Short description:

Addressing a variety of applications, this book covers a wide range of topics pertaining to image restoration. Topics covered include image denoising, document image analysis, remote sensing applications, and image formation modeling. Content also addresses blind image restoration, iterative optimization techniques, learning-based image restoration, and non-local approach, as well as super-resolution, multi-spectral restoration, and video inpainting. Contributors from various countries help readers understand the essentials, the state of the art, and the research directions. The text features background material, mathematical content, a survey of the methods, examples, comparisons, and a bibliography.

Long 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. Fundamentals of Image Restoration. Restoration in the Presence of Unknown Spatially Varying Blur. Image Denoising and Restoration Based on Nonlocal Means. Sparsity-Regularized Image Restoration: Locality and Convexity Revisited. Resolution Enhancement Using Prior Information. Transform Domain-Based Learning for Super Resolution Restoration. Super Resolution for Multispectral Image Classification. Color Image Restoration Using Vector Filtering Operators. Document Image Restoration and Analysis as Separation of Mixtures of Patterns: From Linear to Nonlinear Models. Correction of Spatially Varying Image and Video Motion Blur Using a Hybrid Camera.