Digital Image Processing Introduction

Transcription

1 Digital Processing Introduction Dr. Hatem Elaydi Electrical Engineering Department Islamic University of Gaza Fall 2015 Sep. 7, 2015

2 Digital Processing manipulation data might experience none-ideal acquisition, transmission or display (e.g., restoration, enhancement and interpolation) data might contain sensitive content (e.g., fight against piracy, counterfeit and forgery) s might need artistic effect (e.g., pointillism) compression data need to be accessed at a different time or location Limited storage space and transmission bandwidth analysis data need to be analyzed automatically in order to reduce the burden of human operators manipulated by a computer to see in A.I. tasks 2

3 Processing bigview The way of thinking From art (heuristics) to science (principles) The key is mathematics (how to utilize) The holistic view is connected (the connectivity) The Google -style re-search Ability to search is a basic part of learning 3

4 D.I.P. Theme Park Acquisition Compression Generation Manipulation Analysis Display Perception DIP is also about connecting dots in image compression, you will see why you need to learn matrix theory and statistics 4

5 Compression Why are images compressible? Redundancy in images (NOT random) How data compression works? Probability theory and statistics Shannon s information theory What about the future of image compression? Lossy or lossless 5

6 Shannon s Picture on Communication source channel encoder channel channel decoder destination super-channel source encoder source decoder The goal of communication is to move information from here to there and from now to then Examples of source: Human speeches, photos, text messages, computer programs Examples of channel: storage media, telephone lines, wireless transmission 6

7 Lossless vs. Lossy Compression Lossless: zero error tolerance No information loss Shannon s entropy formula For photographic images, compression ratio is modest (about 2:1) Lossy: the goal is to preserve the visual quality of images Information loss visually acceptable Shannon s rate-distortion function For photographic images, compression ratio is typically around

8 Popular Lossless Compression Techniques WinZip- Based on the celebrated Lempel-Ziv algorithm invented nearly 30 years ago GIF (Graphic Interchange Format) -Based on an enhanced version of LZ algorithm by Welch in 1983 PNG (Portable Network Graphics) - was introduced by CompuServe in 1987 and made popular until it was not royalty-free in

9 Lossy Compression compressed JPEG file (20,407 bytes) JPEG decoder Q Q low compression ratio high quality high compression ratio low quality decompressed image original raw image (262,144 bytes) 9

10 From JPEG to JPEG2000 discrete cosine transform based JPEG (CR=64) wavelet transform based JPEG2000 (CR=64) 10

11 D.I.P. Theme Park Acquisition Compression Generation Manipulation Analysis Display Perception DIP is also about connecting dots in image manipulation, you will see why you need to learn calculus and Fourier transform 11

12 Manipulation 1 - Noise Removal Noise contamination is often inevitable during the acquisition salt and pepper (impulse) noise additive white Gaussian noise Designing image filters in a principled way 12

13 Manipulation 2 - Deblurring License plate is barely legible due to motion blurring Using of FT and the necessity of regularization 13

14 Manipulation 3 - Contrast Enhancement under-exposed image overly-exposed image Modifying the histogram of an image 14

15 Manipulation 4 - Aliasing Reduction Example: aliasing artifacts in MRI image acquisition Ideal quality, slow scanning nonideal quality, fast scanning Tradeoff between scanning time and image quality (image reconstruction) 15

16 Manipulation 5 - Interpolation digital zooming small 1M pixels large 4M pixels Resolution enhancement can be obtained by common image processing software such as Photoshop or Paint Shop Pro Differentiating between digital and optical zooming 16

17 Manipulation 6- Mosaicing Merge multiple images of the same scene into one with larger FOV + = There exist several mosaicing software for automatic stitching F.Y.I.: search Gigapixel images by Google 17

18 Manipulation 7- Error Concealment blocks contaminated by channel errors 18

19 Manipulation 8 - Inpainting Inpainting is the process of reconstructing lost or deteriorated parts of images and videos 19

20 Inpainting Application: Restore Old Photos 20

21 Manipulation 9 - Color Quantization 25,680 colors (24 bits) 256 colors (8 bits) Applications: video cell-phone, gameboy, portable DVD 21

22 Manipulation 10 - Halftoning grayscale: halftoned: 0/255 Halftones are created through a process called dithering, in which the density and pattern of black and white dots are varied to simulate different shades of gray. 22

23 Manipulation 11 - Watermarking Original image Modified image 23

24 Manipulation 12 - Stylization Stylization allows easy creation of stylized (i.e. artistic looking) computer images 24

25 D.I.P. Theme Park Acquisition Compression Generation Manipulation Analysis Display Perception DIP is also about connecting dots in image analysis, you will see why you need to know about neuroscience and psychology 25

26 Analysis 1 - Edge Detection Basic edge detectors based on derivatives 26

27 Analysis 2 - Face Detection Deceivingly simple for humans but notoriously difficult for machines 27

28 Analysis 3 - Change Detection 28

29 Change Detection in Medical Application 29

30 Analysis 4 - Matching Antemortem dental X-ray record Postmortem dental X-ray record 30

31 Matching in Biometrics Two deceivingly similar fingerprints of two different people 31

32 Analysis 5 - Segmentation 32

33 Analysis 6 - Object Recognition License number can be automatically extracted from the image of license plate 33

34 Analysis 7 - Content-based Retrieval retrieved building images 34

35 Looking forward to your cooperation Good luck 35