ADMM-DAD net: a deep unfolding network for analysis compressed sensing

• URL: http://arxiv.org/abs/2110.06986v1
• Date: Wed, 13 Oct 2021 18:56:59 GMT
• Title: ADMM-DAD net: a deep unfolding network for analysis compressed sensing
• Authors: Vasiliki Kouni, Georgios Paraskevopoulos, Holger Rauhut, George C. Alexandropoulos
• Abstract summary: 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.
• Score: 20.88999913266683
• License: http://creativecommons.org/licenses/by/4.0/
• 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 networks, consistently for both real-world image and speech datasets.

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