Compressive sensing (CS) is an emerging sampling technology that enables reconstructing signals from a subset of measurements and even corrupted measurements. Deep learning-based compressive sensing (DCS) has improved CS performance while maintaining a fast reconstruction but requires a training network for each measurement rate. Also, concerning the transmission scheme of measurement lost, DCS cannot recover the original signal. Thereby, it fails to maintain the error-resilient property. In this work, we proposed a robust deep reconstruction network to preserve the error-resilient property under the assumption of random measurement lost. Measurement lost layer is proposed to simulate the measurement lost in an end-to-end framework.
@article{arxiv.1911.12507,
title = {Error Resilient Deep Compressive Sensing},
author = {Thuong and Nguyen Canh and Chien and Trinh Van},
journal= {arXiv preprint arXiv:1911.12507},
year = {2019}
}