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Dangers of Demosaicing: Confusion From Correlation

机译:去马赛克的危险:相关性造成的混乱

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摘要

Images from colour sensors using Bayer filter arrays require demosaicing before viewing or further analysis. Advanced demosaicing methods use empirical knowledge of inter-channel correlations to reduce interpolation artefacts in the resulting images. These inter-channel correlations are however different for standard RGB cameras and hyperspectral imagers using colour sensors with added narrow-band spectral filtering.We study the effects of conventional demosaicing methods on hyperspectral images with a dataset originally collected without a colour filter array. We find that using advanced methods instead of bilinear interpolation results in an overall increase of 9-14% in absolute error and a decrease of 1-3% in PSNR, but also observed a decrease in MSE of 11-13%.For the corresponding RGB images, the advanced methods improved fidelity as expected. The results also demonstrate that the reconstruction methods that take advantage of correlation transport noise present in a single component to other reconstructed layers.
机译:使用拜耳滤镜阵列的颜色传感器中的图像需要进行去马赛克处理,然后才能查看或进一步分析。先进的去马赛克方法使用通道间相关性的经验知识来减少所得图像中的插值伪像。然而,这些通道间的相关性对于使用色彩传感器并添加了窄带光谱滤波的标准RGB相机和高光谱成像仪而言是不同的。我们发现使用高级方法而不是双线性插值可导致绝对误差总体增加9-14%,PSNR降低1-3%,但同时观察到MSE降低11-13%。 RGB图像是先进的方法,可以提高保真度。结果还表明,利用相关性将单个组件中存在的噪声传输到其他重构层的重构方法。

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