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EE 4780

EE 4780. Image Enhancement. Image Enhancement. The objective of image enhancement is to process an image so that the result is more suitable than the original image for a specific application. There are two main approaches:

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EE 4780

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  1. EE 4780 Image Enhancement

  2. Image Enhancement • The objective of image enhancement is to process an image so that the result is more suitable than the original image for a specific application. • There are two main approaches: • Image enhancement in spatial domain: Direct manipulation of pixels in an image • Point processing: Change pixel intensities • Spatial filtering • Image enhancement in frequency domain: Modifying the Fourier transform of an image

  3. Image Enhancement by Point Processing • Intensity Transformation

  4. Image Enhancement by Point Processing • Contrast Stretching

  5. Image Enhancement by Point Processing • Contrast Stretching

  6. Image Enhancement by Point Processing • Intensity Transformation Matlab exercise

  7. Image Enhancement by Point Processing • Intensity Transformation

  8. Image Enhancement by Point Processing • Intensity Transformation

  9. Image Enhancement by Point Processing • Gray-Level Slicing

  10. Image Enhancement by Point Processing • Histogram 255 0

  11. Histogram Specification • Intensity mapping • Assume • T(r) is single-valued and monotonically increasing. • The original and transformed intensities can be characterized by their probability density functions (PDFs)

  12. Histogram Specification • The relationship between the PDFs is • Consider the mapping Cumulative distribution function of r Histogram equalization!

  13. Image Enhancement by Point Processing • Histogram Equalization

  14. Image Enhancement by Point Processing • Histogram Equalization Example Intensity 0 1 2 3 4 5 6 7 Number of pixels 10 20 12 8 0 0 0 0 Intensity 0 1 2 3 4 5 6 7 Number of pixels 0 10 0 0 20 0 12 8

  15. Image Enhancement by Point Processing • Histogram Equalization

  16. Local Histogram Processing • Histogram processing can be applied locally.

  17. Image Subtraction The background is subtracted out, the arteries appear bright.

  18. Image Averaging Corrupted image Original image Noise Assume n(x,y) a white noise with mean=0, and variance If we have a set of noisy images The noise variance in the average image is

  19. Image Averaging

  20. Spatial Filtering A low-pass filter A high-pass filter

  21. Spatial Filtering • Median Filter Sort: (10 10 10 20 25 75 85 90 100) • Example Original signal: 100 100 100 100 10 10 10 10 10 Noisy signal: 100 103 100 100 10 9 10 11 10 Filter by [ 1 1 1]/3: 101 101 70 40 10 10 10 Filter by 1x3 median filter: 100 100 100 10 10 10 10

  22. Spatial Filtering • Median filters are nonlinear. • Median filtering reduces noise without blurring edges and other sharp details. • Median filtering is particularly effective when the noise pattern consists of strong, spike-like components. (Salt-and-pepper noise.)

  23. Spatial Filtering Salt&Pepper noise added Original 3x3 averaging filter 3x3 median filter

  24. Spatial Filtering

  25. Wiener Filter Noisy image Original image Noise Wiener Filter Signal variance Noise variance

  26. Wiener Filter is estimated by Since variance is nonnegative, it is modified as Estimate signal variance locally: N N

  27. Wiener Filter Denoised (3x3neighborhood) Mean Squared Error is 56 Noisy, =10 wiener2 in Matlab

  28. Spatial Filtering

  29. Spatial Filtering • High-boost or high-frequency-emphasis filter • Sharpens the image but does not remove the low-frequency components unlike high-pass filtering

  30. Spatial Filtering • High-boost or high-frequency-emphasis filter • High pass = Original – Low pass • High boost = (Original) + K*(High pass)

  31. Spatial Filtering A high-pass filter A high-boost filter

  32. Spatial Filtering • High-boost or high-frequency-emphasis filter

  33. Spatial Filtering

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