ADMM-DAD Net: A Deep Unfolding Network for Analysis Compressed Sensing

Vasiliki Kouni; Georgios Paraskevopoulos; Holger Rauhut; George C. Alexandropoulos

ICASSP 2022accepted0 citations

ADMM-DAD Net: A Deep Unfolding Network for Analysis Compressed Sensing

Vasiliki Kouni, Georgios Paraskevopoulos, Holger Rauhut, George C. Alexandropoulos

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.

BibTeX

@inproceedings{icassp2022_admmdadnetadeepu,
  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},
  booktitle = {ICASSP 2022},
  year = {2022}
}