Compressive Sensing of Large-Scale Images: An Assumption-Free Approach

TL;DR

This paper introduces an assumption-free large-scale image compressive sensing method that combines operator-based strategies with weighted LASSO and tree-structured sparsity, enabling efficient and high-quality reconstruction.

Contribution

It presents a novel, assumption-free approach for large-scale image compressive sensing using fixed point continuation and weighted LASSO with tree sparsity.

Findings

Verified feasibility through simulations

Outperforms state-of-the-art algorithms

Achieves fast, high-quality reconstruction

Abstract

Cost-efficient compressive sensing of big media data with fast reconstructed high-quality results is very challenging. In this paper, we propose a new large-scale image compressive sensing method, composed of operator-based strategy in the context of fixed point continuation method and weighted LASSO with tree structure sparsity pattern. The main characteristic of our method is free from any assumptions and restrictions. The feasibility of our method is verified via simulations and comparisons with state-of-the-art algorithms.