This book covers image and video color editing research advances over the last two decades. Bringing readers up to speed on digital image and video editing techniques and research, the book delves into an area that has attracted much attention from researchers due to the rapid development of computer graphics and the widespread prevalence of digital cameras and mobile phones in daily life. Readers will get a comprehensive overview of the theory and application of color transfer, emotional color transfer, colorization, decolorization, and style transfer in altering still and moving digital images.
Despite the existence of professional image editing software that can complete complex image editing work, the skills required to achieve satisfactory editing results can be prohibitive, and even professional image editors need to spend a lot of time developing and maintaining aptitude in a niche tool. Instead, the book explores image and video editing techniques that are simple and effective alternatives to such editing software that professional and amateur image editors can utilize. The book focuses on color as one of the most important features of an image or video. Image and video color editing aims to dramatically alter source images suitable for a wide range of applications.
Author(s): Shiguang Liu
Series: Synthesis Lectures on Visual Computing: Computer Graphics, Animation, Computational Photography and Imaging
Publisher: Springer
Year: 2023
Language: English
Pages: 83
City: Cham
Preface
Contents
About the Author
1 Introduction
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1.1 Color Transfer
1.2 Colorization and Decolorization
1.3 Style Transfer
1.4 Enhancement
2 Color Transfer
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2.1 Image Color Transfer
2.1.1 Color Transfer Based on Statistical Information
2.1.2 Color Transfer Based on User Interaction
2.2 Hybrid Transfer
2.3 Video Color Transfer
2.4 Deep Learning-Based Color Transfer
3 Emotional Color Transfer
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3.1 Color Combination-Based Emotional Color Transfer
3.1.1 Emotion Transfer Based on Histogram
3.1.2 Emotion Transfer Based on Emotion Word
3.1.3 Emotion Transfer Based on Color Combination
3.2 Deep Learning-Based Emotional Color Transfer
4 Colorization
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4.1 Image Colorization
4.1.1 Semi-automatic Colorization
4.1.2 Automatic Colorization
4.2 Cartoon Colorization
4.3 Deep Colorization
4.4 Video Colorization
5 Decolorization
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5.1 Image Decolorization
5.1.1 Early Decolorization Methods
5.1.2 Local Decolorization Methods
5.1.3 Global Decolorization Methods
5.1.4 Deep Learning-Based Decolorization Methods
5.2 Video Decolorization
6 Style Transfer
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6.1 Image Style Transfer
6.1.1 Efficiency-Aware Style Transfer
6.1.2 Flexibility-Aware Style Transfer
6.1.3 Quality-Aware Style Transfer
6.2 Video Style Transfer
7 Low-Exposure Image and Video Enhancement
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7.1 Low-Exposure Image Enhancement
7.1.1 Histogram Equalization
7.1.2 Tone Mapping
7.1.3 Retinex Theory-Based Methods
7.1.4 Image Fusion
7.1.5 Deep Learning-Based Methods
7.2 Low-Exposure Video Enhancement
