Bayesian Blind Deconvolution With General Sparse Image Priors - implementation -

Here is nice algorithm implementation sent to me by Derin to start the week: 

Bayesian Blind Deconvolution With General Sparse Image Priors by S. Derin Babacan, Rafael Molina, Minh N. Do, Aggelos K. Katsaggelos. The abstract reads:

 We present a general method for blind image deconvolution using Bayesian inference with super-Gaussian sparse image priors. We consider a large family of priors suitable for modeling natural images, and develop the general procedure for estimating the unknown image and the blur. Our formulation includes a number of existing modeling and inference methods as special cases while providing additional flexibility in image modeling and algorithm design. We also present an analysis of the proposed inference compared to other methods and discuss its advantages. Theoretical and experimental results demonstrate that the proposed formulation is very effective, efficient, and flexible.

The supplementary technical note and implementation of the algorithm are available here.

Thanks Derin !

Join our Reddit Experiment, Join the CompressiveSensing subreddit and post there !

Liked this entry ? subscribe to Nuit Blanche's feed, there's more where that came from. You can also subscribe to Nuit Blanche by Email, explore the Big Picture in Compressive Sensing or the Matrix Factorization Jungle and join the conversations on compressive sensing, advanced matrix factorization and calibration issues on Linkedin.