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基本説明
Topics covered include image denoising, document image analysis, remote sensing applications, and image formation, modeling.
Full 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.
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.
