Deep Mean-shift prior is an approach for image restoration developed based on the estimate of the natural image probability distribution smoothed by a Gaussian kernel.
It is created as a Bayes estimator that boost the Gaussian smoothed density of natural images. Its framework is an example of Bayesian inference using deep learning.
Deep Mean-shift prior allows users to solve noise-blind restoration problems. It builds on denoising
autoencoders (DAEs). It provides image restoration techniques based on gradient-descent risk minimization with competitive results for noise-blind image deblurring, super-resolution and demosaicing
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