├── EDA
└── Images_EDA.ipynb
├── Final.ipynb
├── LICENSE
├── Models
├── (Baseline)_REDNet_256x256.ipynb
├── MWCNN_256x256.ipynb
└── PRIDNet_256x256.ipynb
├── README.md
├── Results
├── PRIDNet_Prediction_1.png
├── PRIDNet_Prediction_2.png
├── PRIDNet_Prediction_3.png
└── Result_scores.png
└── Traditional Filter [NLM]
└── Traditional_Filter_256x256.ipynb
/LICENSE:
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1 | MIT License
2 |
3 | Copyright (c) 2025 Chintan
4 |
5 | Permission is hereby granted, free of charge, to any person obtaining a copy
6 | of this software and associated documentation files (the "Software"), to deal
7 | in the Software without restriction, including without limitation the rights
8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 |
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 |
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 |
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/README.md:
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1 | NOTE: In case you are not able to view the ipynb files on github, then paste its link on nbviewer,
2 | https://nbviewer.jupyter.org/
3 |
4 | # Image-Denoising-using-Deep-Learning
5 |
6 | In this repo I have implemented three different deep learning architectures for image denoising,
7 | REDNet; https://arxiv.org/pdf/1606.08921.pdf
8 | Multi-level Wavelet CNN (MWCNN); https://arxiv.org/pdf/1805.07071.pdf
9 | PRIDNet; https://arxiv.org/pdf/1908.00273.pdf
10 |
11 | I found that PRIDNet was giving the best results as compared to the other two, giving me the best PSNR and SSIM scores along with best visualizations which were very close to the ground truth images.
12 |
13 | 
14 |
15 | ## PRIDNet Results
16 |
17 | 
18 | 
19 | 
20 | Notice the level of detail in the eye ball.
21 |
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/Results/PRIDNet_Prediction_1.png:
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https://raw.githubusercontent.com/chintan1995/Image-Denoising-using-Deep-Learning/5161b4c076e01f8b977a8faba65fe8df4e238b43/Results/PRIDNet_Prediction_1.png
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/Results/PRIDNet_Prediction_2.png:
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https://raw.githubusercontent.com/chintan1995/Image-Denoising-using-Deep-Learning/5161b4c076e01f8b977a8faba65fe8df4e238b43/Results/PRIDNet_Prediction_2.png
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/Results/PRIDNet_Prediction_3.png:
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https://raw.githubusercontent.com/chintan1995/Image-Denoising-using-Deep-Learning/5161b4c076e01f8b977a8faba65fe8df4e238b43/Results/PRIDNet_Prediction_3.png
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/Results/Result_scores.png:
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https://raw.githubusercontent.com/chintan1995/Image-Denoising-using-Deep-Learning/5161b4c076e01f8b977a8faba65fe8df4e238b43/Results/Result_scores.png
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