Caffe and MatCovNet implementations (see DMSP-tensorflow for TensorFlow implementation)
Deep Mean-Shift Priors for Image Restoration (project page)
Siavash Arjomand Bigdeli, Meiguang Jin, Paolo Favaro, Matthias Zwicker
Advances in Neural Information Processing Systems (NIPS), 2017
Abstract:
In this paper we introduce a natural image prior that directly
represents a Gaussian-smoothed version of the natural image
distribution. We include our prior in a formulation of image restoration
as a Bayes estimator that also allows us to solve noise-blind image
restoration problems. We show that the gradient of our prior corresponds
to the mean-shift vector on the natural image distribution. In
addition, we learn the mean-shift vector field using denoising
autoencoders, and use it in a gradient descent approach to perform Bayes
risk minimization. We demonstrate competitive results for noise-blind
deblurring, super-resolution, and demosaicing.