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• W2895893207 abstract "The challenging inverse problem of blind deblurring has been investigated thoroughly for natural images. Existing algorithms exploit edge-type structures, or similarity to smaller patches within the image, to estimate the correct blurring kernel. However, these methods do not perform well enough on natural stochastic textures (NST), which are mostly random and in general are not characterized by distinct edges and contours. In NST even small kernels cause severe degradation to images. Restoration poses therefore an outstanding challenge. In this work, we refine an existing method by implementing an anisotropic fractal model to estimate the blur kernel's power spectral density. The final kernel is then estimated via an adaptation of a phase retrieval algorithm, originally proposed for sparse signals. We further incorporate additional constraints that are specific to blur filters, to yield even better results. The latter are compared with results obtained by recently published blind deblurring methods." @default.
• W2895893207 created "2018-10-26" @default.
• W2895893207 creator A5002916521 @default.
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• W2895893207 date "2019-02-01" @default.
• W2895893207 modified "2023-09-23" @default.
• W2895893207 title "Blind Deblurring of Natural Stochastic Textures Using an Anisotropic Fractal Model and Phase Retrieval Algorithm" @default.
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• W2895893207 doi "https://doi.org/10.1109/tip.2018.2874291" @default.
• W2895893207 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30296232" @default.
• W2895893207 hasPublicationYear "2019" @default.
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