There is active research targeting local image manipulations that can fool deep neural
networks (DNNs) into producing incorrect results. This paper examines a type of global …

Convolutional neural networks have become the most widely used tool for computer vision
applications like image classification, segmentation, object localization etc. Recent studies …

This paper focuses on correcting a camera image that has been improperly white-balanced.
This situation occurs when a camera's auto white balance fails or when the wrong manual …

In this paper, we present CLCC, a novel contrastive learning framework for color constancy.
Contrastive learning has been applied for learning high-quality visual representations for …

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 …

Deep neural networks for automatic image colorization often suffer from the color-bleeding
artifact, a problematic color spreading near the boundaries between adjacent objects. Such …

Color correction for underwater images has received increasing interest, due to its critical
role in facilitating available mature vision algorithms for underwater scenarios. Inspired by …

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) …

We present the Colorization Transformer, a novel approach for diverse high fidelity image
colorization based on self-attention. Given a grayscale image, the colorization proceeds in …

Cameras currently allow access to two image states:(i) a minimally processed linear raw-
RGB image state (ie, raw sensor data); or (ii) a highly-processed nonlinear image state (eg …