Book: Super-Resolution Imaging

Includes techniques to improve images and video acquired by sensors

Published: Friday, September 24, 2010 - 12:25

(CRC Press: Boca Raton, FL) -- With the exponential increase in computing power and the 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  (CRC Press, 2010), edited by Peyman Milanfar and the latest offering in the CRC Press Digital Imaging and Computer Vision series, consolidates recent research of eminent scholars and practitioners from the United States, United Kingdom, Israel, Japan, and the European Union. It describes the latest in both theoretical and practical aspects of the technology and is of direct relevance to academia and industry.

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 features downloadable tools to supplement material found in the book.

The book concentrates 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, multichannel, 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.

Super-Resolution Imaging covers:

• History and future directions of super-resolution imaging

• Locally adaptive processing methods vs. 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

Peyman Milanfar is a professor of electrical engineering at the University of California, Santa Cruz (UCSC). He received a doctorate 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.

About The Author

CRC Press

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