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• W3099794995 abstract "We propose an adaptive learning procedure to learn patch-based image priors for image denoising. The new algorithm, called the expectation-maximization (EM) adaptation, takes a generic prior learned from a generic external database and adapts it to the noisy image to generate a specific prior. Different from existing methods that combine internal and external statistics in ad hoc ways, the proposed algorithm is rigorously derived from a Bayesian hyper-prior perspective. There are two contributions of this paper. First, we provide full derivation of the EM adaptation algorithm and demonstrate methods to improve the computational complexity. Second, in the absence of the latent clean image, we show how EM adaptation can be modified based on pre-filtering. The experimental results show that the proposed adaptation algorithm yields consistently better denoising results than the one without adaptation and is superior to several state-of-the-art algorithms." @default.
• W3099794995 created "2020-11-23" @default.
• W3099794995 creator A5002282875 @default.
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• W3099794995 date "2016-10-01" @default.
• W3099794995 modified "2023-10-17" @default.
• W3099794995 title "Adaptive Image Denoising by Mixture Adaptation" @default.
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• W3099794995 doi "https://doi.org/10.1109/tip.2016.2590318" @default.
• W3099794995 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/27416593" @default.
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