Color constancy is an important process in camera pipeline to remove the color bias of
captured image caused by scene illumination. Recently, significant improvements in color …

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

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 …

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

Computational color constancy aims to estimate the color of the light source. The
performance of many vision tasks, such as object detection and scene understanding, may …

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 …

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 …

Color constancy is an essential function in digital photography and a fundamental process
for many computer vision applications. Accordingly, many methods have been proposed …

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