[IPOL announce] new article: An Analysis and Implementation of the FFDNet Image Denoising Method

[announcements about the IPOL journal]

A new article is available in IPOL: http://

Matias Tassano, Julie Delon, and Thomas Veit,
An Analysis and Implementation of the FFDNet Image Denoising Method,
Image Processing On Line, 9 (2019), pp. 1–25.
https://doi.org/10.5201/ipol.2019.231

Abstract
FFDNet is a recent image denoising method based on a convolutional 
neural network architecture. In contrast to other existing neural 
network denoisers, FFDNet exhibits several desirable properties such as 
faster execution time and smaller memory footprint, and the ability to 
handle a wide range of noise levels effectively with a single network 
model. The combination between its denoising performance and lower 
computational load makes this algorithm attractive for practical 
denoising applications. In this paper we propose an open-source 
implementation of the method based on PyTorch, a popular machine 
learning library for Python. Code for the training of the network is 
also provided. We also discuss the characteristics of the architecture 
of this algorithm and we compare it to other similar methods.