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.
| Published in | Mathematics and Computer Science (Volume 5, Issue 2) |
| DOI | 10.11648/j.mcs.20200502.12 |
| Page(s) | 56-63 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2020. Published by Science Publishing Group |
Image Denoising, Convolutional Neural Networks, Block Matching, Unsupervised Learning, Non-Local Self-Similarity
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APA Style
Abderraouf Khodja, Zhonglong Zheng, Yiran He. (2020). Similarity Noise Training for Image Denoising. Mathematics and Computer Science, 5(2), 56-63. https://doi.org/10.11648/j.mcs.20200502.12
ACS Style
Abderraouf Khodja; Zhonglong Zheng; Yiran He. Similarity Noise Training for Image Denoising. Math. Comput. Sci. 2020, 5(2), 56-63. doi: 10.11648/j.mcs.20200502.12
AMA Style
Abderraouf Khodja, Zhonglong Zheng, Yiran He. Similarity Noise Training for Image Denoising. Math Comput Sci. 2020;5(2):56-63. doi: 10.11648/j.mcs.20200502.12
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Download@article{10.11648/j.mcs.20200502.12,
author = {Abderraouf Khodja and Zhonglong Zheng and Yiran He},
title = {Similarity Noise Training for Image Denoising},
journal = {Mathematics and Computer Science},
volume = {5},
number = {2},
pages = {56-63},
doi = {10.11648/j.mcs.20200502.12},
url = {https://doi.org/10.11648/j.mcs.20200502.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mcs.20200502.12},
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.},
year = {2020}
}
TY - JOUR T1 - Similarity Noise Training for Image Denoising AU - Abderraouf Khodja AU - Zhonglong Zheng AU - Yiran He Y1 - 2020/06/03 PY - 2020 N1 - https://doi.org/10.11648/j.mcs.20200502.12 DO - 10.11648/j.mcs.20200502.12 T2 - Mathematics and Computer Science JF - Mathematics and Computer Science JO - Mathematics and Computer Science SP - 56 EP - 63 PB - Science Publishing Group SN - 2575-6028 UR - https://doi.org/10.11648/j.mcs.20200502.12 AB - 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. VL - 5 IS - 2 ER -
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