For the imaging signal processing (ISP) pipeline of digital image devices, it is of high
significance to remove undesirable illuminant effects and obtain color invariance, commonly …

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 …

Recently, Convolutional Neural Networks (CNNs) have been widely used to solve the
illuminant estimation problem and have often led to state-of-the-art results. Standard …

Illuminant estimation is critically important in computational color constancy, which has
attracted great attentions and motivated the development of various statistical-and learning …

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 capturing images, cameras often encounter color-cast problems owing to variations in the
illumination of different scenes. Existing algorithms attempt to estimate the illuminant …

Color constancy is one of the key steps in the process of image formation in digital cameras.
Its goal is to process the image so that there is no influence of illumination color on the …

Computational color constancy refers to the problem of estimating the color of the scene
illumination in a color image, followed by color correction of the image through a white …

To achieve computer vision color constancy (CVCC), it is vital but challenging to estimate
scene illumination from a digital image, which distorts the true color of an object. Estimating …

Deep neural networks (DNNs) have been widely used for illuminant estimation, which
commonly requires great efforts to collect sensor-specific data. In this paper, we propose a …