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Denoising of Photographic Images and Video.pdf
Preface
Contents
1 Modeling and Estimation of Signal-Dependent and Correlated Noise
1.1 Introduction: Acquisition Devices and Noise Sources
1.2 Additive White Gaussian Noise
1.3 Raw Image Dataset
1.4 One-Parameter Families of Distributions
1.4.1 Poisson Noise and Poisson Family of Distributions
1.4.2 Scaled Poisson Distribution Family
1.4.3 Poisson–Gaussian Noise
1.4.4 Gaussian Approximation of the Poisson Distribution
1.4.5 Signal-Dependent Heteroskedastic Gaussian Models
1.4.6 Doubly Censored Heteroskedastic Gaussian Noise: A Model for Clipped Noisy Data
1.5 Estimation of the Standard Deviation Curve
1.5.1 Patch-Based Methods
1.5.2 Segmentation-Based Methods
1.5.3 Alternative Approaches
1.6 Correlated Noise
1.6.1 Stationary Correlated Noise
1.6.2 Correlated Signal-Dependent Noise model
1.6.3 Estimation
1.7 Photo-Response Nonuniformity
1.8 Conclusions
References
2 Sparsity-Based Denoising of Photographic Images: From Model-Based to Data-Driven
2.1 Introduction
2.2 Image Denoising via Local Sparsity Models: From Sparsifying Transform to Dictionary Learning
2.2.1 Transform-Based Image Denoising
2.2.2 Image Denoising via Dictionary Learning
2.3 Image Denoising via Nonlocal Sparsity Models: Beyond BM3D
2.3.1 Image Denoising via Clustering-Based Sparse Representation (CSR)
2.3.2 Simultaneous Sparse Coding with Low-Rank Approximation
2.3.3 Simultaneous Sparse Coding with Gaussian Scalar Mixture (GSM)
2.4 Learning-Based Image Denoising: Heavy Noise and Deep Learning
2.4.1 Learning Parametric Sparse Models for Heavy Noise Removal
2.4.2 Learning Denoising Prior in Deep Convolutional Neural Network
2.5 Conclusions
References
3 Image Denoising—Old and New
3.1 Introduction
3.2 Denoising as Statistical Inference and MAP Estimation
3.3 Variational Image Denoising Methods
3.3.1 Total Variation (TV)-Based Image Regularization
3.3.2 Generalizations: Vectorial TV, Total Generalized Variation
3.3.3 Nonconvex Regularizers
3.3.4 Non-local Regularization
3.3.5 A Discussion Within Classical Denoising Methods
3.4 Learning-Based Denoising Methods
3.5 Combining Learning and Variational Methods
3.5.1 Lacking Flexibility
3.5.2 Learning the Regularizer
3.5.3 Developing Network Architectures from Optimization Methods
3.5.4 Algorithmic Schemes Based on Learned Proximal Operators
3.6 Numerical Experiments: Denoising by Denoising?
3.6.1 Different Noise Types and Algorithmic Schemes
3.6.2 Handling Constraints
3.7 Conclusions
References
4 Convolutional Neural Networks for Image Denoising and Restoration
4.1 Introduction
4.1.1 Recent Advances
4.2 Learning Deep CNN for Image Denoising
4.2.1 Architecture Design: DnCNN
4.2.2 Residual Learning and Batch Normalization
4.2.3 Connection with TNRD
4.2.4 Understanding the CNN Modeling Capacity
4.2.5 Implementation and Experiments
4.3 CNN for Fast and Flexible Image Denoising
4.3.1 Network Architecture: FFDNet
4.3.2 Taking Noise Level as CNN Input
4.3.3 Single Non-blind Model Versus Single Blind Model
4.3.4 Denoising on Subimages
4.3.5 Dataset Generation and Network Training
4.3.6 Experiments on Synthetic and Real Images
4.3.7 Running Time
4.4 CNN Denoiser Prior Based Image Restoration
4.4.1 Half Quadratic Splitting Algorithm
4.4.2 CNN Denoisers and Parameter Setting
4.4.3 Image Deblurring
4.4.4 Single Image Super-Resolution
4.4.5 Image Inpainting
4.5 Challenges and Possible Solutions
References
5 Gaussian Priors for Image Denoising
5.1 Introduction
5.2 What Is Encoded in Gaussian and GMM Priors?
5.3 How to Derive Estimators Under Gaussian and GMM Priors
5.3.1 Estimation with Gaussian Priors
5.3.2 Estimation with Gaussian Mixture Models
5.3.3 Other Estimation Strategies
5.4 From Patches to Images: Aggregation Procedures
5.5 Inference of Gaussian and GMM Priors
5.5.1 Gaussian Models
5.5.2 How to Group Patches to Infer Gaussian Priors?
5.5.3 Inference for Gaussian Mixture Models
5.5.4 Inference in High Dimension
5.6 Discussion and Conclusion
References
6 Internal Versus External Denoising—Benefits and Bounds
6.1 Introduction
6.2 On the Preference of Local Denoising
6.2.1 Internal Local Versus Global Denoising
6.2.2 Internal Local Versus External Denoising
6.2.3 Patch Signal-to-Noise Ratio (PatchSNR) and Its Implication on Internal Versus External Preference
6.3 Noise Overfitting
6.4 The Impact of Deep Learning on Denoising and Their Bounds
References
7 Patch-Based Methods for Video Denoising
7.1 Introduction
7.2 Image Denoising
7.2.1 White Noise Removal
7.3 White Noise Removal in Video Sequences
7.3.1 Introduction
7.3.2 Patch Selection Criteria
7.3.3 Patch Denoising
7.3.4 Brief Comparison
7.4 Real Noise Removal
7.4.1 Introduction
7.4.2 Temporal Estimation for Signal-Dependent Noise
7.4.3 Real Video Denoising Chain
7.4.4 Experimentation and Comparison
7.5 Conclusion
References
8 Image and Video Noise: An Industry Perspective
8.1 Overview
8.2 Noise in the Image Processing Pipeline
8.2.1 From Scene to Sensor
8.2.2 From Sensor to Rendered Image
8.2.3 Fixed-Pattern Noise
8.2.4 Transmission and Rendering
8.3 Measuring Noise in Images
8.3.1 Classifying Image Noise/Difference/Quality Metrics
8.3.2 Commonly Used Metrics in Industry Applications
8.3.3 Standards Related to Noise Measurement
8.4 Conclusion
References
9 Noise Characteristics and Noise Perception
9.1 Beyond Standard Noise Models: Noise and Denoising in a Real Camera Processing Pipeline
9.2 Camera Processing Pipeline: From Sensor Data to Monitor-Ready Image Data
9.3 Camera Noise Characteristics
9.4 Camera Noise in the Processing Pipeline
9.5 Local Correlation Introduced with Debayering
9.6 Influence on Denoising
9.7 Visual Perception of Spatially Correlated Noise
9.8 Noise Perception in Videos
9.8.1 Related Work
9.8.2 Noise Visibility Test
9.8.3 Results
9.8.4 Discussion
9.9 Summary and Conclusion
References
10 Pull-Push Non-local Means with Guided and Burst Filtering Capabilities
10.1 Introduction
10.2 Related Work
10.2.1 Overview of Pull-Push
10.2.2 Overview of NLM
10.3 Pull-Push NLM
10.3.1 Pull Stage: Downfuse
10.3.2 Push Stage: Upfuse
10.3.3 Per-Level Patch Variance Reduction
10.3.4 Selective Up-Integration Using Reliability
10.4 Joint Pull-Push Non-local Means
10.5 Burst Processing
10.5.1 Processing of Burst Frames Without Explicit Motion Compensation
10.5.2 Simultaneous NLM and Motion Compensation Using the Pull-Push Framework
10.6 Experimental Results
10.6.1 Pull-Push NLM Experiments
10.6.2 Joint Pull-Push NLM Experiments
10.6.3 Burst Processing Experiments
10.7 Conclusion
References
11 Three Approaches to Improve Denoising Results that Do Not Involve Developing New Denoising Methods
11.1 Introduction
11.2 Denoise a Transform of an Image Instead of the Image Itself
11.2.1 Image Decomposition in a Moving Frame
11.2.2 Application to Image Denoising
11.2.3 The Noise Level Is Higher on the Intensity Values of a Gray-Level Image Than on Its Components in a Well-Chosen Moving Frame
11.2.4 Experiments
11.2.5 Research Avenue to Explore
11.3 Have a Proper Noise Model
11.3.1 An Image Database with Clean and Noisy Versions of Photos in RAW and Nonlinear Camera Output Versions
11.3.2 Ranking Denoising Algorithms: AWG Noise Versus Realistic Noise Model
11.3.3 Comparing Local Denoising on DRAW Versus Nonlocal Denoising on Camera Output
11.3.4 Research Avenue to Explore
11.4 Optimize Denoising Methods According to Perceived Quality of Results
11.4.1 Experiments
11.4.2 Research Avenue to Explore
11.5 Conclusion
References
Index
Advances in Computer Vision and Pattern Recognition
Marcelo Bertalmío Editor
Denoising of
Photographic
Images and
Video
Fundamentals, Open Challenges and
New Trends
Advances in Computer Vision and Pattern
Recognition
Founding editor
Sameer Singh, Rail Vision, Castle Donington, UK
Series editor
Sing Bing Kang, Microsoft Research, Redmond, WA, USA
Advisory Board
Horst Bischof, Graz University of Technology, Austria
Richard Bowden, University of Surrey, Guildford, UK
Sven Dickinson, University of Toronto, ON, Canada
Jiaya Jia, The Chinese University of Hong Kong, Hong Kong
Kyoung Mu Lee, Seoul National University, South Korea
Yoichi Sato, The University of Tokyo, Japan
Bernt Schiele, Max Planck Institute for Computer Science, Saarbrücken, Germany
Stan Sclaroff, Boston University, MA, USA
More information about this series at http://www.springer.com/series/4205
Marcelo Bertalmío
Editor
Denoising of Photographic
Images and Video
Fundamentals, Open Challenges and New
Trends
123
Editor
Marcelo Bertalmío
Pompeu Fabra University
Barcelona, Spain
ISSN 2191-6586
Advances in Computer Vision and Pattern Recognition
ISBN 978-3-319-96028-9
https://doi.org/10.1007/978-3-319-96029-6
ISSN 2191-6594
(electronic)
ISBN 978-3-319-96029-6
(eBook)
Library of Congress Control Number: 2018948593
© Springer International Publishing AG, part of Springer Nature 2018
This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part
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The use of general descriptive names, registered names,
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The publisher, the authors and the editors are safe to assume that the advice and information in this
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A Serrana, Graciela, Lucas y Vera
Preface
Noise is always present in images, regardless of the way they have been acquired.
This is why noise removal or denoising is a key image processing problem,
especially with respect
to photo and video cameras, where the push toward
ever-increasing resolution, dynamic range, and frame rate implies ever higher
demands on denoising performance.
Denoising has a long and rich history, with early works dating back to the 1960s.
Classic denoising techniques were mostly based in one of these two approaches:
modification of transform coefficients (using the Fourier transform, the DCT, some
form of wavelet, etc.) or averaging image values (in a neighborhood, along con-
tours, with similar but possibly distant pixels, etc.). Both types of approaches
yielded results that were modest, in terms of objective error metrics and also in
terms of visual quality, with frequent problems such as oversmoothing, staircase
effects, or ringing artifacts.
In 2005, the groundbreaking nonlocal means method proposed comparing image
neighborhoods (patches) in order to denoise single pixels. This approach produced
results that were shockingly superior to the state of the art at the time, so much so
that from then on virtually all image denoising algorithms have been patch-based.
Actually, the increase in quality of the denoising algorithms in the past few years
has been so dramatic that several recent works have questioned whether or not there
is still room for improvement in denoising, with some researchers considering the
problem pretty much solved and no longer relevant.
One of the goals of this book is to show that, in fact, that is not the case: in
denoising there are some fundamental challenges that remain unsolved and that
include how to properly model noise in real scenarios, how to tailor denoising
algorithms to these models, and how to evaluate the results in a way that is con-
sistent with perceived image quality.
Another goal was to have a book dealing exclusively with noise removal for
photographs and video. Despite the commercial significance of the image and video
industry and the fact that many academic works on denoising are evaluated on
regular photos and videos, this would be, surprisingly, the first book centered on
this specific topic.
ix
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