Image Restoration

Image Restoration: Fundamentals and Advances

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Features

    • Provides a comprehensive coverage of the image restoration field
    • Reviews the state of the art in image restoration techniques
    • Provides a balanced and guiding presentation of fundamental and advanced ideas and techniques
    • Includes a large portion of experimentally reproducible material (useful MATLAB codes/demos for beginners, all on an accompanying website)

    Summary

    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

    Methodology

    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

    Editor Bio(s)

    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.

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