Super-Resolution Imaging

1st Edition

Peyman Milanfar

CRC Press
Published September 28, 2010
Reference - 490 Pages - 163 B/W Illustrations
ISBN 9781439819302 - CAT# K11018
Series: Digital Imaging and Computer Vision

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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 industry, providing a base of understanding for future progress.

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Recent advances in camera sensor technology 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 nonparametric 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.


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