The dichromatic reflection model has been popularly exploited for computer vison tasks, such as color constancy and highlight removal. However, dichromatic model estimation is an severely ill-posed problem. Thus, several assumptions have been commonly made to estimate the dichromatic model, such as white-light (highlight removal) and the existence of highlight regions (color constancy). In this paper, we propose a spatio-temporal deep network to estimate the dichromatic parameters under AC light sources. The minute illumination variations can be captured with high-speed camera. The proposed network is composed of two sub-network branches. From high-speed video frames, each branch generates chromaticity and coefficient matrices, which correspond to the dichromatic image model. These two separate branches are jointly learned by spatio-temporal regularization. As far as we know, this is the first work that aims to estimate all dichromatic parameters in computer vision. To validate the model estimation accuracy, it is applied to color constancy and highlight removal. Both experimental results show that the dichromatic model can be estimated accurately via the proposed deep network.

中文翻译：

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交流光源下的深度二色模型估计


二色反射模型已广泛用于计算机视觉任务，例如颜色恒常性和高光去除。然而，二色模型估计是一个严重不适定的问题。因此，通常会做出几个假设来估计二色模型，例如白光（高光去除）和高光区域的存在（颜色恒定性）。在本文中，我们提出了一种时空深度网络来估计交流光源下的二色参数。微小的照明变化可以用高速相机捕捉到。所提出的网络由两个子网分支组成。每个分支从高速视频帧生成与二色图像模型相对应的色度和系数矩阵。这两个独立的分支是通过时空正则化共同学习的。据我们所知，这是第一个旨在估计计算机视觉中所有二色参数的工作。为了验证模型估计的准确性，将其应用于颜色恒定性和高光去除。两个实验结果都表明，可以通过所提出的深度网络准确估计二色模型。