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learn_prox_ops - Implementation of Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems

  •    Python

This repository provides the implementation of our paper Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems (Tim Meinhardt, Michael Möller, Caner Hazirbas, Daniel Cremers, ICCV 2017) [https://arxiv.org/abs/1704.03488]. All results presented in our work were produced with this code. Additionally we provide a TensorFlow implementation of the denoising convolutional neural network (DNCNN) introduced in Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising [https://arxiv.org/abs/1608.03981].

ProximalOperators.jl - Proximal operators for nonsmooth optimization in Julia

  •    Julia

Proximal operators for nonsmooth optimization in Julia. This package can be used to easily implement proximal algorithms for convex and nonconvex optimization problems such as ADMM, the alternating direction method of multipliers. See the documentation on how to use the package.





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