Color constancy via multibranch deep probability network

Fei Wang; Wei Wang; Zhiliang Qiu; Jianwu Fang; Jianru Xue; Jingru Zhang

doi:10.1117/1.JEI.27.4.043010

10 July 2018 Color constancy via multibranch deep probability network

Fei Wang, Wei Wang, Zhiliang Qiu, Jianwu Fang, Jianru Xue, Jingru Zhang

Author Affiliations +

Journal of Electronic Imaging, Vol. 27, Issue 4, 043010 (July 2018). https://doi.org/10.1117/1.JEI.27.4.043010

Abstract

A learning-based multibranch deep probability network is proposed to estimate the illuminated color of the light source in a scene, commonly referred to as color constancy. The method consists of two coupled subnetworks, which are the deep multibranch illumination estimating network (DMBEN) and deep probability computing network (DPN). The one branch of DMBEN estimates the global illuminant through pooling layer and fully connected layer, whereas the other branch is built as an end-to-end residuals network (Res-net) to evaluate the local illumination. The other adjoint subnetwork DPN separately computes the probabilities that results of DMBEN are similar to the ground truth, then determines the better estimation according to the two probabilities under a new criterion. The results of extensive experiments on Color Checker and NUS 8-Camera datasets show that the proposed approach is superior to the state-of-the-art methods both in efficiency and effectiveness.

Citation Download Citation

Fei Wang, Wei Wang, Zhiliang Qiu, Jianwu Fang, Jianru Xue, and Jingru Zhang "Color constancy via multibranch deep probability network," Journal of Electronic Imaging 27(4), 043010 (10 July 2018). https://doi.org/10.1117/1.JEI.27.4.043010

Received: 19 January 2018; Accepted: 22 June 2018; Published: 10 July 2018

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