Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei 10672, Taiwan.
Sensors (Basel). 2020 Jul 14;20(14):3908. doi: 10.3390/s20143908.
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 R G B W 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 R G B W . In the first-stage, a cross shape-based color difference approach is proposed in order to interpolate the missing color pixels in the color plane of I R G B W . 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 R G B W as the ground truth RGBW CFA image, an I R G B W -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 's pansharpening-based method, and Kwan and Chou's deep learning-based method.
作为彩色滤光片阵列（CFA）2.0，RGBW CFA 模式中每个 CFA 像素仅包含一个 R、G、B 或 W 颜色值，比拜耳 CFA 模式提供了更多的亮度信息。为了提供高质量的 RGB 全彩色图像作为人类感知的目标图像，需要对 RGBW CFA 图像进行 I R G B W 去马赛克处理。在这封信中，我们提出了一种用于 I R G B W 的三阶段去马赛克方法。在第一阶段，提出了一种基于十字形的色差方法，以便在 I R G B W 的颜色平面中插值缺失的颜色像素。在第二阶段，提出了一种基于迭代误差补偿的去马赛克处理过程，以提高去马赛克 RGB 全彩色图像的质量。在第三阶段，以输入图像 I R G B W 作为 RGBW CFA 图像的真实值，提出了一种基于 I R G B W 的细化过程，以细化第二阶段得到的去马赛克图像的质量。基于从柯达和 IMAX 数据集收集的测试 RGBW 图像，综合实验结果表明，与 Hamilton 和 Compton 以及两种最先进的方法（Kwan 的 pansharpening 方法和 Kwan 和 Chou 的基于深度学习的方法）提出的三阶段去马赛克方法相比，该方法在质量和感知效果方面都有了显著的提高。
