Caffe and MatCovNet implementations (see DMSP-tensorflow for TensorFlow implementation)
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.
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.