Deep Learning Approaches for Sparse Recovery in Compressive Sensing
Résumé
Compressive sensing enables sparse signals recovery by less measurements than required by the Nyquist rate, so leading to energy and processing saving. Accuracy and complexity improvements can be achieved applying neural network to sparse linear inverse problem. This work focuses on sparse recovery with deep network. Improvements to the Learned Iterative Shrinkage-Thresholding Algorithm (LISTA) and a novel neural network are proposed. Results show that these propositions can decrease up to 10.8 dB the NMSE value and require fewer layers than if only LISTA is used to estimate the signal.