ADMM-DAD net: a deep unfolding network for analysis compressed sensing
Information Theory
2022-05-03 v5 Computer Vision and Pattern Recognition
Information Retrieval
Machine Learning
math.IT
Abstract
In this paper, we propose a new deep unfolding neural network based on the ADMM algorithm for analysis Compressed Sensing. The proposed network jointly learns a redundant analysis operator for sparsification and reconstructs the signal of interest. We compare our proposed network with a state-of-the-art unfolded ISTA decoder, that also learns an orthogonal sparsifier. Moreover, we consider not only image, but also speech datasets as test examples. Computational experiments demonstrate that our proposed network outperforms the state-of-the-art deep unfolding network, consistently for both real-world image and speech datasets.
Keywords
Cite
@article{arxiv.2110.06986,
title = {ADMM-DAD net: a deep unfolding network for analysis compressed sensing},
author = {Vasiliki Kouni and Georgios Paraskevopoulos and Holger Rauhut and George C. Alexandropoulos},
journal= {arXiv preprint arXiv:2110.06986},
year = {2022}
}