Solve Mismatch Problem in Compressed Sensing

TL;DR

This paper introduces a new algorithm to address the mismatch problem in compressed sensing by transforming it into a matched problem through a specially constructed measurement matrix, improving image recovery under low noise conditions.

Contribution

It presents a novel approach that transforms the mismatch problem into a matched problem using a new measurement matrix, with algorithms for solution and calibration.

Findings

Effective in transforming mismatch to match under low noise

Improves image recovery accuracy

Algorithms demonstrated through experiments

Abstract

This article proposes a novel algorithm for solving mismatch problem in compressed sensing. Its core is to transform mismatch problem into matched by constructing a new measurement matrix to match measurement value under unknown measurement matrix. Therefore, we propose mismatch equation and establish two types of algorithm based on it, which are matched solution of unknown measurement matrix and calibration of unknown measurement matrix. Experiments have shown that when under low gaussian noise levels, the constructed measurement matrix can transform the mismatch problem into matched and recover original images. The code is available: https://github.com/yanglebupt/mismatch-solution