A multilevel based reweighting algorithm with joint regularizers for sparse recovery

TL;DR

This paper introduces a multilevel reweighting algorithm with joint regularizers, combining wavelet-based sparsity promotion and TGV, to improve image reconstruction from Fourier measurements with enhanced performance and efficiency.

Contribution

It proposes a novel split Bregman algorithm integrating multilevel reweighted  and TGV regularizers for sparse signal recovery, improving upon existing methods.

Findings

Significantly better reconstruction quality in experiments.

Lower computational costs compared to established methods.

Enhanced exploitation of sparsity in signal recovery.

Abstract

Sparsity is one of the key concepts that allows the recovery of signals that are subsampled at a rate significantly lower than required by the Nyquist-Shannon sampling theorem. Our proposed framework uses arbitrary multiscale transforms, such as those build upon wavelets or shearlets, as a sparsity promoting prior which allow to decompose the image into different scales such that image features can be optimally extracted. In order to further exploit the sparsity of the recovered signal we combine the method of reweighted $\ell^1$, introduced by Cand\`es et al., with iteratively updated weights accounting for the multilevel structure of the signal. This is done by directly incorporating this approach into a split Bregman based algorithmic framework. Furthermore, we add total generalized variation (TGV) as a second regularizer into the split Bregman algorithm. The resulting algorithm is then applied to a classical and widely considered task in signal- and image processing which is the reconstruction of images from their Fourier measurements. Our numerical experiments show a highly improved performance at relatively low computational costs compared to many other well established methods and strongly suggest that sparsity is better exploited by our method.