This book focuses on the super resolution of images and video. The authors' use of the term super resolution (SR) is used to describe the process of obtaining a high resolution (HR) image, or a sequence of HR images, from a set of low resolution (LR) observations. This process has also been referred to in the literature as resolution enhancement (RE). SR has been applied primarily to spatial and temporal RE, but also to hyperspectral image enhancement. This book concentrates on motion based spatial RE, although the authors also describe motion free and hyperspectral image SR problems. Also examined is the very recent research area of SR for compression, which consists of the intentional downsampling, during pre-processing, of a video sequence to be compressed and the application of SR techniques, during post-processing, on the compressed sequence. It is clear that there is a strong interplay between the tools and techniques developed for SR and a number of other inverse problems encountered in signal processing (e.g., image restoration, motion estimation). SR techniques are being applied to a variety of fields, such as obtaining improved still images from video sequences (video printing), high definition television, high performance color Liquid Crystal Display (LCD) screens, improvement of the quality of color images taken by one CCD, video surveillance, remote sensing, and medical imaging. The authors believe that the SR/RE area has matured enough to develop a body of knowledge that can now start to provide useful and practical solutions to challenging real problems and that SR techniques can be an integral part of an image and video codec and can drive the development of new coder-decoders (codecs) and standards.

Autorentext

Aggelos K. Katsaggelos received the Diploma degree in electrical and mechanical engineering from the Aristotelian University of Thessaloniki, Greece, in 1979 and the M.S. and Ph.D. degrees both in electrical engineering from the Georgia Institute of Technology, in 1981 and 1985, respectively. In 1985 he joined the Department of Electrical Engineering and Computer Science at Northwestern University, where he is currently professor. He was the holder of the Ameritech Chair of Information Technology (1997-2003). He is also the Director of the Motorola Center for Seamless Communications and a member of the Academic Affiliate Staff, Department of Medicine, at Evanston Hospital. Dr. Katsaggelos has served the IEEE in many capacities (i.e., current member of the Publication Board of the IEEE Proceedings, editor-in-chief of the IEEE Signal Processing Magazine 1997-2002, member of the Board of Governors of the IEEE Signal Processing Society 1999-2001, and member of the Steering Committees of the IEEE Transactions on Image Processing 1992-1997). He is the editor of Digital Image Restoration (Springer-Verlag 1991), co-author of Rate-Distortion Based Video Compression (Kluwer 1997), co-editor of Recovery Techniques for Image and Video Compression and Transmission, (Kluwer 1998), co-author of Super-resolution for Images and Video (Claypool, 2007) and Joint Source-Channel Video Transmission (Claypool, 2007). He is the co-inventor of twelve international patents, a Fellow of the IEEE, and the recipient of the IEEE Third Millennium Medal (2000), the IEEE Signal Processing Society Meritorious Service Award (2001), an IEEE Signal Processing Society Best Paper Award (2001), and an IEEE International Conference on Multimedia and Expo Paper Award (2006). He is a Distinguished Lecturer of the IEEE Signal Processing Society (2007-08). Rafael Molina was born in 1957. He received the degree in mathematics (statistics) in 1979 and the Ph.D. degree in optimal design in linear models in 1983. He became Professor of computer science and artificial intelligence at the University of Granada, Granada, Spain, in 2000. His areas of research interest are image restoration (applications to astronomy and medicine), parameter estimation in image restoration, super resolution of images and video, and blind deconvolution. He is currently the Head of the computer science and Artificial Intelligence Department at the University of Granada. Javier Mateos was born in Granada, Spain, in 1968. He received the degree in computer science in 1991 and the Ph.D. degree in computer science in 1998, both from the University of Granada. He was an Assistant Professor with the Department of Computer Science and Artificial Intelligence, University of Granada, from 1992 to 2001, and then he became a permanent Associate Professor. He is conducting research on image and video processing, including image restoration, image, and video recovery and super-resolution from (compressed) stills and video sequences.

Klappentext

This book focuses on the super resolution of images and video. The authors use of the term super resolution (SR) is used to describe the process of obtaining a high resolution (HR) image, or a sequence of HR images, from a set of low resolution (LR) observations. This process has also been referred to in the literature as resolution enhancement (RE). SR has been applied primarily to spatial and temporal RE, but also to hyperspectral image enhancement. This book concentrates on motion based spatial RE, although the authors also describe motion free and hyperspectral image SR problems. Also examined is the very recent research area of SR for compression, which consists of the intentional downsampling, during pre-processing, of a video sequence to be compressed and the application of SR techniques, during post-processing, on the compressed sequence. It is clear that there is a strong interplay between the tools and techniques developed for SR and a number of other inverse problems encountered in signal processing (e.g., image restoration, motion estimation). SR techniques are being applied to a variety of fields, such as obtaining improved still images from video sequences (video printing), high definition television, high performance color Liquid Crystal Display (LCD) screens, improvement of the quality of color images taken by one CCD, video surveillance, remote sensing, and medical imaging. The authors believe that the SR/RE area has matured enough to develop a body of knowledge that can now start to provide useful and practical solutions to challenging real problems and that SR techniques can be an integral part of an image and video codec and can drive the development of new coder-decoders (codecs) and standards.

Inhalt
Introduction.- Bayesian Formulation of Super-Resolution Image Reconstruction.- Low-Resolution Image Formation Models.- Motion Estimation in Super Resolution.- Estimation of High-Resolution Images.- Bayesian Inference Models in Super Resolution.- Super Resolution for Compression.