As the color filter array (CFA)2.0, the RGBW CFA pattern, in which each CFA pixel contains only one R, G, B, or W color value, provides more luminance information than the Bayer CFA pattern. Demosaicking RGBW CFA images $I^{RGBW}$ is necessary in order to provide high-quality RGB full-color images as the target images for human perception. In this letter, we propose a three-stage demosaicking method for $I^{RGBW}$ . In the first-stage, a cross shape-based color difference approach is proposed in order to interpolate the missing W color pixels in the W color plane of $I^{RGBW}$ . In the second stage, an iterative error compensation-based demosaicking process is proposed to improve the quality of the demosaiced RGB full-color image. In the third stage, taking the input image $I^{RGBW}$ as the ground truth RGBW CFA image, an $I^{RGBW}$ -based refinement process is proposed to refine the quality of the demosaiced image obtained by the second stage. Based on the testing RGBW images that were collected from the Kodak and IMAX datasets, the comprehensive experimental results illustrated that the proposed three-stage demosaicking method achieves substantial quality and perceptual effect improvement relative to the previous method by Hamilton and Compton and the two state-of-the-art methods, Kwan et al.’s pansharpening-based method, and Kwan and Chou’s deep learning-based method.

中文翻译：

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使用基于迭代误差补偿的方法对 RGBW CFA 图像进行有效的三阶段去马赛克方法。


作为滤色器阵列（CFA）2.0，RGBW CFA图案（其中每个CFA像素仅包含一个R、G、B或W颜色值）比Bayer CFA图案提供更多亮度信息。对 RGBW CFA 图像进行去马赛克${我}^{右G乙瓦}$为了提供高质量的RGB全彩图像作为人类感知的目标图像是必要的。在这封信中，我们提出了一种三阶段去马赛克方法${我}^{右G乙瓦}$。在第一阶段，提出了一种基于十字形状的色差方法，以便对W颜色平面中缺失的W颜色像素进行插值。${我}^{右G乙瓦}$。在第二阶段，提出了一种基于迭代误差补偿的去马赛克过程，以提高去马赛克RGB全彩图像的质量。第三阶段，获取输入图像${我}^{右G乙瓦}$作为地面实况 RGBW CFA 图像，${我}^{右G乙瓦}$提出了基于细化过程来细化第二阶段获得的去马赛克图像的质量。 基于从 Kodak 和 IMAX 数据集收集的测试 RGBW 图像，综合实验结果表明，所提出的三阶段去马赛克方法相对于 Hamilton 和 Compton 之前的方法以及两种状态，在质量和感知效果上取得了实质性的改进。最先进的方法，Kwan等人。的基于全色锐化的方法，以及 Kwan 和 Chou 的基于深度学习的方法。