A Greedy Blind Calibration Method for Compressed Sensing with Unknown Sensor Gains

TL;DR

This paper introduces a greedy blind calibration method for compressed sensing systems with unknown sensor gains, enabling accurate signal recovery with minimal measurements by iteratively estimating both the signal and sensor discrepancies.

Contribution

It presents a novel greedy algorithm based on projected gradient descent for blind calibration in compressed sensing, addressing the bilinear inverse problem with unknown sensor gains.

Findings

Exact recovery achieved with few snapshots in compressive imaging

Algorithm demonstrates favorable phase transition behavior

Sample complexity requirements are empirically characterized

Abstract

The realisation of sensing modalities based on the principles of compressed sensing is often hindered by discrepancies between the mathematical model of its sensing operator, which is necessary during signal recovery, and its actual physical implementation, which can amply differ from the assumed model. In this paper we tackle the bilinear inverse problem of recovering a sparse input signal and some unknown, unstructured multiplicative factors affecting the sensors that capture each compressive measurement. Our methodology relies on collecting a few snapshots under new draws of the sensing operator, and applying a greedy algorithm based on projected gradient descent and the principles of iterative hard thresholding. We explore empirically the sample complexity requirements of this algorithm by testing its phase transition, and show in a practically relevant instance of this problem for compressive imaging that the exact solution can be obtained with only a few snapshots.