As-projective-as-possible bias correction for illumination estimation algorithms.
As-projective-as-possible bias correction for illumination estimation algorithms.
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DOI：
10.1364/josaa.36.000071
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发表时间：
2018-12
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影响因子：
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通讯作者：
M. Afifi;Abhijith Punnappurath;G. Finlayson;Michael S. Brown
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文献类型：
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作者：
M. Afifi;Abhijith Punnappurath;G. Finlayson;Michael S. Brown
照度估计是相机板载自动白平衡（AWB）功能中的关键程序。照明估计算法以传感器原始rgb色彩空间中的R， G， B向量的形式从图像中估计场景照明的颜色。虽然基于学习的方法在照明估计方面表现出色，但相机仍然依赖于简单的基于统计的算法，这些算法的准确性较低，但能够在相机硬件上快速执行。提高这些基于统计的快速算法的准确性的有效策略是应用估计后的偏差校正函数来变换估计的R， G， B向量，使其更接近正确的解。Finlayson [Interface Focus8, 20180008 (2018)2042-889810.1098/rsfs.2018.0008]最近的工作表明，由于R， G， B照明向量的大小与AWB过程无关，因此偏差校正函数可以表示为射影变换。本文建立在这一发现的基础上，并表明通过使用尽可能射影（APAP）射影变换可以得到进一步的改进，该射影变换局部适应于输入R， G， B向量。我们证明了所提出的APAP偏差校正在几种著名的统计照明估计方法上的有效性。我们还描述了一种快速查找方法，该方法允许仅通过少量查找操作来执行APAP转换。
Illumination estimation is the key routine in a camera's onboard auto-white-balance (AWB) function. Illumination estimation algorithms estimate the color of the scene's illumination from an image in the form of an R, G, B vector in the sensor's raw-RGB color space. While learning-based methods have demonstrated impressive performance for illumination estimation, cameras still rely on simple statistical-based algorithms that are less accurate but capable of executing quickly on the camera's hardware. An effective strategy to improve the accuracy of these fast statistical-based algorithms is to apply a post-estimate bias-correction function to transform the estimated R, G, B vector such that it lies closer to the correct solution. Recent work by Finlayson [Interface Focus8, 20180008 (2018)2042-889810.1098/rsfs.2018.0008] showed that a bias-correction function can be formulated as a projective transform because the magnitude of the R, G, B illumination vector does not matter to the AWB procedure. This paper builds on this finding and shows that further improvements can be obtained by using an as-projective-as-possible (APAP) projective transform that locally adapts the projective transform to the input R, G, B vector. We demonstrate the effectiveness of the proposed APAP bias correction on several well-known statistical illumination estimation methods. We also describe a fast lookup method that allows the APAP transform to be performed with only a few lookup operations.