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Chapter 7 Case Study 1: Image Deconvolution

Chapter 7 Case Study 1: Image Deconvolution. Different Types of Image Blur. Defocus blur --- Depth of field effects Scene motion --- Objects in the scene moving Camera shake --- User moving hands. Image formation model: Convolution. Blind vs. non-blind deconvolution.

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Chapter 7 Case Study 1: Image Deconvolution

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  1. Chapter 7 Case Study 1: Image Deconvolution

  2. Different Types of Image Blur • Defocus blur • --- Depth of field effects • Scene motion • --- Objects in the scene moving • Camera shake • --- User moving hands

  3. Image formation model: Convolution

  4. Blind vs. non-blind deconvolution

  5. Image deconvolution is ill-posed

  6. Why is this hard?

  7. Case study 1:Deblurring with Blind Deconvolution

  8. Probabilistic Model of Image Formation

  9. Deconvolutionwith prior

  10. Likelihood P(Y | K, X)

  11. Image prior P(X):Use parametric model (mixture of Gaussians) of sharp image statistics Blurry images have different statistics

  12. Blur prior P(K): Positive and Sparse

  13. The obvious thing to do: a MAP Solver No success!

  14. Deconvolutionwith prior using Variational Bayesian approach

  15. Deconvolutionwith prior using Variational Bayesian approach

  16. Preprocessing

  17. Initialization

  18. Inferring the kernel: multiscale method

  19. Image Reconstruction

  20. Case study 2:Motion deblurring • from a single image

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