Multi Frame Super Resolution Techniques And Applications

Image Resolution is the most important quality factor of videos and images. Multi-frame Super-resolution (SR) is the process of creating a higher resolution image with finer details, by using the information of multiple low-resolution images taken from almost the same scene. In recent years images with higher resolution became the most primary requirement in many of the image processing applications, such as scientific applications, medical imaging, robotics, video sequences and satellite imaging. This book aimed at providing a good guide in analyzing the most appropriate multi-frame super-resolution methods based on spatial and frequency domain. It also presents an optimized method which can be used for many image enhancement applications.

Optical coherence tomography (OCT) is a promising non-invasive non-contact 3D imaging technique that can be used to evaluate and inspect material surfaces, multilayer polymer films, fiber coils, and coatings. OCT can be used for the examination of cultural heritage objects and 3D imaging of microstructures. With subsurface 3D fingerprint imaging capability, OCT could be a valuable tool for enhancing security in biometric applications. OCT can also be used for the evaluation of fastener flushness for improving aerodynamic performance of high-speed aircraft. More and more OCT non-medical applications are emerging. In this book, we present some recent advancements in OCT technology and non-medical applications.

This proceedings book contains selected and expanded contributions presented at the 7th International Symposium of Space Optical Instruments and Applications, held in Beijing, China, on Oct 21–23, 2022. The meeting was organized by the Sino-Holland Space Optical Instruments Joint Laboratory and supported by Beijing Institute and Space Mechanics and Electricity. In the recent years, space optical payloads are advancing toward high spatial resolution, high temporal resolution, high radiometric resolution, and high spectral resolution and becoming more and more intelligent. Commercial remote sensing industry has made steady progress in terms of the scope of satellite systems and applications. Meanwhile, space optical remote sensing data has been extensively applied to monitoring of resources, meteorology, ocean, environment, disaster reduction, and many other fields. The symposium focused on key innovations of space-based optical instruments and applications and the newest developments in theory, technology, and applications in optics, in both China and Europe. It thus provided a platform for exchanges on the latest research and current and planned optical missions. The major topics covered in these conference proceedings are: 1) Advanced space optical remote sensing application technology. 2) Deep space exploration and astronomical observation technology. 3) Advanced space optical remote sensing instrument technology. 4) Commercial optical observation technology and services.

Example-Based Super Resolution provides a thorough introduction and overview of example-based super resolution, covering the most successful algorithmic approaches and theories behind them with implementation insights. It also describes current challenges and explores future trends. Readers of this book will be able to understand the latest natural image patch statistical models and the performance limits of example-based super resolution algorithms, select the best state-of-the-art algorithmic alternative and tune it for specific use cases, and quickly put into practice implementations of the latest and most successful example-based super-resolution methods. Provides detailed coverage of techniques and implementation details that have been successfully introduced in diverse and demanding real-world applications Covers a wide variety of machine learning approaches, ranging from cross-scale self-similarity concepts and sparse coding, to the latest advances in deep learning Presents a statistical interpretation of the subspace of natural image patches that transcends super resolution and makes it a valuable source for any researcher on image processing or low-level vision

Super-Resolution Imaging serves as an essential reference for both academicians and practicing engineers. It can be used both as a text for advanced courses in imaging and as a desk reference for those working in multimedia, electrical engineering, computer science, and mathematics. The first book to cover the new research area of super-resolution imaging, this text includes work on the following groundbreaking topics: Image zooming based on wavelets and generalized interpolation; Super-resolution from sub-pixel shifts; Use of blur as a cue; Use of warping in super-resolution; Resolution enhancement using multiple apertures; Super-resolution from motion data; Super-resolution from compressed video; Limits in super-resolution imaging. Written by the leading experts in the field, Super-Resolution Imaging presents a comprehensive analysis of current technology, along with new research findings and directions for future work.

This book explores the application of deep learning techniques within a particularly difficult computational type of computer vision (CV) problem ─ super-resolution (SR). The authors present and discuss ways to apply computational intelligence (CI) methods to SR. The volume also explores the possibility of using different kinds of CV techniques to develop and enhance the tools/processes related to SR. The application areas covered include biomedical engineering, healthcare applications, medicine, histology, and material science. The book will be a valuable reference for anyone concerned with multiple multimodal images, especially professionals working in remote sensing, nanotechnology and immunology at research institutes, healthcare facilities, biotechnology institutions, agribusiness services, veterinary facilities, and universities.

This book aims to explore the latest practices and research works in the area of sensor fusion. The book intends to provide a collection of novel ideas, theories, and solutions related to the research areas in the field of sensor fusion. This book is a unique, comprehensive, and up-to-date resource for sensor fusion systems designers. This book is appropriate for use as an upper division undergraduate or graduate level text book. It should also be of interest to researchers, who need to process and interpret the sensor data in most scientific and engineering fields. The initial chapters in this book provide a general overview of sensor fusion. The later chapters focus mostly on the applications of sensor fusion. Much of this work has been published in refereed journals and conference proceedings and these papers have been modified and edited for content and style. With contributions from the world's leading fusion researchers and academicians, this book has 22 chapters covering the fundamental theory and cutting-edge developments that are driving this field.

This book constitutes the thoroughly refereed post-conference proceedings of the 7th International Conference on Intelligent Computing, ICIC 2011, held in Zhengzhou, China, in August 2011. The 94 revised full papers presented were carefully reviewed and selected from 832 submissions. The papers are organized in topical sections on intelligent computing in scheduling; local feature descriptors for image processing and recognition; combinatorial and numerical optimization; machine learning theory and methods; intelligent control and automation; knowledge representation/reasoning and expert systems; intelligent computing in pattern recognition; intelligent computing in image processing; intelligent computing in computer vision; biometrics with applications to individual security/forensic sciences; modeling, theory, and applications of positive systems; sparse manifold learning methods and applications; advances in intelligent information processing.