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Optimal Image Restoration: Wiener and Inverse Filtering Techniques

Explore the power of Wiener and Inverse filtering for image restoration. Learn how to deblur images using Pseudo-Inverse and Radially Limited filters. Dive into concepts of signal and noise power estimation to enhance image quality efficiently. Discover the comparison between Inverse and Wiener Filtering methods.

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Optimal Image Restoration: Wiener and Inverse Filtering Techniques

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  1. Chapter 5 Image Restoration

  2. Chapter 5 Image Restoration

  3. Chapter 5 Image Restoration

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  28. ^ x(m,n) Deblurring: Pseudo-Inverse Filtering h(m,n) x(m,n) y(m,n) g(m,n) blurring filter deblur filter 1 What if at some(u,v), H(u,v) is 0 (or very close to 0) ? Inverse filter: G(u,v) = H(u,v) small threshold Pseudo-inverse filter:

  29. Inverse and Pseudo-Inverse Filtering blurred image  = 0.1

  30. Chapter 5 Image Restoration

  31. Chapter 5 Image Restoration

  32. Chapter 5 Image Restoration

  33. Chapter 5 Image Restoration

  34. Chapter 5 Image Restoration

  35. Wiener (Least Square) Filtering Wiener filter: noise power signal power • Optimal in the least MSE sense, i.e. • G(u, v) is the best possible linear filter that minimizes • Have to estimate signal and noise power

  36. Inverse filtering Blurred image Radially limited inverse filtering R = 70 Weiner filtering From [Gonzalez & Woods]

  37. Inverse vs. Weiner Filtering distorted inverse filtering Wiener filtering motion blur + noise less noise less noise From [Gonzalez & Woods]

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