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Universal Demosaicking of Color Filter Arrays

机译:彩色滤光片阵列的通用演示

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A large number of color filter arrays (CFAs), periodic or aperiodic, have been proposed. To reconstruct images from all different CFAs and compare their imaging quality, a universal demosaicking method is needed. This paper proposes a new universal demosaicking method based on inter-pixel chrominance capture and optimal demosaicking transformation. It skips the commonly used step to estimate the luminance component at each pixel, and thus, avoids the associated estimation error. Instead, we directly use the acquired CFA color intensity at each pixel as an input component. Two independent chrominance components are estimated at each pixel based on the inter-pixel chrominance in the window, which is captured with the difference of CFA color values between the pixel of interest and its neighbors. Two mechanisms are employed for the accurate estimation: distance-related and edge-sensing weighting to reflect the confidence levels of the inter-pixel chrominance components, and pseudoinverse-based estimation from the components in a window. Then from the acquired CFA color component and two estimated chrominance components, the three primary colors are reconstructed by a linear color transform, which is optimized for the least transform error. Our experiments show that the proposed method is much better than other published universal demosaicking methods.
机译:已经提出了许多周期性的或非周期性的滤色器阵列(CFA)。为了从所有不同的CFA重建图像并比较其成像质量,需要一种通用的去马赛克方法。提出了一种基于像素间色度捕获和最优去马赛克转换的通用去马赛克方法。它跳过了通常使用的步骤来估计每个像素处的亮度分量,因此避免了相关的估计误差。相反,我们直接将每个像素处获取的CFA颜色强度用作输入分量。根据窗口中的像素间色度,在每个像素处估计两个独立的色度分量,该色度分量是通过感兴趣的像素与其相邻像素之间CFA颜色值的差异捕获的。两种机制可用于精确估计:距离相关加权和边缘感知加权以反映像素间色度分量的置信度,以及从窗口中的分量基于伪逆的估计。然后从获取的CFA颜色分量和两个估计的色度分量中,通过线性颜色变换来重构这三种原色,并针对最小的变换误差对其进行了优化。我们的实验表明,所提出的方法比其他已发布的通用去马赛克方法要好得多。

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