Image Restoration : Fundamentals and Advances book cover
1st Edition

Image Restoration
Fundamentals and Advances

ISBN 9781138071773
Published March 29, 2017 by CRC Press
378 Pages 45 Color & 118 B/W Illustrations

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Book Description

Image Restoration: Fundamentals and Advances responds to the need to update most existing references on the subject, many of which were published decades ago. Providing a broad overview of image restoration, this book explores breakthroughs in related algorithm development and their role in supporting real-world applications associated with various scientific and engineering fields. These include astronomical imaging, photo editing, and medical imaging, to name just a few. The book examines how such advances can also lead to novel insights into the fundamental properties of image sources.

Addressing the many advances in imaging, computing, and communications technologies, this reference strikes just the right balance of coverage between core fundamental principles and the latest developments in this area. Its content was designed based on the idea that the reproducibility of published works on algorithms makes it easier for researchers to build on each other’s work, which often benefits the vitality of the technical community as a whole. For that reason, this book is as experimentally reproducible as possible.

Topics covered include:

  • Image denoising and deblurring
  • Different image restoration methods and recent advances such as nonlocality and sparsity
  • Blind restoration under space-varying blur
  • Super-resolution restoration
  • Learning-based methods
  • Multi-spectral and color image restoration
  • New possibilities using hybrid imaging systems

Many existing references are scattered throughout the literature, and there is a significant gap between the cutting edge in image restoration and what we can learn from standard image processing textbooks. To fill that need but avoid a rehash of the many fine existing books on this subject, this reference focuses on algorithms rather than theories or applications. Giving readers access to a large amount of downloadable source code, the book illustrates fundamental techniques, key ideas developed over the years, and the state of the art in image restoration. It is a valuable resource for readers at all levels of understanding.

Table of Contents

Image Denoising: Past, Present, and Future, X. Li

Historical Review of Image Denoising

First Episode: Local Wiener Filtering

Second Episode: Understanding Transient Events

Third Generation: Understanding Nonlocal Similarity

Conclusions and Perspectives

Fundamentals of Image Restoration,
B.K. Gunturk

Linear Shift-Invariant Degradation Model

Image Restoration Methods

Blind Image Restoration

Other Methods of Image Restoration

Super Resolution Image Restoration

Regularization Parameter Estimation

Beyond Linear Shift-Invariant Imaging Model

Restoration in the Presence of Unknown Spatially Varying Blur,
M. Sorel and F. Sroubek

Blur models

Space-Variant Super Resolution

Image Denoising and Restoration Based on Nonlocal Means,
P. van Beek, Y. Su, and J. Yang

Image Denoising Based on the Nonlocal Means

Image Deblurring Using Nonlocal Means Regularization

Recent Nonlocal and Sparse Modeling Methods

Reducing Computational Cost of NLM-Based Methods

Sparsity-Regularized Image Restoration: Locality and Convexity Revisited,
W. Dong and X. Li

Historical Review of Sparse Representations

From Local to Nonlocal Sparse Representations

From Convex to Nonconvex Optimization Algorithms

Reproducible Experimental Results

Conclusions and Connections

Resolution Enhancement Using Prior Information,
H.M. Shieh, C.L. Byrne, and M.A. Fiddy

Fourier Transform Estimation and Minimum L2-Norm Solution

Minimum Weighted L2-Norm Solution

Solution Sparsity and Data Sampling

Minimum L1-Norm and Minimum Weighted L1-Norm Solutions

Modification with Nonuniform Weights

Summary and Conclusions

Transform Domain-Based Learning for Super Resolution Restoration,
P.P. Gajjar, M.V. Joshi, and K.P. Upla

Introduction to Super Resolution

Related Work

Description of the Proposed Approach

Transform Domain-Based Learning of the Initial HR Estimate

Experimental Results

Conclusions and Future Research Work

Super Resolution for Multispectral Image Classification,
F. Li, X. Jia, D. Fraser, and A. Lambert


Experimental Results

Color Image Restoration Using Vector Filtering Operators,
R. Lukac

Color Imaging Basics

Color Space Conversions

Color Image Filtering

Color Image Quality Evaluation

Document Image Restoration and Analysis as Separation of Mixtures of Patterns: From Linear to Nonlinear Models,
A. Tonazzini, I. Gerace, and F. Martinelli

Linear Instantaneous Data Model

Linear Convolutional Data Model

Nonlinear Convolutional Data Model for the Recto–Verso Case

Conclusions and Future Prospects

Correction of Spatially Varying Image and Video Motion Blur Using a Hybrid Camera
, Y.-W. Tai and M.S. Brown

Related Work

Hybrid Camera System

Optimization Framework

Deblurring of Moving Objects

Temporal Upsampling

Results and Comparisons

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Bahadir K. Gunturk received his B.S. degree from Bilkent University, Turkey, and his Ph.D. degree from the Georgia Institute of Technology in 1999 and 2003, respectively, both in electrical engineering. Since 2003, he has been with the Department of Electrical and Computer Engineering at Louisiana State University, where he is an associate professor. His research interests are in image processing and computer vision. Dr. Gunturk was a visiting scholar at the Air Force Research Lab in Dayton, Ohio, and at Columbia University in New York City. He is the recipient of the Outstanding Research Award at the Center of Signal and Image Processing at Georgia Tech in 2001, the Air Force Summer Faculty Fellowship Program (SFFP) Award in 2011 and 2012, and named as a Flagship Faculty at Louisiana State University in 2009.

Xin Li received his B.S. degree with highest honors in electronic engineering and information science from the University of Science and Technology of China, Hefei, in 1996, and his Ph.D. degree in electrical engineering from Princeton University, Princeton, New Jersey, in 2000. He was a member of the technical staff with Sharp Laboratories of America, Camas, Washington, from August 2000 to December 2002. Since January 2003, he has been a faculty member in the Lane Department of Computer Science and Electrical Engineering at West Virginia University. He is currently a tenured associate professor at that school. His research interests include image/video coding and processing. Dr. Li received a Best Student Paper Award at the Visual Communications and Image Processing Conference in 2001; a runner-up prize of Best Student Paper Award at the IEEE Asilomar Conference on Signals, Systems and Computers in 2006; and a Best Paper Award at the Visual Communications and Image Processing Conference in 2010.