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- W172545020 abstract "Compressed sensing has received significant attention in recent years, primarily as a mathematical phenomenon. There has been, on the other hand, significantly less attention paid towards incorporation of compressed-sensing methodology into practical signal-processing applications. In this talk, we consider compressed sensing in the context of several image and video applications. The foundations of our discussion rest on a recent block-based strategy for compressed-sensing recovery of a single still image. In our approach, block-based random image sampling is coupled with a projection-driven compressed-sensing recovery that encourages sparsity in the domain of an image transform simultaneously with a smooth reconstructed image. The proposed approach yields images with quality that matches or exceeds that produced by a popular, yet computationally expensive, technique which minimizes total variation. This still-image reconstruction is then extended to multiview image sets, incorporating inter-image disparity compensation into the image-recovery process in order to take advantage of the high degree of inter-image correlation common to multiview scenarios. Finally, a similar approach is adopted for the reconstruction of video in which each frame has been subject to block-based random projections, and motion estimation and motion compensation across an entire group of frames informs the compressed-sensing recovery process." @default.
- W172545020 created "2016-06-24" @default.
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- W172545020 date "2006-01-01" @default.
- W172545020 modified "2023-10-18" @default.
- W172545020 title "Data Compression Conference (DCC'06)" @default.
- W172545020 doi "https://doi.org/10.1109/ieeeconf11561.2006" @default.
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