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.
|Number of pages||12|
|Publication status||Published - 2017 Jan 1|
Bibliographical noteFunding Information:
This work was supported in part by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (B0101-16-0552, Development of Predictive Visual Intelligence Technology) and the Culture Technology (CT) Research & Development Program 2015 funded by the Ministry of Culture, Sports and Tourism (MCST) and Korea Creative Content Agency (KOCCA) (R2015040004).
© 2016 Elsevier Ltd
All Science Journal Classification (ASJC) codes
- Signal Processing
- Computer Vision and Pattern Recognition
- Artificial Intelligence