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Diego Carrera, Alessandro Foi, Giacomo Boracchi and Brendt Wohlberg, "On the Weighting for Convolutional Sparse Coding", in Signal Processing with Adaptive Sparse Structured Representations (SPARS), (Toulouse, France), Jul 2019

Abstract

We consider image denoising via convolutional sparse coding with weighted \ell-1 penalization, and investigate the rationale behind the weighting scheme based on the reciprocal correlation between the dictionary and the image. We show that this weighting scheme, which has recently been proposed for convolutional sparse coding, yields, in case of orthonormal dictionaries, weights that are very close to the oracle weights in WaveShrink, i.e. the MSE-optimal soft thresholds. Furthermore, our empirical analysis shows that in the convolutional case, both weighting schemes achieve comparable denoising quality, providing a substantial improvement over the standard uniform weights.

BibTeX Entry

@inproceedings{carrera-2019-weighting,
author = {Diego Carrera and Alessandro Foi and Giacomo Boracchi and Brendt Wohlberg},
title = {On the Weighting for Convolutional Sparse Coding},
year = {2019},
month = Jul,
urlpdf = {http://www.conftool.net/spars2019/index.php/Abstract256-CARRERA-256.pdf?page=downloadPaper&filename=Abstract256-CARRERA-256.pdf&form_id=256&form_version=final},
booktitle = {Signal Processing with Adaptive Sparse Structured Representations (SPARS)},
address = {Toulouse, France}
}