Outdoor daytime multi - illuminant color constancy

Abstract

White-balancing is an important part of the image processing pipeline and is used in many computer vision applications. It removes the chromatic influence of the illumination on objects in the scene. White balancing is important in tasks such as object detection and object tracking. This problem is tackled in a myriad of ways, but most methods use the assumption that images contain only one dominant uniform illuminant. In recent years, neural networks have been used to create state-of-the-art methods for single illuminant white-balancing, but the problem of multi-illuminant white-balancing has been largely ignored. The main reason for this is the lack of multi-illuminant datasets. In this paper, we introduce a convolutional neural network for multi-illuminant (sun and shadow) illumination estimation. For the training and testing of the created model over 100 outdoor daytime images were taken using the Canon EOS 550D camera. We show that the model outperforms existing statistics-based methods on the test data.