Thursday, February 23, 2017

Automatic Parameter Tuning for Image Denoising with Learned Spasifying Transforms

A first step toward automating dictionary learning ! Ican see some potential that slowly but surely Luke will come to the other side of the Deep Learning Force :-)

Automatic Parameter Tuning for Image Denoising with Learned Spasifying Transforms by Luke Pfister and Yoram Bresler

Data-driven and learning-based sparse signal models outperform analytical models (e.g, wavelets), for image denoising, but require careful parameter tuning to reach peak performance. In this work, we provide a solution to the problem of parameter tuning for image denoising with transform sparsity regularization. We show that by viewing a learned sparsifying transform as a filter bank we can utilize the SURELET denoising algorithm to automatically tune parameters for an image denoising task. Numerical experiments show that combining SURELET with a learned sparsifying transform provides the best of both worlds. Our approach requires no parameter tuning for image denoising, yet outperforms SURELET with analytic transforms and matches the performance of transform learning denoising with hand-tuned parame-ters 

incidently I just noticed the Transform Learning page aiming to provide Sparse Representations at Scale. 
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