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- DescriptionWith the exponential increase in computing power and broad proliferation of digital cameras, super-resolution imaging is poised to become the next killer app. The growing interest in this techlogy has manifested itself in an explosion of literature on the subject. Super-Resolution Imaging consolidates key recent research contributions from eminent scholars and practitioners in this area and serves as a starting point for exploration into the state of the art in the field. It describes the latest in both theoretical and practical aspects of direct relevance to academia and industry, providing a base of understanding for future progress. Features downloadable tools to supplement material found in the book Recent advances in camera sensor techlogy have led to an increasingly larger number of pixels being crammed into ever-smaller spaces. This has resulted in an overall decline in the visual quality of recorded content, necessitating improvement of images through the use of post-processing. Providing a snapshot of the cutting edge in super-resolution imaging, this book focuses on methods and techniques to improve images and video beyond the capabilities of the sensors that acquired them. It covers: History and future directions of super-resolution imaging Locally adaptive processing methods versus globally optimal methods Modern techniques for motion estimation How to integrate robustness Bayesian statistical approaches Learning-based methods Applications in remote sensing and medicine Practical implementations and commercial products based on super-resolution The book concludes by concentrating on multidisciplinary applications of super-resolution for a variety of fields. It covers a wide range of super-resolution imaging implementation techniques, including variational, feature-based, multi-channel, learning-based, locally adaptive, and nparametric methods. This versatile book can be used as the basis for short courses for engineers and scientists, or as part of graduate-level courses in image processing.
- Author BiographyPeyman Milanfar is Professor of Electrical Engineering at the University of California, Santa Cruz. He received a B.S. degree in Electrical Engineering/Mathematics from the University of California, Berkeley, and the Ph.D. degree in Electrical Engineering from the Massachusetts Institute of Technology. Prior to coming to UCSC, he was at SRI (formerly Stanford Research Institute) and served as a Consulting Professor of computer science at Stanford. In 2005 he founded MotionDSP, Inc., to bring state-of-art video enhancement technology to consumer and forensic markets. He is a Fellow of the IEEE for contributions to Inverse Problems and Super-resolution in Imaging.
- PublisherTaylor & Francis Inc
- Date of Publication07/09/2010
- SubjectOther Specific Technologies
- Series TitleDigital Imaging and Computer Vision
- Series Part/Volume Numberv. 1
- Place of PublicationBosa Roca
- Country of PublicationUnited States
- ImprintCRC Press Inc
- Content Note163 black & white illustrations, 13 black & white tables
- Weight839 g
- Width156 mm
- Height235 mm
- Spine30 mm
- Edited byPeyman Milanfar
- Format DetailsUnsewn / adhesive bound
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