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• W2947002011 abstract "We describe a novel method for training high-quality image denoising models based on unorganized collections of corrupted images. The training does not need access to clean reference images, or explicit pairs of corrupted images, and can thus be applied in situations where such data is unacceptably expensive or impossible to acquire. We build on a recent technique that removes the need for reference data by employing networks with a blind spot in the receptive field, and significantly improve two key aspects: image quality and training efficiency. Our result quality is on par with state-of-the-art neural network denoisers in the case of i.i.d. additive Gaussian noise, and not far behind with Poisson and impulse noise. We also successfully handle cases where parameters of the noise model are variable and/or unknown in both training and evaluation data." @default.
• W2947002011 created "2019-06-07" @default.
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• W2947002011 date "2019-01-29" @default.
• W2947002011 modified "2023-09-27" @default.
• W2947002011 title "High-Quality Self-Supervised Deep Image Denoising" @default.
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• W2947002011 hasPublicationYear "2019" @default.
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