  Computational color constancy refers to the estimation of the scene
illumination and makes the perceived color relatively stable under varying
illumination. In the past few years, deep Convolutional Neural Networks (CNNs)
have delivered superior performance in illuminant estimation. Several
representative methods formulate it as a multi-label prediction problem by
learning the local appearance of image patches using CNNs. However, these
approaches inevitably make incorrect estimations for the ambiguous patches
affected by their neighborhood contexts. Inaccurate local estimates are likely
to bring in degraded performance when combining into a global prediction. To
address the above issues, we propose a contextual deep network for patch-based
illuminant estimation equipped with refinement. First, the contextual net with
a center-surround architecture extracts local contextual features from image
patches, and generates initial illuminant estimates and the corresponding color
corrected patches. The patches are sampled based on the observation that pixels
with large color differences describe the illumination well. Then, the
refinement net integrates the input patches with the corrected patches in
conjunction with the use of intermediate features to improve the performance.
To train such a network with numerous parameters, we propose a stage-wise
training strategy, in which the features and the predicted illuminant from
previous stages are provided to the next learning stage with more finer
estimates recovered. Experiments show that our approach obtains competitive
performance on two illuminant estimation benchmarks.
