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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

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.

See manuscript for details of the method.

This code runs in Python and you need to install TensorFlow.

Contents:

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).

License

<a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.