A Penalty Function Promoting Sparsity Within and Across Groups
\.Ilker Bayram, Sava\c{s}kan Bulek

TL;DR
This paper introduces a new weakly-convex penalty function that encourages sparsity both across groups and within individual groups, improving signal processing tasks like denoising and deconvolution.
Contribution
A novel weakly-convex penalty function is proposed, along with associated thresholding methods, for enhanced sparsity promotion in grouped signals.
Findings
Effective in denoising and deconvolution tasks
Outperforms state-of-the-art methods in experiments
Applicable to signals with isolated non-zero features
Abstract
We introduce a new weakly-convex penalty function for signals with a group behavior. The penalty promotes signals with a few number of active groups, where within each group, only a few high magnitude coefficients are active. We derive the threshold function associated with the proposed penalty and study its properties. We discuss how the proposed penalty/threshold function can be useful for signals with isolated non-zeros, such as audio with isolated harmonics along the frequency axis, or reflection functions in exploration seismology where the non-zeros occur on the boundaries of subsoil layers. We demonstrate the use of the proposed penalty/threshold functions in a convex denoising and a non-convex deconvolution formulation. We provide convergent algorithms for both formulations and compare the performance with state-of-the-art methods.
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