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RED: Regularization by Denoising

Presented by: 
Peyman Milanfar Google, Google
Tuesday 31st October 2017 - 09:00 to 09:50
INI Seminar Room 1
Image denoising has reached impressive heights in performance and quality -- almost as good as it can ever get. But this isn't the only way in which tasks in image processing can exploit the image denoising engine. I will describe Regularization by Denoising (RED): using the denoising engine in defining the regularization of any inverse problem. We propose an explicit image-adaptive Laplacian-based regularization functional that makes the overall penalty defined by the denoiser clear and well-defined. With complete flexibility to choose the iterative optimization procedure for minimizing this functional, RED is capable of incorporating any image denoising algorithm as a regularizer, treat general inverse problems very effectively, and is guaranteed to converge to a globally optimal result. I will show examples of its utility, including state-of-the-art results in image deblurring and super-resolution problems. (Joint work with Yaniv Romano and Michael Elad)
University of Cambridge Research Councils UK
    Clay Mathematics Institute London Mathematical Society NM Rothschild and Sons