252 research outputs found
DECONET: an Unfolding Network for Analysis-based Compressed Sensing with Generalization Error Bounds
We present a new deep unfolding network for analysis-sparsity-based
Compressed Sensing. The proposed network coined Decoding Network (DECONET)
jointly learns a decoder that reconstructs vectors from their incomplete, noisy
measurements and a redundant sparsifying analysis operator, which is shared
across the layers of DECONET. Moreover, we formulate the hypothesis class of
DECONET and estimate its associated Rademacher complexity. Then, we use this
estimate to deliver meaningful upper bounds for the generalization error of
DECONET. Finally, the validity of our theoretical results is assessed and
comparisons to state-of-the-art unfolding networks are made, on both synthetic
and real-world datasets. Experimental results indicate that our proposed
network outperforms the baselines, consistently for all datasets, and its
behaviour complies with our theoretical findings.Comment: Accepted in IEEE Transactions on Signal Processin
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