Color image compressive sensing reconstruction by using inter-channel correlation

摘要

This paper proposes a novel algorithm for compressive sensing (CS) reconstruction of color images. First of all, to better describe color image characteristics, we take inter-channel correlation into consideration and present two types of regularization, including inter-channel correlation-based nonlocal low-rank (ICNL) regularization and inter-channel correlation-based total variation (ICTV) regularization. Afterwards, both regularization terms are incorporated into the minimization problem, and an efficient algorithm is proposed to solve the joint formulation, by using a split-Bregman-based technique. To demonstrate the effectiveness of the proposed approach, four benchmark methods are compared, and the experiments are carried out on several color images with different subrates.