IEEE Trans Image Process. 2017 Sep;26(9):4347-4362. doi: 10.1109/TIP.2017.2713044. Epub 2017 Jun 7.
In this paper, we present a three-stage method for the estimation of the color of the illuminant in RAW images. The first stage uses a convolutional neural network that has been specially designed to produce multiple local estimates of the illuminant. The second stage, given the local estimates, determines the number of illuminants in the scene. Finally, local illuminant estimates are refined by non-linear local aggregation, resulting in a global estimate in case of single illuminant. An extensive comparison with both local and global illuminant estimation methods in the state of the art, on standard data sets with single and multiple illuminants, proves the effectiveness of our method.
在本文中，我们提出了一种用于估计 RAW 图像中光源颜色的三阶段方法。第一阶段使用了一个专门设计的卷积神经网络，该网络可以生成多个光源的局部估计值。第二阶段根据局部估计值确定场景中的光源数量。最后，通过非线性局部聚合对局部光源估计值进行细化，从而在存在单个光源的情况下得到全局估计值。在具有单个和多个光源的标准数据集上，与现有技术中的局部和全局光源估计方法进行了广泛比较，证明了我们方法的有效性。
