Error Resilient Deep Compressive Sensing

Compressive sensing is a strategically sampling technology that enables reconstructing signals from a subset of measurements lower than the Nyquist sampling rate, even when the measurement data are corrupted. Deep learning-based compressive sensing has recently enable to improve system performance, especially in terms of reconstruction quality, while maintaining low computational complexity. However, this framework requires a training network for each measurement rate. Also, concerning the transmission scheme of measurements lost, deep learning-based compressive sensing cannot recover the original signal. Thereby, it fails to maintain the error-resilient property. In this work, we propose 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. Numerical results demonstrate the improvements of our proposed method compared to the other benchmarks.