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首页> 外文期刊>Signal Processing. Image Communication: A Publication of the the European Association for Signal Processing >Color image demosaicking using inter-channel correlation and nonlocal self-similarity
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Color image demosaicking using inter-channel correlation and nonlocal self-similarity

机译：使用通道间相关性和非局部自相似性的彩色图像去马赛克

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摘要

Color demosaicking is used to reconstruct full color images from incomplete color filter array samples captured by cameras with a single sensor array. In reconstructing natural-looking images, one key challenge is to model and respect the statistics of natural images. This paper presents a novel modeling strategy and an efficient color demosaicking algorithm. The approach starts with joint modeling of the color images, which supports simultaneous representation of inter-channel correlation and structural information in an image. The inter-channel correlation is explored by measuring the channel difference signals in the gradient domain, while the structural information is explored by nonlocal low-rank regularization. An efficient algorithm is then proposed to solve the joint formulation, by dividing the minimization problem into two sub-problems and solving them iteratively. The effectiveness of the proposed approach is demonstrated with extensive experiments on both noiseless and noisy datasets, with comparison with existing state-of-the-arts color demosaicking methods. (C) 2015 Elsevier B.V. All rights reserved.
机译：彩色去马赛克用于从具有单个传感器阵列的相机捕获的不完整滤色器阵列样本中重建全彩色图像。在重建看起来自然的图像时，关键的挑战是建模和尊重自然图像的统计信息。本文提出了一种新颖的建模策略和一种有效的色彩去马赛克算法。该方法从彩色图像的联合建模开始，该模型支持在图像中同时表示通道间相关性和结构信息。通过在梯度域中测量通道差异信号来探索通道间相关性，而通过非局部低秩正则化来探索结构信息。通过将最小化问题分为两个子问题并迭代求解，提出了一种有效的算法来求解联合公式。通过与无噪声和高噪声数据集进行广泛的实验，并与现有的最新彩色去马赛克方法进行比较，证明了该方法的有效性。 （C）2015 Elsevier B.V.保留所有权利。

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