Computational Imaging/ Image Processing Seminar

Apr 14

Friday, April 14, 2017

12:00 pm - 1:00 pm
Gross Hall, 330 -- Ahmadieh Family Grand Hall

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Peyman Milanfar - Google Research

Regularization by Denoising (RED) ABSTRACT: Image denoising is the most fundamental problem in image enhancement, and it is largely solved: It has reached impressive heights in performance and quality -- almost as good as it can ever get. But interestingly, it turns out that we can solve many other problems using the image denoising "engine". I will describe the Regularization by Denoising (RED) framework: using the denoising engine in defining the regularization of any inverse problem. The idea is to define an explicit image-adaptive regularization functional directly using a high performance denoiser. Surprisingly, the resulting regularizer is guaranteed to be convex, and the overall objective functional is explicit, 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 the globally optimal result.


Dawn, Ariel