The aim of colour constancy is to discount the effect of the scene illumination from the image colours and restore the colours of the objects as captured under a 'white' illuminant. For the majority of colour constancy methods, the first step is to estimate the scene illuminant colour. Generally, it is assumed that the illumination is uniform in the scene. However, real world scenes have multiple illuminants, like sunlight and spot lights all together in one scene. We present in this paper a simple yet very effective framework using a deep CNN-based method to estimate and use multiple illuminants for colour constancy. Our approach works well in both the multi and single illuminant cases. The output of the CNN method is a region-wise estimate map of the scene which is smoothed and divided out from the image to perform colour constancy. The method that we propose outperforms other recent and state of the art methods and has promising visual results.
翻译:彩色凝聚的目的是将场景光照从图像颜色中减去,并恢复在“白”光照下捕获的物体的颜色。 对于大多数彩色凝聚方法而言,第一步是估计场景光亮度。 一般来说, 假设场景中的光度是统一的。 然而, 真实的世界场景有多种光度, 如阳光和点光, 在一个场景中一齐存在。 我们在本文件中提出了一个简单而非常有效的框架, 使用深厚的CNN 方法来估计和使用多光度的颜色。 我们的方法在多光度和单一光度的案例中都很有效。 CNN 方法的输出是场景的区域性估计图, 平滑, 从图像中分离出来, 进行彩色凝聚。 我们提出的方法超越了其他最新和最新艺术方法, 并且有希望的视觉结果 。