Compressive large-scale image sensing

Wei-Jie Liang, Gang-Xuan Lin, Chun-Shien Lu

Abstract: Cost-efficient compressive sensing of large-scale images with fast reconstructed high-quality results is very challenging. In this paper, we propose a new compressive large-scale image sensing method, composed of operator-based strategy in the context of fixed point continuation technique 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 computational complexity and convergence analyses, extensive simulations, and comparisons with state-of-the-art algorithms.