Image and video enhancement such as color constancy, low light enhancement, and tone
mapping on smartphones is challenging because high-quality images should be achieved …

Image harmonization, aiming to make composite images look more realistic, is an important
and challenging task. The composite, synthesized by combining foreground from one image …

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) …

While modern deep neural networks (DNNs) achieve state-of-the-art results for illuminant
estimation, it is currently necessary to train a separate DNN for each type of camera sensor …

We present here a method for computational color constancy in which a deep convolutional
neural network is trained to detect achromatic pixels in color images after they have been …

Color constancy is an important process in camera pipeline to remove the color bias of
captured image caused by scene illumination. Recently, significant improvements in color …

Abstract We present" Cross-Camera Convolutional Color Constancy"(C5), a learning-based
method, trained on images from multiple cameras, that accurately estimates a scene's …

Auto white balance (AWB) is applied by camera hardware at capture time to remove the
color cast caused by the scene illumination. The vast majority of white-balance algorithms …

In this paper, we describe a new large dataset for illumination estimation. This dataset,
called INTEL-TAU, contains 7022 images in total, which makes it the largest available high …

Contemporary approaches frame the color constancy problem as learning camera specific
illuminant mappings. While high accuracy can be achieved on camera specific data, these …