Auto white balance (AWB) is applied by camera hardware at capture time to remove the color cast caused by the scene illumination. The vast majority of white-balance algorithms assume a single light source illuminates the scene; however, real scenes often have mixed lighting conditions. This paper presents an effective AWB method to deal with such mixed-illuminant scenes. A unique departure from conventional AWB, our method does not require illuminant estimation, as is the case in traditional camera AWB modules. Instead, our method proposes to render the captured scene with a small set of predefined white-balance settings. Given this set of rendered images, our method learns to estimate weighting maps that are used to blend the rendered images to generate the final corrected image. Through extensive experiments, we show this proposed method produces promising results compared to other alternatives for single- and mixed-illuminant scene color correction. Our source code and trained models are available at https://github.com/mahmoudnafifi/mixedillWB.
翻译:相机硬件在捕捉时应用自动白平衡(AWB)来去除现场光照造成的颜色。 绝大多数白平衡算法假设单一光源照亮现场; 然而, 真实场景往往有混合的照明条件。 本文展示了一种有效的AWB处理这种混合光化场景的方法。 与常规的AWB不同, 我们的方法不需要光化估计, 传统相机 AWB 模块的情况就是如此。 相反, 我们的方法是用一套小的预设白平衡设置来将所捕捉的场景变成一套小的。 鉴于这组图像, 我们的方法学会了估算加权图, 用来混合所拍摄的图像以生成最后的校正图像。 我们通过广泛的实验展示了这一拟议方法, 与其他单一和混合光化场景颜色校正的替代方法相比, 产生了有希望的结果。 我们的来源代码和经过培训的模型可以在 https://github.com/mahoudnafififi/ mixedibbb 上查阅。