Deep Mean-Shift Priors for Image Restoration
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DAEs fixed net obj(Matlab) reference issue Apr 6, 2018
data added support for MatConvNet Sep 29, 2017
DMSPDeblur.m added support for MatConvNet Sep 29, 2017 Update May 28, 2018
computePSNR.m added support for MatConvNet Sep 29, 2017
demo.m added support for MatConvNet Sep 29, 2017

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


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.


See manuscript for details of the method.

This code runs in Matlab and you need to install either MatCaffe or MatConvNet.


demo.m: Includes an example for non-blind and noise-blind image deblurring.

DMSPDeblur.m: Implements MAP function for non-blind image deblurring. Use Matlab's help function to learn about the input and output arguments.

DAEs: Includes DAE models and function handles (in Caffe and matconvnet).

data: Includes sample image and blur kernels.