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. Deconvolution with 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. Deconvolution with 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