Titlebook: Super Resolution of Images and Video; Aggelos K. Katsaggelos,Rafael Molina,Javier Mateos Book 2007 Springer Nature Switzerland AG 2007

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Aggelos K. Katsaggelos,Rafael Molina,Javier Mateoseman, Professor Emeritus of Neurobiology at the University of California, Berkeley. We have relied heavily on his work on preafference and attention to guide the development of our robot. In addition, we have used research from a myriad of different ?elds. Our huge thanks to all the researchers who’

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Book 2007play 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 pri

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1559-8136 ocess 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 im

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Bayesian Inference Models in Super Resolution,tion vector consisting of either the uncompressed LR observations g or both the LR-compressed observations y and the motion vectors v, and ., and . denote the HR image and motion vectors, respectively.

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Low-Resolution Image Formation Models, capturing a dynamic scene. As we have already mentioned in the previous chapters, the LR sequence may be compressed. We therefore first describe the case of no compression and then extend the formation models to include compression.

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Motion Estimation in Super Resolution,estimated. This motion estimation problem corresponds to finding the solution to Eq. (2.4) for the alternate approach and to the motion estimation part of the sequential methodology described in Chapter 2.

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