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 …

Deep Neural Networks (DNNs) have been widely used for illumination estimation, which is
time-consuming and requires sensor-specific data collection. Our proposed method uses a …

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

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 (CC) refers to the ability to reproduce the veritable color of objects by
eliminating the influence of environmental illumination. The performance of CC has been …

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 …

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

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 …

Color constancy is one of the fundamental tasks in computer vision. Many supervised
methods, including recently proposed Convolutional Neural Networks (CNN)-based …

A learning-based multibranch deep probability network is proposed to estimate the
illuminated color of the light source in a scene, commonly referred to as color constancy. The …