Deep Structured-Output Regression Learning for Computational Color Constancy
This addresses the fundamental computer vision problem of color constancy for image processing applications, but it is incremental as it builds on existing CNN methods.
The paper tackles the problem of computational color constancy by estimating illuminant colors from images, introducing a deep structured-output regression framework that automatically learns features and captures output correlations, achieving competitive performance on two public benchmarks.
Computational color constancy that requires esti- mation of illuminant colors of images is a fundamental yet active problem in computer vision, which can be formulated into a regression problem. To learn a robust regressor for color constancy, obtaining meaningful imagery features and capturing latent correlations across output variables play a vital role. In this work, we introduce a novel deep structured-output regression learning framework to achieve both goals simultaneously. By borrowing the power of deep convolutional neural networks (CNN) originally designed for visual recognition, the proposed framework can automatically discover strong features for white balancing over different illumination conditions and learn a multi-output regressor beyond underlying relationships between features and targets to find the complex interdependence of dif- ferent dimensions of target variables. Experiments on two public benchmarks demonstrate that our method achieves competitive performance in comparison with the state-of-the-art approaches.