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Color image compressive sensing reconstruction by using inter-channel correlation

机译:使用通道间相关性彩色图像压缩感应重建

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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.
机译:本文提出了一种用于彩色图像的压缩感测(CS)重建的新颖算法。首先,为了更好地描述彩色图像特征,我们考虑频道间相关性并呈现两种类型的正则化,包括基于间相关的基于间相关的非本地低秩(ICNL)正则化和基于间的信道相关的总变化(ICTV)正规化。之后,将两种正则化术语结合到最小化问题中,并且提出了一种有效的算法来解决关节配方,通过使用基于分裂的基于分裂的技术。为了证明所提出的方法的有效性,比较了四种基准方法,并在具有不同序列的几种彩色图像上进行实验。

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