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- W2899117438 abstract "This paper aims to exploit the full potential of gradient-based methods, attempting to explore a simple, robust yet discriminative image prior for blind deblurring. The specific contributions are three-fold: Above all, a pure gradient-based heavy-tailed model is proposed as a generalized integration of the normalized sparsity and the relative total variation. On the second, a plug-and-play algorithm is deduced to alternatively estimate the intermediate sharp image and the nonparametric blur kernel. With the numerical scheme, image estimation is simplified to an image smoothing problem. Lastly, a great many experiments are performed accompanied with comparisons with state-of-the-art approaches on synthetic benchmark datasets and real blurry images in various scenarios. The experimental results show well the effectiveness and robustness of the proposed method." @default.
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- W2899117438 date "2018-01-01" @default.
- W2899117438 modified "2023-10-16" @default.
- W2899117438 title "Blind Deblurring Using Discriminative Image Smoothing" @default.
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- W2899117438 doi "https://doi.org/10.1007/978-3-030-03398-9_42" @default.
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