Error Resilient Deep Compressive Sensing

TL;DR

This paper introduces a robust deep learning-based compressive sensing method that maintains error resilience even when measurements are randomly lost, improving signal reconstruction reliability.

Contribution

A novel deep reconstruction network with a measurement lost layer that simulates measurement loss, ensuring error resilience in compressive sensing.

Findings

Enhanced error resilience in deep compressive sensing

Effective simulation of measurement loss during training

Maintains reconstruction quality under measurement loss

Abstract

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.