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Color Image and Video Enhancement 无水印pdf.pdf
Cover
Preface
Contents
Contributors
1 Colorimetric Characterization
1.1 Introduction
1.2 CIE XYZ
1.3 sRGB
1.4 Color Correction
1.5 Theoretical Relationship between RGB and XYZ
1.6 Data-Based Color-Correction Methods
1.7 Applications and Limitations
1.8 Conclusions
References
2 Image Demosaicing
2.1 Introduction
2.1.1 Demosaicing
2.1.2 Demosaicing Artifacts
2.1.3 Demosaicing Principles
2.1.4 Evaluation Criteria
2.2 Demosaicing Approaches
2.2.1 Edge-Sensitive Methods
2.2.2 Directional Interpolation and Decision Methods
2.2.3 Frequency-Domain Approaches
2.2.4 Wavelet-Based Methods
2.2.5 Statistical Reconstruction Techniques
2.2.6 Experimental Results
2.3 Advanced Topics
2.3.1 Joint Demosaicing and Deblurring
2.3.2 Joint Demosaicing and Super-Resolution
2.4 Conclusion
References
3 DCT-Based Color Image Denoising: Efficiency Analysis and Prediction
3.1 Introduction
3.2 Image and Noise Properties
3.2.1 Image Properties
3.2.2 Noise Models
3.3 Considered Filters and Quantitative Criteria
3.4 Component-Wise Denoising and Prediction
3.5 3D Denoising of Color Images
3.6 Conclusions
References
4 Impulsive Noise Filters for Colour Images
4.1 Modelling Impulsive Noise
4.1.1 Measuring the Quality of a Filter
4.2 Robust Filters For Impulsive Noise
4.2.1 Comparison of Robust Methods
4.3 Adaptive Filtering
4.3.1 Partition Based Filters
4.3.2 Switching Filters
4.3.2.1 Based on Robust Statistics
4.3.2.2 Based on Peer Groups
4.3.2.3 Using Fuzzy Metrics
4.3.2.4 Using Quaternions
4.3.2.5 Noise Detection in Several Steps
4.3.2.6 Based on Morphological Operations
4.3.3 Filters Based on Weighted Coefficients
4.3.4 Fuzzy Filters
4.3.5 Regularization Filters
4.3.6 Comparison of Advanced Adaptive Methods
References
5 Spatial and Frequency-Based Variational Methods for Perceptually Inspired Color and Contrast Enhancement of Digital Images
5.1 Introduction
5.2 Variational Interpretation of Histogram Equalization
5.3 A Basic Set of HVS Properties
5.3.1 Adaptation to the Average Luminance Level
5.3.2 Local Contrast Enhancement
5.3.3 Color Constancy
5.3.4 Weber's Law
5.4 Variational Perceptually Inspired Color Enhancement of Digital Images
5.4.1 Stability of the Numerical Scheme for the Minimization of Perceptual Functionals and Reduction of Computational Complexity
5.4.2 Relationship with Existing Perceptually Inspired Color Correction Models
5.5 Wavelet-based Implementation of Variational Perceptually Inspired Color Correction
5.5.1 Adjustment to the Average Value in the Wavelet Domain
5.5.2 Local Contrast Enhancement in the Wavelet Domain
5.6 Conclusions and Future Perspectives
Appendix 1
Appendix 2
References
6 The Color Logarithmic Image Processing (CoLIP) Antagonist Space
6.1 Introduction
6.2 Human Color Perception
6.2.1 Trichromacy and Color Photoreception
6.2.2 Opponent Process
6.2.3 Just Noticeable Differences and Logarithmic Laws
6.2.4 Color Representation Systems and Conversions
6.2.4.1 XYZ to LMS Conversion
6.2.4.2 sRGB to XYZ Conversion
6.2.4.3 XYZ to L*a*b* Conversion
6.3 LIP
6.3.1 Gray-Tone Functions
6.3.2 The Vectorial Structure
6.3.3 Illustrations and Applications
6.3.3.1 Elementary Operations Illustrations
6.3.3.2 Dynamic Range Maximization
6.4 CoLIP
6.4.1 From Cone Intensities to Achromatic and Chromatic Tones
6.4.1.1 Color Tones
6.4.1.2 Color Logarithmic Response
6.4.1.3 Opponent Process
6.4.2 The Trichromatic Antagonist Vectorial Structure
6.4.3 Bounded Vector Space
6.4.4 Summary
6.5 Psychophysical Connections
6.5.1 Color Combination
6.5.2 Comparison with the L*a*b* Space
6.5.3 Color-Matching Functions
6.5.4 Chromaticity Diagram and the Maxwell Triangle
6.6 Application to Image Processing and Analysis
6.6.1 Contrast Enhancement
6.6.1.1 Histogram Equalization
6.6.1.2 Dynamic Enhancement
6.6.2 White Balance Correction
6.6.3 Color Transfer
6.6.4 K-Means Clustering
6.6.5 Mathematical Morphology
6.6.5.1 Color Morphology
6.6.5.2 CoLIP Morphology
6.6.5.3 CoLIP Image Enhancement
6.7 Concluding Discussion and Perspectives
References
7 Color Management and Virtual Restoration of Artworks
7.1 Introduction
7.2 Color Perception
7.3 The Retinex Approach and Other Techniques of Color Enhancement
7.4 Color and Painting Restoration
7.5 Multispectral Images
7.6 Applications to Paintings
7.7 Principal Component Analysis
7.8 Evaluation of Restoration Work: A Case Study
7.9 Applications to Mosaics
7.10 3D Applications
7.11 Conclusions
References
8 A GPU-Accelerated Adaptive Simultaneous Dynamic Range Compression and Local Contrast Enhancement Algorithm for Real-Time Color Image Enhancement
8.1 Introduction
8.2 The Existing SDRCLCE Algorithm
8.3 The Adaptive SDRCLCE Algorithm
8.3.1 An Existing Adaptive Intensity Transfer Function
8.3.2 Application of SDRCLCE Formula into the Adaptive Intensity Transfer Function
8.3.3 Extension to Video Signal Processing
8.4 GPU Acceleration of the Adaptive SDRCLCE Algorithm
8.4.1 2D LLUT Update
8.4.2 Local Average Computation
8.4.3 2D LLUT Indexing
8.5 Experimental Results
8.5.1 Quantitative and Visual Comparisons Between CPU and GPU Implementations
8.5.2 Computational Performance Analysis
8.6 Conclusions
References
9 Color Equalization and Retinex
9.1 Introduction
9.2 Color Equalization
9.2.1 Histogram Equalization
9.2.2 Contrast Limited Adaptive Histogram Equalization
9.2.3 Automatic Color Equalization
9.2.3.1 Variational Framework for Contrast Enhancement
9.2.3.2 Variational Formulation of ACE
9.3 Color Constancy
9.3.1 Retinex Theory
9.3.2 Path-Based Retinex
9.3.2.1 Original Retinex
9.3.2.2 Random Spray Retinex
9.3.2.3 RACE
9.3.3 Recursive Retinex
9.3.4 Center/Surround Retinex
9.3.5 PDE-based Retinex
9.3.6 Variational Retinex
9.3.6.1 Kernel-Based Retinex and Contrast Enhancement
9.3.6.2 Variational Framework for The PDE-based Retinex
9.3.6.3 TV Regularized Model for Retinex
9.3.6.4 Variational Bayesian Method for Retinex
9.3.6.5 Variational Perceptually-Inspired Color Enhancement in Wavelet Domain
9.4 Discussions
References
10 Color Correction for Stereo and Multi-view Coding
10.1 Introduction
10.2 Color Calibration Methods for Multi-view Images/Video
10.2.1 Calibration of Multi-camera Setup
10.2.2 Embedded Illumination/Color Compensation
10.2.3 Preprocessed Color Correction
10.3 Simulations
10.4 Conclusion and Future Trends
References
11 Enhancement of Image Content for Observers with Colour Vision Deficiencies
11.1 Introduction
11.1.1 Normal Colour Vision
11.2 Simulation of Colour Vision Deficiencies
11.2.1 Simulation of Dichromatic CVDs
11.2.2 Simulation of Anomalous Trichromatic CVDs
11.3 Enhancement of Image Content for Observers with Colour Vision Deficiencies (Daltonization)
11.3.1 Content-Independent Methods
11.3.2 Content-Dependent Methods
11.4 Summary
References
12 Overview of Grayscale Image Colorization Techniques
12.1 Introduction
12.2 Literature Overview
12.3 Basics of Semi-Automatic Grayscale Image Colorization
12.4 Distance Transformation Based Colorization
12.5 Chrominance Blending Colorization
12.6 Isoline Image Colorization
12.7 Colorization Using Optimization
12.8 Examples of Semi-Automatic Image Colorization
12.9 Automatic Image Colorization Using Color Transfer
12.10 Colorization Quality Assessment
12.11 Summary
References
13 Computationally Efficient Data and Application Driven Color Transforms for the Compression and Enhancement of Images and Video
13.1 Introduction
13.2 Methodology
13.2.1 The aKLT: A Low-Complexity Unsupervised Data-Dependent Transform
13.2.2 A Supervised Approach to an Application-Dependent Color Transform Using Labeled Pixels
13.2.3 Combining Unsupervised and Supervised Approaches
13.3 Results and Discussion
13.3.1 Experimental Settings
13.3.2 Results
13.4 Conclusions
References
Color Image and Video Enhancement
M. Emre Celebi • Michela Lecca • Bogdan Smolka
Editors
Color Image and Video
Enhancement
1 C
Bogdan Smolka
Silesian University of Technology
Gliwice
Poland
Center for Information and Communication Technology
Editors
M. Emre Celebi
Louisiana State University
Shreveport
Louisiana
USA
Michela Lecca
Fondazione Bruno Kessler
Trento
Italy
ISBN 978-3-319-09362-8
DOI 10.1007/978-3-319-09363-5
ISBN 978-3-319-09363-5 (eBook)
Library of Congress Control Number: 2015943686
Springer Cham Heidelberg New York Dordrecht London
c Springer International Publishing Switzerland 2015
This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of
the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation,
broadcasting, reproduction on microfilms or in any other physical way, and transmission or information
storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology
now known or hereafter developed.
The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication
does not imply, even in the absence of a specific statement, that such names are exempt from the relevant
protective laws and regulations and therefore free for general use.
The publisher, the authors and the editors are safe to assume that the advice and information in this book
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the editors give a warranty, express or implied, with respect to the material contained herein or for any
errors or omissions that may have been made.
Printed on acid-free paper
Springer International Publishing AG Switzerland is part of Springer Science+Business Media
(www.springer.com)
Preface
Enhancement of digital images and video sequences is the process of increasing
the quality of the visual information by improving its visibility and perceptibil-
ity. Enhancement is a necessary step in image/video processing applications when
the conditions under which a scene is captured result in quality degradation, e.g.,
increased/decreased brightness and/or contrast, distortion of colors, and introduc-
tion of noise and other artifacts such as blotches and streaks. Unfortunately, most of
the traditional enhancement methods are designed for monochromatic image/video
data. The multivariate nature of color image/video data presents considerable chal-
lenges for researchers and practitioners as the numerous methods developed for
single channel data are often not directly applicable to multichannel data.
The goal of this volume is to summarize the state-of-the-art in color image and
video enhancement. The intended audience includes researchers and practitioners,
who are increasingly using color images and videos.
The volume opens with two chapters related to image acquisition. In “Colori-
metric Characterisation,” Westland focuses on the problem of color reproduction in
devices such as cameras, monitors, and printers. The author describes color spaces
mainly used for representing colors by consumer technologies currently available,
analyzes the device accuracy on the reproduction of real-world colors, and illustrates
various color correction methods for matching the color gamuts of different devices.
In “Image Demosaicing,” Zhen and Stevenson present an overview of demosaicking
methods. The authors introduce the fundamentals of interpolation and analyze the
structure of various state-of-the-art approaches. In addition, they elaborate on the
advantages and disadvantages of the examined techniques and evaluate their per-
formance using popular image quality metrics. Finally, they discuss demosaicing
combined with deblurring and super-resolution.
The volume continues with two chapters on noise removal. In “DCT-Based Color
Image Denoising: Efficiency Analysis and Prediction,” Lukin et al. discuss image
denoising techniques based on the discrete cosine transform (DCT). The authors
analyze noise models, discuss various image quality measures, describe various
types of filters, and introduce the concept of image enhancement utilizing the DCT.
v
vi
Preface
In “Impulsive Noise Filters for Colour Images,” Morillas et al. give an overview
of the impulsive noise reduction methods for color images. They analyze various
models of impulsive noise contamination, introduce quality metrics used for the
evaluation of filtering effectiveness, discuss various methods of vector ordering, and
analyze the main types of noise reduction algorithms. The authors not only describe
various approaches to impulsive noise reduction, but also evaluate their effectiveness
and summarize their main properties.
The volume continues with seven chapters on color/contrast enhancement. In
“Spatial and Frequency-Based Variational Methods for Perceptually Inspired Color
and Contrast Enhancement of Digital Images,” Provenzi considers perceptually
inspired color correction algorithms that aim to reproduce the color sensation pro-
duced by the human vision system. These algorithms are based on the well-known
Retinex model, introduced by Land and McCann about 45 years ago. The author
shows that Retinex-like approaches can be embedded in a general variational frame-
work, where these methods can be interpreted as a local, nonlinear modification
of histogram equalization. In “The Color Logarithmic Image Processing (CoLIP)
Antagonist Space,” Gavet et al. present a survey of Color Logarithmic Image
Processing, a perceptually-oriented mathematical framework for representing and
processing color images. The authors also present various applications of this frame-
work ranging from contrast enhancement to segmentation. In “Color Management
and Virtual Restoration of Artworks,” Maino and Monti present a survey of the use
of color and contrast enhancement techniques in the virtual restoration of artworks
such as paintings, mosaics, ancient archival documents, and manuscripts. Histogram
equalization approaches, Retinex-like methods, and multi-spectral image process-
ing algorithms are essential tools to analyse an artwork, to discover its history, to
measure its conservation/degradation status, and to plan future physical restora-
tion. The authors provide examples of applications of such digital techniques on
several well-known Italian artworks. In “A GPU-Accelerated Adaptive Simulta-
neous Dynamic Range Compression and Local Contrast Enhancement Algorithm
for Real-Time Color Image Enhancement,” Tsai and Huang propose an adaptive
dynamic range compression algorithm for color image enhancement. The authors
demonstrate that a CUDA implementation of the proposed algorithm achieves up
to 700% speed up when executed on an NVIDIA NVS 5200M GPU compared to
a LUT-accelerated implementation executed on an Intel Core i7-3520M CPU. In
“Color Equalization and Retinex,” Wang et al. give an overview of several percep-
tually inspired color correction algorithms that attempt to simulate the human color
constancy capability. The authors first describe two histogram equalization meth-
ods that modify the image colors by manipulating respectively the global and local
color distributions. They then illustrate an automatic color equalization approach
that enhances the color and contrast of an image by combining the Gray-World and
White-Patch models. Finally, they describe the Retinex model and various imple-
mentations of it. In “Color Correction for Stereo and Multi-View Coding,” Fezza
and Larabi first present a survey of color correction methods for multi-view video.
They then compare the quantitative/qualitative performance of some of the popular
Preface
vii
methods with respect to color consistency, coding performance, and rendering qual-
ity. Finally, in “Enhancement of Image Content for Observers with Colour Vision
Deficiencies,” Mili´c et al. present a survey of daltonization methods designed for
enhancing the perceptual quality of color images for the benefit of observers with
color vision deficiencies.
In “Computationally Efficient Data and Application Driven Color Transforms
for the Compression and Enhancement of Images and Video,” Minervini et al. deal
with the problem of efficient coding and transmission of color images and videos.
The RGB data recorded by camera sensors are typically redundant due to high
correlation of the color channels. The authors describe two frameworks to obtain
linear maps of the RGB data that minimize the loss of information due to com-
pression. The first adapts to the image data and aims at reconstruction accuracy,
representing an efficient approximation of the classic Karhunen-Loève transform.
The second adapts to the application in which the images are used, for instance,
an image classification task. A chapter entitled “Overview of Grayscale Image Col-
orization Techniques,” by Popowicz and Smolka completes the volume. The authors
first present a survey of semi-automatic grayscale image colorization methods. They
then compare the performance of three semi-automatic and one fully-automatic
method on a variety of images. Finally, they propose a methodology for evaluating
colorization methods based on several well-known quality assessment measures.
As editors, we hope that this volume focused on color image and video enhance-
ment will demonstrate the significant progress that has occurred in this field in recent
years. We also hope that the developments reported in this volume will motivate fur-
ther research in this exciting field.
M. Emre Celebi
Michela Lecca
Bogdan Smolka
Contents
1
2
3
4
5
6
7
8
Colorimetric Characterization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Stephen Westland
1
Image Demosaicing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
Ruiwen Zhen and Robert L. Stevenson
DCT-Based Color Image Denoising: Efficiency Analysis and
Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
Vladimir Lukin, Sergey Abramov, Ruslan Kozhemiakin, Alexey Rubel,
Mikhail Uss, Nikolay Ponomarenko, Victoriya Abramova, Benoit
Vozel, Kacem Chehdi, Karen Egiazarian and Jaakko Astola
Impulsive Noise Filters for Colour Images . . . . . . . . . . . . . . . . . . . . . . . . 81
Samuel Morillas, Valentín Gregori, Almanzor Sapena,
Joan-Gerard Camarena and Bernardino Roig
Spatial and Frequency-Based Variational Methods for Perceptually
Inspired Color and Contrast Enhancement of Digital Images . . . . . . . . 131
Edoardo Provenzi
The Color Logarithmic Image Processing (CoLIP) Antagonist Space . 155
Yann Gavet, Johan Debayle and Jean-Charles Pinoli
Color Management and Virtual Restoration
of Artworks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183
Giuseppe Maino and Mariapaola Monti
A GPU-Accelerated Adaptive Simultaneous Dynamic Range
Compression and Local Contrast Enhancement Algorithm for
Real-Time Color Image Enhancement . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233
Chi-Yi Tsai and Chih-Hung Huang
ix
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