Images have an ever-increasing presence in our daily lives. This increases the need for
accurate and efficient image processing. One of the first processing steps in modern …

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 study the importance of pretraining for the generalization capability in the
color constancy problem. We propose two novel approaches based on convolutional …

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 …

Computational colour constancy refers to the problem of obtaining reliable illuminant colour
so that the image can be rectified to generate a new image under the canonical illumination …

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

Color Constancy aims to correct image color casts caused by scene illumination. Recently,
although the deep learning approaches have remarkably improved on single-camera data …

In general, computational methods to estimate the color of the light source are based on
single, low-level image cues such as pixel values and edges. Only a few methods are …

In this paper, we provide a novel dataset designed for camera invariant color constancy
research. Camera invariance corresponds to the robustness of an algorithm's performance …

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 …