TL;DR
This paper introduces ADMM-DAD net, a deep unfolding neural network based on ADMM for analysis compressed sensing, which jointly learns sparsification and signal reconstruction, outperforming existing methods on image and speech data.
Contribution
It presents a novel deep unfolding network that combines ADMM with learned analysis operators, improving reconstruction performance over prior state-of-the-art methods.
Findings
Outperforms existing deep unfolding networks on image datasets
Achieves superior results on speech datasets
Demonstrates robustness across different data types
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.
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Taxonomy
MethodsTest · Alternating Direction Method of Multipliers
