Image Restoration Fundamentals and Advances

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.

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

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.