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A Geometric Method for Optimal Design of Color Filter Arrays

机译:彩色滤光片阵列优化设计的几何方法

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A color filter array (CFA) used in a digital camera is a mosaic of spectrally selective filters, which allows only one color component to be sensed at each pixel. The missing two components of each pixel have to be estimated by methods known as demosaicking. The demosaicking algorithm and the CFA design are crucial for the quality of the output images. In this paper, we present a CFA design methodology in the frequency domain. The frequency structure, which is shown to be just the symbolic DFT of the CFA pattern (one period of the CFA), is introduced to represent images sampled with any rectangular CFAs in the frequency domain. Based on the frequency structure, the CFA design involves the solution of a constrained optimization problem that aims at minimizing the demosaicking error. To decrease the number of parameters and speed up the parameter searching, the optimization problem is reformulated as the selection of geometric points on the boundary of a convex polygon or the surface of a convex polyhedron. Using our methodology, several new CFA patterns are found, which outperform the currently commercialized and published ones. Experiments demonstrate the effectiveness of our CFA design methodology and the superiority of our new CFA patterns.
机译:数码相机中使用的滤色镜阵列(CFA)是光谱选择滤镜的镶嵌图,它只能在每个像素处感应一种颜色分量。必须通过称为去马赛克的方法来估计每个像素丢失的两个分量。去马赛克算法和CFA设计对于输出图像的质量至关重要。在本文中,我们提出了频域中的CFA设计方法。引入的频率结构只是CFA模式的符号DFT(CFA的一个周期),用于表示在频域中使用任何矩形CFA采样的图像。基于频率结构,CFA设计涉及旨在最小化去马赛克误差的约束优化问题的解决方案。为了减少参数数量并加快参数搜索速度,将优化问题重新设计为选择凸多边形边界或凸多面体表面上的几何点。使用我们的方法,发现了几种新的CFA模式,它们优于目前商业化和已发布的CFA模式。实验证明了我们的CFA设计方法的有效性以及新的CFA模式的优越性。

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