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Similarity Noise Training for Image Denoising
Mathematics and Computer Science
Volume 5, Issue 2, March 2020, Pages: 56-63
Received: May 11, 2020; Accepted: May 25, 2020; Published: Jun. 3, 2020
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Authors
Abderraouf Khodja, College of Mathematics and Computer Science, Zhejiang Normal University, Jinhua, China
Zhonglong Zheng, College of Mathematics and Computer Science, Zhejiang Normal University, Jinhua, China
Yiran He, College of Mathematics and Computer Science, Zhejiang Normal University, Jinhua, China
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Abstract
Deep learning has attracted a lot of attention lately, thanks. Thanks to its high modeling performance, technological advancement, and big data for training, deep learning has achieved a remarkable improvement in both high and low-level vision tasks. One crucial aspect of the success of a deep learning-based model is an adequate large data set for fueling the training stage. But in many cases, well-labeled large data is hard to acquire. Recent works have shown that it is possible to optimize denoising models by minimizing the difference between different noise instances of the same image. Yet, it is not a common practice to collect data with different noise instances of the same sample. Addressing this issue, we propose a training method that enables training deep convolutional neural network models for Gaussian denoising to be trained in cases of no ground truth data. More specifically, we propose to train a deep learning-based denoising model using only a single noise instance. With that in mind we develop a non-local self-similarity noise training method that uses only one noise instance.
Keywords
Image Denoising, Convolutional Neural Networks, Block Matching, Unsupervised Learning, Non-Local Self-Similarity
To cite this article
Abderraouf Khodja, Zhonglong Zheng, Yiran He, Similarity Noise Training for Image Denoising, Mathematics and Computer Science. Vol. 5, No. 2, 2020, pp. 56-63. doi: 10.11648/j.mcs.20200502.12
Copyright
Copyright © 2020 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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