This notebook compares neural networks trained via supervised and unsupervised learning. It also includes a comparison with linear shift-invariant denoising.
Natural images
This page contains code to reproduce the computational experiments in the tutorial Learn, Denoise and Discover: A Guide to Deep Denoising with an Application to Electron Microscopy , involving networks trained to denoise natural photographic images. The code is in the form of Jupyter notebooks that can be run in Google colab. These notebooks use pretrained models. The full code repository, available on GitHub, contains the models, as well as code to train them. .
This notebook visualizes the denoising strategy learned by a neural network for specific inputs, by visualizing equivalent filters that are equal to the network gradient with respect to its input.