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.
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