Buch, Englisch, 496 Seiten, Format (B × H): 161 mm x 242 mm, Gewicht: 824 g
Buch, Englisch, 496 Seiten, Format (B × H): 161 mm x 242 mm, Gewicht: 824 g
Reihe: Digital Imaging and Computer Vision
ISBN: 978-1-4398-1930-2
Verlag: Taylor & Francis
Features downloadable tools to supplement material found in the book
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.
Zielgruppe
Researchers and technicians in image and video processing and computer vision, computer science, and applied mathematics; including electrical and industrial engineers
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
Image Super-Resolution: Historical Overview and Future Challenges. Super-Resolution Using Adaptive Wiener Filters. Locally Adaptive Kernel Regression for Space-Time Super-Resolution. Super-Resolution With Probabilistic Motion Estimation. Spatially Adaptive Filtering as Regularization in Inverse Imaging. Registration for Super-Resolution. Towards Super-Resolution in the Presence of Spatially Varying Blur. Toward Robust Reconstruction-Based Super-Resolution. Multi-Frame Super-Resolution from a Bayesian Perspective. Variational Bayesian Super Resolution Reconstruction. Pattern Recognition Techniques for Image Super-Resolution. Super-Resolution Reconstruction of Multi-Channel Images. New Applications of Super-Resolution in Medical Imaging. Practicing Super-Resolution: What Have We Learned?