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- W2943669936 abstract "Leveraging current state-of-the-art denoisers to tackle other inverse problems in imaging is a challenging task, which has recently been the topic of significant research effort. In this paper, we present several contributions to this research front, based on two fundamental building blocks:1) the recently proposed plug-and-play framework, which allows combining iterative algorithms for imaging inverse problems with state-of-the-art image denoisers, used in black-box fashion; and 2) patch-based denoisers, using Gaussian mixture models (GMM). We exploit the adaptability of GMM to learn class-adapted denoisers, which opens the door to embedding a patch classification step in the algorithmic loop, yielding simultaneous restoration and semantic segmentation. We apply the proposed approach to several standard imaging inverse problems (deblurring, compressive sensing reconstruction, and super-resolution), obtaining results that are competitive with the state of the art." @default.
- W2943669936 created "2019-05-09" @default.
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- W2943669936 date "2019-12-01" @default.
- W2943669936 modified "2023-10-16" @default.
- W2943669936 title "Image Restoration and Reconstruction using Targeted Plug-and-Play Priors" @default.
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- W2943669936 doi "https://doi.org/10.1109/tci.2019.2914773" @default.
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