1. Fast image deconvolution using Hyper-Laplacian Prior
Dilip Krishnan Rob Fergus
New york University
Presented by Zhengming Xing

2. Outline
Introduction
Algorithm
Experiment result

3. introduction
Hyper-Laplacian Prior
speed

4. algorithm For non-blind deconvolution problem
Given y (the blurred image), and k( blur kernel), x(original image). Assume Gaussian noise.
Hyper-Laplacain prior
Minimize

5. Optimize problem recall
Half quadratic penalty method, introduce auxiliary variable.And consider the one special case.

6. Solve sub-problem
Recall:
Fixed w

7. Solve sub-problem Fixed X Recall:
Lookup table: pre-compute solution for different
Analytic solution: for particular value of

8. Recall:
Take derivative
Compare the different root and find the global minimum

9. Summary of the algorithm

10. Summary of the algorithm

11. Experiment description
Grey scale real world image, blurred by camera shaked kernels and add Gaussian noise. The kernels are minor perturbed.
Measured with the SNR

12. result

13. result

14. result