Abstract
Improvements in color constancy have arisen from the
use of convolutional neural networks (CNNs). However, the
patch-based CNNs that exist for this problem are faced with
the issue of estimation ambiguity, where a patch may contain insufficient information to establish a unique or even a
limited possible range of illumination colors. Image patches with estimation ambiguity not only appear with great frequency in photographs, but also significantly degrade the
quality of network training and inference. To overcome
this problem, we present a fully convolutional network architecture in which patches throughout an image can carry different confidence weights according to the value they
provide for color constancy estimation. These confidence
weights are learned and applied within a novel pooling layer where the local estimates are merged into a global solution. With this formulation, the network is able to determine “what to learn” and “how to pool” automatically from color constancy datasets without additional supervision. The proposed network also allows for end-to-end
training, and achieves higher efficiency and accuracy. On standard benchmarks, our network outperforms the previous
state-of-the-art while achieving 120× greater efficiency.