Computational color constancy refers to the problem of computing the
illuminant color so that the images of a scene under varying illumination can
be normalized to an image under the canonical illumination. In this paper, we
adopt a deep learning framework for the illumination estimation problem. The
proposed method works under the assumption of uniform illumination over the
scene and aims for the accurate illuminant color computation. Specifically, we
trained the convolutional neural network to solve the problem by casting the
color constancy problem as an illumination classification problem. We designed
the deep learning architecture so that the output of the network can be
directly used for computing the color of the illumination. Experimental results
show that our deep network is able to extract useful features for the
illumination estimation and our method outperforms all previous color constancy
methods on multiple test datasets.

本文采用深度学习框架来解决计算机颜色恒常性问题，使用卷积神经网络将颜色恒常性问题视为照明分类问题，设计深度学习架构以直接用于计算照明颜色，实验结果表明我们的深度网络能够提取有用特征用于照明估计，且在多个测试数据集上性能优于所有先前的颜色恒常方法。