Computational color constancy is a fundamental prerequisite for many computer vision
applications. This paper presents a survey of many recent developments and state-of-the-art …

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 …

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 …

Color constancy is a well-studied topic in color vision. Methods are generally categorized as
(1) low-level statistical methods,(2) gamut-based methods, and (3) learning-based methods …

In this paper, we present a three-stage method for the estimation of the color of the illuminant
in RAW images. The first stage uses a convolutional neural network that has been specially …

Edge-based color constancy methods make use of image derivatives to estimate the
illuminant. However, different edge types exist in real-world images, such as material …

Illumination estimation is the process of determining the chromaticity of the illumination in an
imaged scene in order to remove undesirable color casts through white-balancing. While …

Illuminant estimation is a key step for computational color constancy. Instead of using the
grey world or grey edge assumptions, we propose in this paper a novel method for …

Exemplar-based learning or, equally, nearest neighbor methods have recently gained
interest from researchers in a variety of computer science domains because of the …

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 …