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Bayer Image Demosaicking Using Eight-Directional Weights Based on the Gradient of Color Difference

机译:基于色差梯度的八维重量,拜耳图像去索探测

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摘要

In this paper, we propose a new demosaicking algorithm which uses eight-directional weights based on the gradient of color difference (EWGCD) for Bayer image demosaicking. To obtain the interpolation of green (G) pixels, the eight-directional G pixel values are first estimated in red (R)/blue (B) pixels. This estimate is used to calculate the color difference in R/B pixels of the Bayer image in diagonal directions. However, in horizontal and vertical directions, the new estimated G pixels are defined to obtain the color difference. The eight-directional weights of estimated G pixels can be obtained by considering the gradient of the color difference and the gradient of the RGB pixels of the Bayer image. Therefore, the eight-directional weighted values and the first estimated G pixel values are combined to obtain the full G image. Compared with six similar algorithms using the same eighteen McMaster images, the results of the experiment demonstrate that the proposed algorithm has a better performance not only in the subjective visual measurement but also in the assessments of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) index measurement.
机译:在本文中,我们提出了一种新的去迭算法,该算法利用八个方向权重,基于拜耳图像脱索的色差(EWGCD)的梯度。为了获得绿色(G)像素的插值,首先以红色(R)/蓝色(B)像素估计八个方向G像素值。该估计用于计算拜耳图像的R / B像素在对角线方向上的颜色差异。然而,在水平和垂直方向上,确定新的估计的G像素以获得颜色差异。可以通过考虑拜耳图像的RGB像素的梯度来获得估计的G像素的八个方向权重。因此,组合八个方向加权值和第一估计的G像素值以获得全G图像。与使用相同的十八McMaster映像的六种类似的算法相比,实验结果表明,该算法不仅在主观视觉测量中具有更好的性能,而且还具有峰值信噪比(PSNR)的评估中的性能更好结构相似性(SSIM)索引测量。

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