Underwater computer vision has attracted increasing attention in the research community
due to the recent advances in underwater platforms such as of rovers, gliders, autonomous …

Despite extensive research conducted in machine vision for harvesting robots, practical
success in this field of agrobotics is still limited. This article presents a comprehensive …

Camera sensors often fail to capture clear images or videos in a poorly lit environment. In
this paper, we propose a trainable hybrid network to enhance the visibility of such degraded …

Retinex theory is developed mainly to decompose an image into the illumination and
reflectance components by analyzing local image derivatives. In this theory, larger …

Abstract The 3D Lookup Table (3D LUT) is a highly-efficient tool for real-time image
enhancement tasks, which models a non-linear 3D color transform by sparsely sampling it …

We propose a joint intrinsic-extrinsic prior model to estimate both illumination and
reflectance from an observed image. The 2D image formed from 3D object in the scene is …

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 …

Adjusting photographs to obtain compelling renditions requires skill and time. Even contrast
and brightness adjustments are challenging because they require taking into account the …

We introduce a deep learning approach to realistically edit an sRGB image's white balance.
Cameras capture sensor images that are rendered by their integrated signal processor (ISP) …

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 …