| Description |
1 online resource (492 pages) |
| Series |
Digital Imaging and Computer Vision |
| Contents |
Front cover; Contents; Preface; Chapter 1: Image Super-Resolution: Historical Overviewand Future Challenges; Chapter 2: Super-Resolution Using Adaptive Wiener Filters; Chapter 3: Locally Adaptive Kernel Regression for Space-Time Super-Resolution; Chapter 4: Super-Resolution with Probabilistic Motion Estimation; Chapter 5: Spatially Adaptive Filtering asRegularization in Inverse Imaging:Compressive Sensing, Super-Resolution, and Upsampling; Chapter 6: Registration for Super-Resolution: Theory, Algorithms, and Applications in Image and Mobile Video Enhancement |
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Chapter 7: Towards Super-Resolution in the Presence of Spatially Varying BlurChapter 8: Toward Robust Reconstruction-Based Super-Resolution; Chapter 9: Multiframe Super-Resolution from a Bayesian Perspective; Chapter 10: Variational Bayesian Super-Resolution Reconstruction; Chapter 11: Pattern Recognition Techniques for Image Super-Resolution; Chapter 12: Super-Resolution Reconstruction of Multichannel Images; Chapter 13: New Applications of S |
| Summary |
With 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 technology 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 industr |
| Notes |
Print version record |
| Subject |
High resolution imaging.
|
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Image processing -- Digital techniques.
|
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digital imaging.
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High resolution imaging
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Image processing -- Digital techniques
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| Form |
Electronic book
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| ISBN |
9781439819319 |
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1439819319 |
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