Correcting Camera Shake by Incremental Sparse Approximation - implementation -

[ Update: there is a new version of the paper here. For more info check This Week's Guardians of Science: Zeno Gantner and Peyman Milanfar ]

Paul Shearer just sent me the following:

"Dear Igor,

I've recently submitted a paper on sparsity-based blind deconvolution that I thought might be of interest to some Nuit Blanche readers. The arXiv link is

http://arxiv.org/abs/1302.0439

and a MATLAB implementation (with data reproducing the claimed results) is available on my website:

http://sitemaker.umich.edu/shearerp/home

The abstract is below my signature.

Cheers,

Paul Shearer

PhD candidate, Applied Mathematics

University of Michigan, Ann Arbor

shearerp@umich.edu"

Thank you Paul  !

Correcting Camera Shake by Incremental Sparse Approximation by Paul Shearer, Anna C. Gilbert, Alfred O. Hero III

The problem of deblurring an image when the blur kernel is unknown remains challenging after decades of work. Recently there has been rapid progress on correcting irregular blur patterns caused by camera shake, but there is still much room for improvement. We propose a new blind deconvolution method using incremental sparse edge approximation to recover images blurred by camera shake. We estimate the blur kernel first from only the strongest edges in the image, then gradually refine this estimate by allowing for weaker and weaker edges. Our method matches the benchmark deblurring performance of the state-of-the-art while being significantly faster and easier to generalize.

Previous featured papers on Blind Deconvolution can be found here.



Join the CompressiveSensing subreddit or the Google+ Community 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.