Caffe and MatCovNet implementations (see DMSP-tensorflow for TensorFlow V1 and DMSP-TF2 page for Tensorflow V2 implementation with super-resolution support)

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.