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Lecture 3 (2.5.07) Image Enhancement in Spatial Domain

Lecture 3 (2.5.07) Image Enhancement in Spatial Domain. Shahram Ebadollahi. DIP ELEN E4830. Today’s Lecture - Outline. Review of Lecture 2 Processing Images in Spatial Domain: Intro Image Histogram Point Operations Using Histogram for Image Enhancement Kernel Operations.

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Lecture 3 (2.5.07) Image Enhancement in Spatial Domain

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  1. Lecture 3(2.5.07)Image Enhancement in Spatial Domain Shahram Ebadollahi DIP ELEN E4830

  2. Today’s Lecture - Outline • Review of Lecture 2 • Processing Images in Spatial Domain: Intro • Image Histogram • Point Operations • Using Histogram for Image Enhancement • Kernel Operations

  3. Today’s Lecture - Outline • Review of Lecture 2 • Processing Images in Spatial Domain: Intro • Image Histogram • Point Operations • Using Histogram for Image Enhancement • Kernel Operations

  4. Today’s Lecture - Outline • Review of Lecture 2 • Processing Images in Spatial Domain: Intro • Image Histogram • Point Operations • Using Histogram for Image Enhancement • Kernel Operations

  5. Processing Images in Spatial Domain: Introduction : Spatial operator defined on a neighborhood N of a given pixel point processing mask processing

  6. Mask (filter, kernel, window, template) processing y y (0,0) (0,0) x x

  7. Today’s Lecture - Outline • Review of Lecture 2 • Processing Images in Spatial Domain: Intro • Image Histogram • Point Operations • Using Histogram for Image Enhancement • Kernel Operations

  8. H H Image Histogram normalized histogram 0.5 bi-level image 0 255 256x256 Pixel values linearly increasing from 0 to 255 with increasing column index histogram 1/256 0 255 256x256

  9. Image Histogram: example

  10. Today’s Lecture - Outline • Review of Lecture 2 • Processing Images in Spatial Domain: Intro • Image Histogram • Point Operations • Using Histogram for Image Enhancement • Kernel Operations

  11. Point Processing:Thresholding Input gray-level value Output gray-level value

  12. Point Processing:Gamma Correction

  13. L-1 0 L-1 Point Processing:Contrast Stretching

  14. 0 L-1 0 L-1 Point Processing: Clipping & Thresholding clipping thresholding

  15. Point Processing:Gray-level Slicing 0 L-1 0 L-1

  16. Point Processing:Bit-plane Slicing lsb msb where, e.g.

  17. Point Processing:Bit-plane Slicing (example) Point operation for obtaining n-th bit-plane: Bi-level image n=7 n=6 n=4 n=5

  18. Today’s Lecture - Outline • Review of Lecture 2 • Processing Images in Spatial Domain: Intro • Image Histogram • Point Operations • Using Histogram for Image Enhancement • Kernel Operations

  19. Apply a transform to an image such that the resulting image has desired histogram. Histogram Equalization (linearization) Histogram Specification (matching) Non-adaptive vs. Adaptive Histogram Modification Global histogram Local histogram Histogram Modification

  20. Histogram Equalization Equalized Image Source image Corresponding Histograms

  21. Often images poorly use the full range of the gray scale Solution: Transform image such that its histogram is spread out more evenly in gray scale Rather than guessing the parameters and the form of the transformation use original gray-scale distribution as the cue Histogram Equalization

  22. Histogram Equalization Histogram Equalization # pixels with the j-th gray-level Point operation for equalizing histogram for the example image image size

  23. Transform image such that resulting image has specified histogram Histogram Matching Histogram Matching

  24. Histogram Matching

  25. Adaptive Histogram Equalization y (0,0) Histogram Equalization Note: local structure revealed x

  26. Today’s Lecture - Outline • Review of Lecture 2 • Processing Images in Spatial Domain: Intro • Image Histogram • Point Operations • Using Histogram for Image Enhancement • Kernel Operations

  27. Kernel Operator: Intro Note: need to handle borders of the image

  28. Kernel Operator: Intro Spatial Filtering kernel

  29. Low-pass filter FT FT Smoothing: Image Averaging * Image edges are softened

  30. Smoothing: Averaging (example) original 3x3 5x5 9x9 Noise effect is gone, but image edges are heavily blurred also 15x15 35x35

  31. Order Statistics Filter original

  32. Image Derivative

  33. Image Sharpening: 1-st derivative Image gradient: Robert’s operator Sobel filter in frequency domain Sobel’s operator

  34. Image Sharpening: 2-nd derivative Image Laplacian:

  35. Image Sharpening: 2-nd derivative + * Laplacian filter in frequency domain

  36. High-boost Filtering Avg. - + + Unsharp mask: high-boost with A=1

  37. Recap • Point operations vs. Kernel Operations • Image Histogram • Image Enhancement using Point Operators • Contrast Stretching • Gamma Correction • Using Image Histogram for Enhancement • Histogram Equalization • Histogram Matching • Image Enhancement using Kernel Operators • Low-pass filtering (averaging) • High-pass filtering (sharpening)

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