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Correction of complex purple fringing by green-channel compensation and local luminance adaptation

机译:通过绿色通道补偿和局部亮度自适应校正复杂的紫色条纹

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摘要

In natural photography, defects in camera imaging pipeline often result in some form of colour noise or distortion. Nature of this distortion is generally intertwined with scene dependent variables such as positioning, intensity and composition of light source and local object colour reflectivity. One such defect is called purple fringing aberration (PFA). PFA problems are of two types, one of which corresponds to a localised fringing effect near high contrast zones (termed as Isolated PFA or IS-PFA) and the second which corresponds to a widespread semi-transparent purple haze over a large part of natural scene (termed as complex PFA or C-PFA). Much of the PFA-correction solutions have been driven towards IS-PFA and very little towards C-PFA. Based on a premise that in C-PFA, green channel is heavily suppressed and noisy, while colour information in red and blue channels are largely conserved, authors propose a green-channel compensation algorithm for restoring true natural colours in fringe affected region. To correct white-tuft produced by proposed compensation algorithm, they also devise a suitable localised luminance adaptation procedure to equalise perceived changes in luminance profile. Comparisons with state-of-the-art methods devised to combat this purple haze effect yield promising results for a majority of test cases.
机译:在自然摄影中,相机成像管线中的缺陷通常会导致某种形式的颜色噪声或失真。这种失真的性质通常与场景相关的变量交织在一起,这些变量取决于场景的变量,例如光源的位置,强度和组成以及本地物体的颜色反射率。一种这样的缺陷称为紫色条纹像差(PFA)。 PFA问题有两种类型,一种对应于高对比度区域附近的局部边缘效应(称为孤立PFA或IS-PFA),另一种对应于大部分自然场景中广泛分布的半透明紫色雾(称为复杂PFA或C-PFA)。许多PFA校正解决方案已被推向IS-PFA,而很少被推向C-PFA。基于在C-PFA中,绿色通道被严重抑制和嘈杂的前提,而红色和蓝色通道中的颜色信息被大量保留,作者提出了一种绿色通道补偿算法,用于恢复边缘受影响区域的真实自然色彩。为了校正由提出的补偿算法产生的白簇,他们还设计了一种合适的局部亮度适应程序,以均衡亮度轮廓中的感知变化。与针对这种紫色雾霾效果而设计的最新方法的比较在大多数测试用例中产生了可喜的结果。

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