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Full Description
Block-Based Compressed Sensing of Images and Video overviews the emerging concept of compressed sensing (CS) with a particular focus on recent proposals for its use with a variety of imaging media, including still images, motion video, as well as multiview images and video. Throughout, it considers a variety of CS reconstruction techniques proposed in recent literature and examines relative performance of several prominent reconstruction algorithms for each of the various imagery formats.
Particular emphasis is placed on block-based measurement and reconstruction which has the advantages of significantly reduced memory and computation with respect to other approaches relying on full-frame CS measurement operators. The book employs extensive experimental comparisons to evaluate various prominent reconstruction algorithms for still-image, motion-video, and multiview scenarios in terms of both reconstruction quality as well as computational complexity.
It is not intended to serve as an indepth tutorial on the theory or mathematics of compressed sensing. The coverage of CS theory is brief, while the specifics of the application of block-based compressed sensing (BCS) to natural imagery consume the bulk of the discussion.
Contents
Acronyms. 1: Introduction 2: Compressed Sensing 3: Block-Based Compressed Sensing for Still Images 4: Block-Based Compressed Sensing for Video 5: Multihypothesis Prediction for Compressed Sensing of Video 6: Compressed Sensing of Multiview Image and Video. Conclusions. Acknowledgements. References
