TensorFlow V1 implementation (See DMSP page for Caffe and MatCovNet implementations, and DMSP-TF2 page for Tensorflow V2 implementation with super-resolution support))
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 Python and you need to install TensorFlow.
demo_DMSP.py: Includes an example for non-blind and noise-blind image deblurring.
DMSPDeblur.py: Implements MAP function for non-blind image deblurring. Use Python's help function to learn about the input and output arguments.
DAE_model: Includes DAE model and implementation.
data: Includes sample image(s).
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
