1. 1 Lecture 1 1 Image Processing Eng. Ahmed H. Abo absa E-mail: a.absa@up.edu.psa.absa@up.edu.ps

2. 2 Lecture 1 What is Digital Image Processing l Processing digital images by means of a digital computer. l A digital image can be modeled as a two dimensional function,,where x and y are spatial coordinates, and the value of the function is the intensity or gray level of the image at that point.

3. 3 Lecture 1 What is Digital Image Processing l A digital image is composed of a finite number of elements, each of which has a particular location and value. These elements are referred to as picture elements, pixels, and pels.

4. 4 Lecture 1 Digital Image Processing l Image Enhancement l Image Restoration l Image Understanding (or Computer Vision) l Image Coding (or Image Data Compression)

5. 5 Lecture 1 Image Enhancement l Goal u to accentuate certain image features for subsequent analysis or for image display Input : imageOutput : image

6. 6 Lecture 1 Image Enhancement l Techniques u Contrast enhancement u histogram equalization u pseudo coloring u noise filtering u edge sharpening u smoothing l Applications u processing of remote-sensed image via satellite u radar, SAR, Ultrasonic image processing

7. 7 Lecture 1 Image Restoration l Goal u to remove or minimize known/unknown degradations in image Input : imageOutput : image

8. 8 Lecture 1 Image Restoration l Techniques u De-blurring u noise filtering u correction of geometric distortion u inverse filtering u Least mean square(Wiener) filtering l Applications u remote-sensed image processing u noise cancellation

9. 9 Lecture 1 Image Understanding l Goal u to interpret or describe the meaning contained in the image Input : imageOutput : interpretation(description) “ME” “circle”

10. 10 Lecture 1 Image Understanding l Techniques u boundary descriptor u regional descriptor u relational descriptor l Applications u character recognition u automatic inspection of industrial parts u ATR(automatic target recognition) u target tracking

11. 11 Lecture 1 Image Data Compression l Goal u to reduce the amount of data required to represent images Input : imageOutput : bit-stream data “010100101100110101001....”

12. 12 Lecture 1 Image Data Compression l Techniques u Error-free coding( or lossless coding) u Lossy compression u Image Compression Standard vJPEG, H.261, H.263, MPEG-1,2,4 etc l Applications u Transmission vteleconferencing,TV system, remote sensing via satellite u Storage vVOD(video on demand), Video CD, DVD(digital video disk), medical imaging, educational and business documents

13. 13 Lecture 1 The whole electromagnetic spectrum is used by “imagers” Imaging

14. 14 Lecture 1 From the gigantic … Scales of Imaging

15. 15 Lecture 1 … to the everyday … Scales of Imaging

16. 16 Lecture 1 … to the tiny. Scales of Imaging

17. 17 Lecture 1 Digital Image Formation

18. 18 Lecture 1 Matrix Representation H=256 W=256 Divide into 8x8 blocks

19. 19 Lecture 1 Image Resolution

20. 20 Lecture 1 Image Resolution

21. 21 Lecture 1 l Images and videos are multi-dimensional (≥ 2 dimensions) signals. Dimensionality of Digital Images

22. 22 Lecture 1 The Human Visual System (HVS)

23. 23 Lecture 1 HVS: Foveated Vision Foveated vision: non-uniform resolution of the visual field, highest at the point of fixation and decreasing rapidly

24. 24 Lecture 1 HVS: Visual Illusion

25. 25 Lecture 1 Find the black dot HVS: Visual Illusion

26. 26 Lecture 1 What is this? HVS: Visual Illusion

27. 27 Lecture 1 Which lines are straight? HVS: Visual Illusion

28. 28 Lecture 1 Color

29. 29 Lecture 1 Color: RGB Cube

30. 30 Lecture 1 Color: RGB Representation

31. 31 Lecture 1 Where Are We? Imaging? Computer Vision? Display/Printing? Digital Image Processing Computer Graphics? Biological Vision?

32. 32 Lecture 1 What Do We Do? Image Processing/ Manipulation Image Coding/ Communication Image Analysis/ Interpretation Digital Image Processing

33. 33 Lecture 1 Applications of DIP

34. 34 Lecture 1 Image Processing: Image Enhancement Enhance

35. 35 Lecture 1 Image Processing: Image Denoising Denoise

36. 36 Lecture 1 Image Processing: Image Deblurring Deblur

37. 37 Lecture 1 Image Processing: Image Inpainting

38. 38 Lecture 1 Image Processing: Image Stylization

39. 39 Lecture 1 Image Analysis: Edge Detection

40. 40 Lecture 1 Image Analysis: Face Detection

41. 41 Lecture 1 Image Analysis: Image Segmentation

42. 42 Lecture 1 Two deceivingly similar fingerprints of two different people Image Analysis: Image Matching

43. 43 Lecture 1 Image Coding: Image Compression compressed bitstream 00111000001001101… (2428 Bytes) image encoder image decoder original image 262144 Bytes compression ratio (CR) = 108:1 From [Gonzalez & Woods]

44. 44 Lecture 1 Lossless image compression –Information preserving original image can be exactly recovered –Low compression ratio –JPEG-LS, JBIG … Lossy image compression –Lose information original image can be recovered, but not the same –High compression ratio –JPEG, JPEG2000 … Image Coding: Image Compression

45. 45 Lecture 1 JPEG (CR=64)JPEG2000 (CR=64) discrete cosine transform basedwavelet transform based Image Coding: From JPEG to JPEG 2000

46. 46 Lecture 1 Image Coding: Video Compression From static images and image sequences (video) –From 2D to 3D –Strong correlations between frames –Representing motion Video compression –Compress each frame independently –Motion-compensated video compression high compression ratio –MPEG1, MPEG2, MPEG4, H.264 …

47. 47 Lecture 1 Image Quality/Distortion Measures _ = Y || X _ = Z _ = For each pixel: Mean Absolute Error (MAE):

48. 48 Lecture 1 Image Quality/Distortion Measures Mean Squared Error (MSE): Peak Signal-to-Noise Ratio (PSNR)  in decibel (dB): L : Dynamic range of pixel intensity L = 2 B – 1, where B is the number of bits to represent a pixel Examples: 8bits/pixel gray-scale image  L = 255 12bits/pixel gray-scale image  L = 4095

49. 49 Lecture 1 Image Quality/Distortion Measures original MAE = 0 MSE = 0 PSNR = infinity noisy image 1 MAE = 7.99 MSE = 100 PSNR = 28.1dB noisy image 2 MAE = 15.9 MSE = 394 PSNR = 22.2dB noisy image 3 MAE = 38.2 MSE = 2250 PSNR = 14.6dB

50. 50 Lecture 1 Image Quality/Distortion Measures _ = Y || X _ = Z _ = Example: two 4 x 4, 4bits/pixel image