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Blur

EE4328, Section 005 Introduction to Digital Image Processing Linear Image Restoration Zhou Wang Dept. of Electrical Engineering The Univ. of Texas at Arlington Fall 2006. Blur. From Prof. Xin Li. out-of-focus blur. motion blur. Question 1: How do you know they are blurred?

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Blur

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  1. EE4328, Section 005 Introduction to Digital Image ProcessingLinear Image RestorationZhou WangDept. of Electrical EngineeringThe Univ. of Texas at ArlingtonFall 2006

  2. Blur From Prof. Xin Li out-of-focus blur motion blur Question 1: How do you know they are blurred? I’ve not shown you the originals! Question 2: How do I deblur an image?

  3. Linear Blur Model • Spatial domain h(m,n) x(m,n) y(m,n) blurring filter From Prof. Xin Li Gaussian blur motion blur

  4. Linear Blur Model • Frequency (2D-DFT) domain H(u,v) X(u,v) Y(u,v) blurring filter From Prof. Xin Li Gaussian blur motion blur

  5. Blurring Effect Gaussian blur motion blur From [Gonzalez & Woods]

  6. ^ ^ ^ x(m,n) x(m,n) x(m,n) Image Restoration: Deblurring/Deconvolution h(m,n) x(m,n) y(m,n) g(m,n) blurring filter deblurring/ deconvolution filter • Non-blind deblurring/deconvolution Given: observation y(m,n) and blurring function h(m,n) Design: g(m,n), such that the distortion between x(m,n) and is minimized • Blind deblurring/deconvolution Given: observation y(m,n) Design: g(m,n), such that the distortion between x(m,n) and is minimized

  7. ^ x(m,n) Deblurring: Inverse Filtering h(m,n) x(m,n) y(m,n) g(m,n) blurring filter inverse filter X(u,v) H(u,v) = Y(u,v) 1 G(u,v) = H(u,v) Y(u,v) 1 X(u,v) = = Y(u,v) Exact recovery! H(u,v) H(u,v)

  8. ^ 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:

  9. Inverse and Pseudo-Inverse Filtering blurred image Adapted from Prof. Xin Li  = 0.1

  10. + More Realistic Distortion Model additive white Gaussian noise w(m,n) y(m,n) ^ h(m,n) g(m,n) x(m,n) x(m,n) blurring filter deblur filter Y(u,v) = X(u,v) H(u,v) + W(u,v) • What happens when an inverse filter is applied? close to zero at high frequencies

  11. Radially Limited Inverse Filtering Radially limited inverse filter: • Motivation • Energy of image signals is concentrated at low frequencies • Energy of noise uniformly is distributed over all frequencies • Inverse filtering of image signal dominated regions only R

  12. Radially Limited Inverse Filtering Image size: 480x480 Original Blurred Inverse filtered Radially limited inverse filtering: R = 85 R = 40 R = 70 From [Gonzalez & Woods]

  13. 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

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

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

  16. + Weiner Image Denoising w(m,n) y(m,n) h(m,n) x(m,n) • What if no blur, but only noise, i.e. h(m,n) is an impulse or H(u, v) = 1 ? Wiener filter: where for H(u,v) = 1 Wiener denoising filter: Typically applied locally in space

  17. Weiner Image Denoising adding noise noise var = 400 local Wiener denoising

  18. Summary of Linear Image Restoration Filters 1 Inverse filter: G(u,v) = H(u,v) Pseudo-inverse filter: Radially limited inverse filter: where Wiener filter: Wiener denoising filter:

  19. Examples • A blur filter h(m,n) has a 2D-DFT given by • Find the deblur filter G(u,v) using • The inverse filtering approach • The pseudo-inverse filtering approach, with  = 0.05 • The pseudo-inverse filtering approach, with = 0.2 • Wiener filtering approach, with and

  20. Examples • Inverse filter • Pseudo-inverse filter, with  = 0.05

  21. Examples • Pseudo-inverse filter, with = 0.2 • Wiener filter, with and

  22. Advanced Image Restoration • Adaptive Processing • Spatial adaptive • Frequency adaptive • Nonlinear Processing • Thresholding, coring … • Iterative restoration • Advanced Transformation / Modeling • Advanced image transforms, e.g., wavelet … • Statistical image modeling • Blind Deblurring / Deconvolution

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