A Survey of Color Mapping and its Applications
Hasan Sheikh Faridul,Tania Pouli,Christel Chamaret,Jürgen Stauder,Alain Trémeau,Erik Reinhard +5 more
- pp 43-67
TLDR
A comprehensive overview of color mapping or color transfer methods is presented and a classification of current solutions depending not only on their algorithmic formulation but also their range of applications is offered.Abstract:
Color mapping or color transfer methods aim to recolor a given image or video by deriving a mapping between that image and another image serving as a reference. This class of methods has received considerable attention in recent years, both in academic literature and in industrial applications. Methods for recoloring images have often appeared under the labels of color correction, color transfer or color balancing, to name a few, but their goal is always the same: mapping the colors of one image to another. In this report, we present a comprehensive overview of these methods and offer a classification of current solutions depending not only on their algorithmic formulation but also their range of applications. We discuss the relative merit of each class of techniques through examples and show how color mapping solutions can and have been applied to a diverse range of problems.read more
Citations
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Proceedings ArticleDOI
BeautyGAN: Instance-level Facial Makeup Transfer with Deep Generative Adversarial Network
TL;DR: A dual input/output Generative Adversarial Network that enables the network to learn translation on instance-level through unsupervised adversarial learning, and could generate visually pleasant makeup faces and accurate transferring results.
Proceedings ArticleDOI
Distort-and-Recover: Color Enhancement Using Deep Reinforcement Learning
TL;DR: In this article, the authors cast the color enhancement process as a Markov Decision Process where actions are defined as global color adjustment operations and train an agent to learn the optimal global enhancement sequence of the actions.
Proceedings ArticleDOI
Filmy Cloud Removal on Satellite Imagery with Multispectral Conditional Generative Adversarial Nets
Kenji Enomoto,Ken Sakurada,Weimin Wang,Hiroshi Fukui,Masashi Matsuoka,Ryosuke Nakamura,Nobuo Kawaguchi +6 more
TL;DR: This paper proposes a network that can remove clouds and generate visible light images from the multispectral images taken as inputs and utilizes the t- Distributed Stochastic Neighbor Embedding (t-SNE) to improve the problem of bias in the training dataset.
Proceedings ArticleDOI
Aesthetic-Driven Image Enhancement by Adversarial Learning
TL;DR: In this article, an adversarial learning based model that performs automatic image enhancement is proposed, which only requires weak supervision (binary labels on image aesthetic quality) and can learn enhancement operators for the task of aesthetic-based image enhancement.
Journal ArticleDOI
Colour Mapping: A Review of Recent Methods, Extensions and Applications
H. Sheikh Faridul,Tania Pouli,Christel Chamaret,Jürgen Stauder,Erik Reinhard,Dmitry Kuzovkin,Alain Trémeau +6 more
TL;DR: A comprehensive overview of colour mapping or colour transfer methods is presented and a classification of current solutions depending not only on their algorithmic formulation but also their range of applications is offered.
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