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 …

In this paper, we present CLCC, a novel contrastive learning framework for color constancy.
Contrastive learning has been applied for learning high-quality visual representations for …

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 …

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 …

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 …

Most digital camera pipelines use color constancy methods to reduce the influence of
illumination and camera sensor on the colors of scene objects. The highest accuracy of color …

In this work we describe a Convolutional Neural Network (CNN) to accurately predict the
scene illumination. Taking image patches as input, the CNN works in the spatial domain …

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

We present here a method for computational color constancy in which a deep convolutional
neural network is trained to detect achromatic pixels in color images after they have been …

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 …