Generalized Alternating Projection Based Total Variation Minimization for Compressive Sensing

TL;DR

This paper introduces a generalized alternating projection algorithm for total variation minimization in compressive sensing, demonstrating high performance on images, hyperspectral data, and videos, and connecting it with ADMM frameworks.

Contribution

The paper proposes a novel GAP-based algorithm for TV minimization in compressive sensing and establishes its connection with ADMM methods for various imaging applications.

Findings

High-quality reconstruction of images, hyperspectral images, and videos.

Effective integration of GAP with ADMM frameworks.

Superior performance compared to existing methods.

Abstract

We consider the total variation (TV) minimization problem used for compressive sensing and solve it using the generalized alternating projection (GAP) algorithm. Extensive results demonstrate the high performance of proposed algorithm on compressive sensing, including two dimensional images, hyperspectral images and videos. We further derive the Alternating Direction Method of Multipliers (ADMM) framework with TV minimization for video and hyperspectral image compressive sensing under the CACTI and CASSI framework, respectively. Connections between GAP and ADMM are also provided.