1 / 34

Chap. 3: Image Enhancement in the Spatial Domain

Chap. 3: Image Enhancement in the Spatial Domain. Spring 2006, Jen-Chang Liu CSIE, NCNU. Announcement. Where to find MATLAB ? It ’ s not free … A book contains the student ’ s edition of MATLAB NCNU CC support 50 on-line version in the computer rooms (307, 308, 208, 413). Image Enhancement.

dora-tillman
Download Presentation

Chap. 3: Image Enhancement in the Spatial Domain

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Chap. 3: Image Enhancement in the Spatial Domain Spring 2006, Jen-Chang Liu CSIE, NCNU

  2. Announcement • Where to find MATLAB ? • It’s not free… • A book contains the student’s edition of MATLAB • NCNU CC support 50 on-line version in the computer rooms (307, 308, 208, 413)

  3. Image Enhancement • Goal: process an image so that the result is more suitable than the original image for a specific application • Visual interpretation • Problem oriented

  4. Image enhancement example

  5. Two categories • There is no general theory of image enhancement • Spatial domain • Direct manipulation of pixels • Point processing • Neighborhood processing • Frequency domain • Modify the Fourier transform of an image

  6. Outline: spatial domain operations • Background • Gray level transformations • Arithmetic/logic operations

  7. f(x,y) g(x,y) T Background • Spatial domain processing • g(x,y)=T[ f(x,y) ] • f(x,y): input image • g(x,y): output image • T: operator • Defined over some neighborhood of (x,y)

  8. Background (cont.) * T applies to each pixel in the input image * T operates over neighborhood of (x,y)

  9. thresholding Point processing • 1x1 neighborhood • Gray level transformation, or point processing • s = T(r) contrast stretching

  10. Neighborhood processing • A larger predefined neighborhood • Ex. 3x3 neighborhood • mask, filters, kernels, templates, windows • Mask processing or filtering

  11. Some Basic Gray Level Transformations • Image negatives (complement) • Log transformation • Power-law transform • Piece-wise linear transform • Gray level slicing • Bit plane slicing

  12. Some gray level transformations • Lookup table • Functional form

  13. Image negatives • Photographic negative 負片 • Suitable for images with dominant black areas Original mammogram(乳房X光片)

  14. Log transformations • s = c log(1+r) • Compress the dynamic range of images with large variation in pixel values

  15. Example: Log transformations • log(fft2(I)) : log of Fourier transform log 2d Fourier transform 頻譜圖

  16. Power-law transformations • s=cr • >1 • <1 • : gamma • display, printers, scanners follow power-law • Gamma correction

  17. =1/2.5 =2.5 Example: Gamma correction 顯示器偏差 原始影像 • CRT: intensity-to-voltage response follow a power-law. 1.8<<2.5 =2.5

  18. <1 Power-law: • Expand dark gray levels =0.6 =0.4 =0.3

  19. Power-law: >1 • Expand light gray levels =3 =4 =5

  20. Piece-wise linear transformations control point • Advantage: the piecewise function can be arbitrarily complex

  21. Contrast stretching How to automatically adjust the gray levels?

  22. Gray-level slicing 切片 • Highlighting a specific range of gray levels

  23. 1 0 0 1 0 1 0 0 Ex. 15010 Bit-plane slicing * Highlight specific bits bit-planes of an image (gray level 0~255)

  24. Bit-plane slicing: example

  25. For image compression 6 7 5 3 4 0 1 2

  26. Arithmetic/logic operations • Logic operations • Image subtraction • Image averaging

  27. Logic operations • Logic operations: pixel-wise AND, OR, NOT • The pixel gray level values are taken as string of binary numbers • Use the binary mask to take out the region of interest(ROI) from an image Ex. 193 => 11000001

  28. Logic operations: example A B A and B AND A or B OR

  29. Image subtraction f:original(8 bits) h:4 sig. bits • Difference image g(x,y)=f(x,y)-h(x,y) scaling difference image

  30. Image subtraction: scaling the difference image • g(x,y)=f(x,y)-h(x,y) • f and h are 8-bit => g(x,y)  [-255, 255] • (1)+255 (2) divide by 2 • The result won’t cover [0,255] • (1)-min(g) (2) *255/max(g) Be careful of the dynamic range after the image is processed.

  31. Image subtraction example: mask mode radiography • Inject contrast medium into bloodstream original (head) difference image 注射碘液 拍攝影像 與原影像 相減

  32. Image averaging • Noisy image g(x,y)=f(x,y)+η(x,y) • Suppose η(x,y) is uncorrelated and has zero mean original noise Clear image Noisy image

  33. Image averaging: noise reduction • Averaging over K noisy images gi(x,y) 期望值接近原圖

  34. original Gaussian noise averaging K=8 averaging K=16 averaging K=64 averaging K=128

More Related