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 …

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 …

In the human and computer vision, color constancy is the ability to perceive the true color of
objects in spite of changing illumination conditions. Color constancy is remarkably …

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 …

Computational color constancy refers to the problem of computing the illuminant color so
that the images of a scene under varying illumination can be normalized to an image under …

Color constancy is an important ability of the human visual system to perceive constant
colors across different illumination. In this paper, we study a more practical yet challenging …

Color is a crucial visual cue readily exploited by Convolutional Neural Networks (CNNs) for
object recognition. However, CNNs struggle if there is data imbalance between color …

Achieving color constancy is an important part of image preprocessing pipeline of
contemporary digital cameras. Its goal is to eliminate the influence of the illumination color …

Color constancy methods are generally based on a simplifying assumption that the spectral
distribution of a light source is uniform across scenes. However, in reality, this assumption is …

For more than a decade, both academia and industry have focused attention on the
computer vision and in particular the computational color constancy (CVCC). The CVCC is …