[IPOL announce] new article: An Analysis and Implementation of the FFDNet Image Denoising Method
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Sun Jan 6 23:21:19 CET 2019
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.
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