DBSN | Unpaired Learning of Deep Image Denoising | Machine Learning library

DBSN is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow, Generative adversarial networks applications. DBSN has no bugs, it has no vulnerabilities and it has low support. However DBSN build file is not available. You can download it from GitHub.

Official PyTorch implementation of Unpaired Learning of Deep Image Denoising. We investigate the task of learning blind image denoising networks from an unpaired set of clean and noisy images. Assuming that the noise can be signal dependent but is spatially uncorrelated, we present a two-stage scheme by incorporating self-supervised learning and knowledge distillation. For self-supervised learning, we suggest a dilated blind-spot network (D-BSN) to learn denoising solely from real noisy images. Due to the spatial independence of noise, we adopt a network by stacking 1 × 1 convolution layers to estimate the noise level map for each image. Both the D-BSN and image-specific noise model (CNN_est) can be jointly trained via maximizing the constrained log-likelihood. Given the output of D-BSN and estimated noise level map, improved denoising performance can be further obtained based on the Bayes’ rule. As for knowledge distillation, we first apply the learned noise models to clean images to synthesize a paired set of training images, and use the real noisy images and the corresponding denoising results in the first stage to form another paired set. Illustration of our two-stage training scheme involving self-supervised learning and knowledge distillation.