Fast image deconvolution using Hyper-Laplacian Prior

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

Outline • Introduction • Algorithm • Experiment result

introduction • Hyper-Laplacian Prior • speed

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

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

Solve sub-problem Recall: • Fixed w

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

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

Summary of the algorithm

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

result

Fast image deconvolution using Hyper-Laplacian Prior

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