Regressing the illumination of a scene from the representations of object appearances is
popularly adopted in computational color constancy. However, it's still challenging due to …

Color Constancy aims to correct image color casts caused by scene illumination. Recently,
although the deep learning approaches have remarkably improved on single-camera data …

Contemporary approaches frame the color constancy problem as learning camera specific
illuminant mappings. While high accuracy can be achieved on camera specific data, these …

In this study, a novel illuminant color estimation framework is proposed for computational
color constancy, which incorporates the high representational capacity of deep-learning …

In this paper, we propose a novel color constancy approach, called Bag of Color Features
(BoCF), building upon Bag-of-Features pooling. The proposed method substantially reduces …

In this paper, we study the importance of pretraining for the generalization capability in the
color constancy problem. We propose two novel approaches based on convolutional …

Illuminant estimation to achieve color constancy is an ill-posed problem. Searching the large
hypothesis space for an accurate illuminant estimation is hard due to the ambiguities of …

Abstract We present" Cross-Camera Convolutional Color Constancy"(C5), a learning-based
method, trained on images from multiple cameras, that accurately estimates a scene's …

In this paper, we provide a novel data set designed for Camera-independent color
constancy research. Camera independence corresponds to the robustness of an algorithm's …

Previous deep learning approaches to color constancy usually directly estimate illuminant
value from input image. Such approaches might suffer heavily from being sensitive to the …