information(i) Understanding of the characteristics of linear-phase finite impulse response (FIR) filters
FIR Filter Design Chapter Intended Learning Outcomes: (i) Understanding of the characteristics of linear-phase finite impulse response (FIR) filters (ii) Ability to design linear-phase FIR filters according
More informationComputer Vision, Lecture 3
Computer Vision, Lecture 3 Professor Hager http://www.cs.jhu.edu/~hager /4/200 CS 46, Copyright G.D. Hager Outline for Today Image noise Filtering by Convolution Properties of Convolution /4/200 CS 46,
More informationDIGITAL IMAGE DE-NOISING FILTERS A COMPREHENSIVE STUDY
INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS ISSN 2320-7345 DIGITAL IMAGE DE-NOISING FILTERS A COMPREHENSIVE STUDY Jaskaranjit Kaur 1, Ranjeet Kaur 2 1 M.Tech (CSE) Student,
More informationBiosignal filtering and artifact rejection. Biosignal processing I, S Autumn 2017
Biosignal filtering and artifact rejection Biosignal processing I, 52273S Autumn 207 Motivation ) Artifact removal power line non-stationarity due to baseline variation muscle or eye movement artifacts
More informationExamples of image processing
Examples of image processing Example 1: We would like to automatically detect and count rings in the image 3 Detection by correlation Correlation = degree of similarity Correlation between f(x, y) and
More informationDigital Image Processing. Lecture 5 (Enhancement) Bu-Ali Sina University Computer Engineering Dep. Fall 2009
Digital Image Processing Lecture 5 (Enhancement) Bu-Ali Sina University Computer Engineering Dep. Fall 2009 Outline Image Enhancement in Spatial Domain Histogram based methods Histogram Equalization Local
More informationEE482: Digital Signal Processing Applications
Professor Brendan Morris, SEB 3216, brendan.morris@unlv.edu EE482: Digital Signal Processing Applications Spring 2014 TTh 14:30-15:45 CBC C222 Lecture 15 Image Processing 14/04/15 http://www.ee.unlv.edu/~b1morris/ee482/
More informationSyllabus of the course Methods for Image Processing a.y. 2016/17
Syllabus of the course Methods for Image Processing a.y. 2016/17 January 14, 2017 This document reports a description of the topics covered in the course Methods for Image processing for the academic year
More informationJune 30 th, 2008 Lesson notes taken from professor Hongmei Zhu class.
P. 1 June 30 th, 008 Lesson notes taken from professor Hongmei Zhu class. Sharpening Spatial Filters. 4.1 Introduction Smoothing or blurring is accomplished in the spatial domain by pixel averaging in
More informationEnhancement. Degradation model H and noise must be known/predicted first before restoration. Noise model Degradation Model
Kuliah ke 5 Program S1 Reguler DTE FTUI 2009 Model Filter Noise model Degradation Model Spatial Domain Frequency Domain MATLAB & Video Restoration Examples Video 2 Enhancement Goal: to improve an image
More informationImage Restoration. Lecture 7, March 23 rd, Lexing Xie. EE4830 Digital Image Processing
Image Restoration Lecture 7, March 23 rd, 2009 Lexing Xie EE4830 Digital Image Processing http://www.ee.columbia.edu/~xlx/ee4830/ thanks to G&W website, Min Wu and others for slide materials 1 Announcements
More information8.2 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 informationInvestigation of Optimal Denoising Filter for MRI Images
International Journal of Applied Engineering Research ISSN 0973-456 Volume 13, Number 15 (018) pp. 164-171 Investigation of Optimal Denoising Filter for MRI Images Ch. Rajasekhara Rao, M N V S S Kumar,
More informationLast Lecture. Lecture 2, Point Processing GW , & , Ida-Maria Which image is wich channel?
Last Lecture Lecture 2, Point Processing GW 2.6-2.6.4, & 3.1-3.4, Ida-Maria Ida.sintorn@it.uu.se Digitization -sampling in space (x,y) -sampling in amplitude (intensity) How often should you sample in
More informationLast Lecture. photomatix.com
Last Lecture photomatix.com HDR Video Assorted pixel (Single Exposure HDR) Assorted pixel Assorted pixel Pixel with Adaptive Exposure Control light attenuator element detector element T t+1 I t controller
More informationAliasing and Antialiasing. What is Aliasing? What is Aliasing? What is Aliasing?
What is Aliasing? Errors and Artifacts arising during rendering, due to the conversion from a continuously defined illumination field to a discrete raster grid of pixels 1 2 What is Aliasing? What is Aliasing?
More informationChrominance Assisted Sharpening of Images
Blekinge Institute of Technology Research Report 2004:08 Chrominance Assisted Sharpening of Images Andreas Nilsson Department of Signal Processing School of Engineering Blekinge Institute of Technology
More informationUnderstanding Digital Signal Processing
Understanding Digital Signal Processing Richard G. Lyons PRENTICE HALL PTR PRENTICE HALL Professional Technical Reference Upper Saddle River, New Jersey 07458 www.photr,com Contents Preface xi 1 DISCRETE
More informationModule 3: Video Sampling Lecture 18: Filtering operations in Camera and display devices. The Lecture Contains: Effect of Temporal Aperture:
The Lecture Contains: Effect of Temporal Aperture: Spatial Aperture: Effect of Display Aperture: file:///d /...e%20(ganesh%20rana)/my%20course_ganesh%20rana/prof.%20sumana%20gupta/final%20dvsp/lecture18/18_1.htm[12/30/2015
More informationImage Filtering. Reading Today s Lecture. Reading for Next Time. What would be the result? Some Questions from Last Lecture
Image Filtering HCI/ComS 575X: Computational Perception Instructor: Alexander Stoytchev http://www.cs.iastate.edu/~alex/classes/2007_spring_575x/ January 24, 2007 HCI/ComS 575X: Computational Perception
More informationImage Processing. Image Processing. What is an Image? Image Resolution. Overview. Sources of Error. Filtering Blur Detect edges
Thomas Funkhouser Princeton University COS 46, Spring 004 Quantization Random dither Ordered dither Floyd-Steinberg dither Pixel operations Add random noise Add luminance Add contrast Add saturation ing
More information!"!#"#$% Lecture 2: Media Creation. Some materials taken from Prof. Yao Wang s slides RECAP
Lecture 2: Media Creation Some materials taken from Prof. Yao Wang s slides RECAP #% A Big Umbrella Content Creation: produce the media, compress it to a format that is portable/ deliverable Distribution:
More informationNoise and Restoration of Images
Noise and Restoration of Images Dr. Praveen Sankaran Department of ECE NIT Calicut February 24, 2013 Winter 2013 February 24, 2013 1 / 35 Outline 1 Noise Models 2 Restoration from Noise Degradation 3 Estimation
More informationDigital 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 informationFourier analysis of images
Fourier analysis of images Intensity Image Fourier Image Slides: James Hays, Hoiem, Efros, and others http://sharp.bu.edu/~slehar/fourier/fourier.html#filtering Signals can be composed + = http://sharp.bu.edu/~slehar/fourier/fourier.html#filtering
More informationAnnouncements. Image Processing. What s an image? Images as functions. Image processing. What s a digital image?
Image Processing Images by Pawan Sinha Today s readings Forsyth & Ponce, chapters 8.-8. http://www.cs.washington.edu/education/courses/49cv/wi/readings/book-7-revised-a-indx.pdf For Monday Watt,.3-.4 (handout)
More informationAchim J. Lilienthal Mobile Robotics and Olfaction Lab, AASS, Örebro University
Achim J. Lilienthal Mobile Robotics and Olfaction Lab, Room T29, Mo, -2 o'clock AASS, Örebro University (please drop me an email in advance) achim.lilienthal@oru.se 4.!!!!!!!!! Pre-Class Reading!!!!!!!!!
More informationExperiment 4- Finite Impulse Response Filters
Experiment 4- Finite Impulse Response Filters 18 February 2009 Abstract In this experiment we design different Finite Impulse Response filters and study their characteristics. 1 Introduction The transfer
More informationLecture No Image Filtering (course: Computer Vision)
Lecture No. 34-35 Image Filtering (course: Computer Vision) e- mail: naeemmahoto@gmail.com Department of So9ware Engineering, Mehran UET Jamshoro, Sind, Pakistan Enhancement using Arithme0c/ Logic Opera0ons
More informationDigital Image Processing
Digital Image Processing D. Sundararajan Digital Image Processing A Signal Processing and Algorithmic Approach 123 D. Sundararajan Formerly at Concordia University Montreal Canada Additional material to
More informationPRACTICAL 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 informationIIR Filter Design Chapter Intended Learning Outcomes: (i) Ability to design analog Butterworth filters
IIR Filter Design Chapter Intended Learning Outcomes: (i) Ability to design analog Butterworth filters (ii) Ability to design lowpass IIR filters according to predefined specifications based on analog
More informationPart I Feature Extraction (1) Image Enhancement. CSc I6716 Spring Local, meaningful, detectable parts of the image.
CSc I6716 Spring 211 Introduction Part I Feature Extraction (1) Zhigang Zhu, City College of New York zhu@cs.ccny.cuny.edu Image Enhancement What are Image Features? Local, meaningful, detectable parts
More informationImage Restoration. Lecture 7, March 23 rd, Lexing Xie. EE4830 Digital Image Processing
Image Restoration Lecture 7, March 23 rd, 2008 Lexing Xie EE4830 Digital Image Processing http://www.ee.columbia.edu/~xlx/ee4830/ thanks to G&W website, Min Wu and others for slide materials 1 Announcements
More informationFinal Exam Solutions June 14, 2006
Name or 6-Digit Code: PSU Student ID Number: Final Exam Solutions June 14, 2006 ECE 223: Signals & Systems II Dr. McNames Keep your exam flat during the entire exam. If you have to leave the exam temporarily,
More informationLast Lecture. photomatix.com
Last Lecture photomatix.com Today Image Processing: from basic concepts to latest techniques Filtering Edge detection Re-sampling and aliasing Image Pyramids (Gaussian and Laplacian) Removing handshake
More informationModule 3 : Sampling and Reconstruction Problem Set 3
Module 3 : Sampling and Reconstruction Problem Set 3 Problem 1 Shown in figure below is a system in which the sampling signal is an impulse train with alternating sign. The sampling signal p(t), the Fourier
More informationMatlab (see Homework 1: Intro to Matlab) Linear Filters (Reading: 7.1, ) Correlation. Convolution. Linear Filtering (warm-up slide) R ij
Matlab (see Homework : Intro to Matlab) Starting Matlab from Unix: matlab & OR matlab nodisplay Image representations in Matlab: Unsigned 8bit values (when first read) Values in range [, 255], = black,
More information4. Design of Discrete-Time Filters
4. Design of Discrete-Time Filters 4.1. Introduction (7.0) 4.2. Frame of Design of IIR Filters (7.1) 4.3. Design of IIR Filters by Impulse Invariance (7.1) 4.4. Design of IIR Filters by Bilinear Transformation
More informationComputer Graphics (Fall 2011) Outline. CS 184 Guest Lecture: Sampling and Reconstruction Ravi Ramamoorthi
Computer Graphics (Fall 2011) CS 184 Guest Lecture: Sampling and Reconstruction Ravi Ramamoorthi Some slides courtesy Thomas Funkhouser and Pat Hanrahan Adapted version of CS 283 lecture http://inst.eecs.berkeley.edu/~cs283/fa10
More informationEEL 6562 Image Processing and Computer Vision Box Filter and Laplacian Filter Implementation
DEPARTMENT OF ELECTRICAL & COMPUTER ENGINEERING EEL 6562 Image Processing and Computer Vision Box Filter and Laplacian Filter Implementation Rajesh Pydipati Introduction Image Processing is defined as
More informationIntroduction Approach Work Performed and Results
Algorithm for Morphological Cancer Detection Carmalyn Lubawy Melissa Skala ECE 533 Fall 2004 Project Introduction Over half of all human cancers occur in stratified squamous epithelia. Approximately one
More informationECE 2111 Signals and Systems Spring 2012, UMD Experiment 9: Sampling
ECE 2111 Signals and Systems Spring 2012, UMD Experiment 9: Sampling Objective: In this experiment the properties and limitations of the sampling theorem are investigated. A specific sampling circuit will
More informationECE 484 Digital Image Processing Lec 09 - Image Resampling
ECE 484 Digital Image Processing Lec 09 - Image Resampling Zhu Li Dept of CSEE, UMKC Office: FH560E, Email: lizhu@umkc.edu, Ph: x 2346. http://l.web.umkc.edu/lizhu slides created with WPS Office Linux
More informationFinal Exam Solutions June 7, 2004
Name: Final Exam Solutions June 7, 24 ECE 223: Signals & Systems II Dr. McNames Write your name above. Keep your exam flat during the entire exam period. If you have to leave the exam temporarily, close
More informationChapter 2 Image Enhancement in the Spatial Domain
Chapter 2 Image Enhancement in the Spatial Domain Abstract Although the transform domain processing is essential, as the images naturally occur in the spatial domain, image enhancement in the spatial domain
More informationImages and Filters. EE/CSE 576 Linda Shapiro
Images and Filters EE/CSE 576 Linda Shapiro What is an image? 2 3 . We sample the image to get a discrete set of pixels with quantized values. 2. For a gray tone image there is one band F(r,c), with values
More informationINTRODUCTION TO COMPUTER MUSIC SAMPLING SYNTHESIS AND FILTERS. Professor of Computer Science, Art, and Music
INTRODUCTION TO COMPUTER MUSIC SAMPLING SYNTHESIS AND FILTERS Roger B. Dannenberg Professor of Computer Science, Art, and Music Copyright 2002-2013 by Roger B. Dannenberg 1 SAMPLING SYNTHESIS Synthesis
More informationChapter 3 Image Enhancement in the Spatial Domain. Chapter 3 Image Enhancement in the Spatial Domain
It makes all the difference whether one sees darkness through the light or brightness through the shadows. - David Lindsay 3.1 Background 76 3.2 Some Basic Gray Level Transformations 78 3.3 Histogram Processing
More informationDigital Processing of Continuous-Time Signals
Chapter 4 Digital Processing of Continuous-Time Signals 清大電機系林嘉文 cwlin@ee.nthu.edu.tw 03-5731152 Original PowerPoint slides prepared by S. K. Mitra 4-1-1 Digital Processing of Continuous-Time Signals Digital
More informationDigital Image Processing
Digital Image Processing Dr. T.R. Ganesh Babu Professor, Department of Electronics and Communication Engineering, Muthayammal Engineering College, Rasipuram, Namakkal Dist. S. Leo Pauline Assistant Professor,
More informationIntroduction. Computer Vision. CSc I6716 Fall Part I. Image Enhancement. Zhigang Zhu, City College of New York
CSc I6716 Fall 21 Introduction Part I Feature Extraction ti (1) Zhigang Zhu, City College of New York zhu@cs.ccny.cuny.edu Image Enhancement What are Image Features? Local, meaningful, detectable parts
More information