Guo, J., Chen, H., Shen, Z. et al. Image denoising based on global image similar patches searching and HOSVD to patches tensor. EURASIP J. Adv. Signal Process. 2022, 19 (2022). https://doi.org/10.1186/s13634-021-00798-4
git clone https://www.github.com/KazukiAmakawa/HOSVD_denoise_patch
Detemine the parameters in main.m and run the project
Parameter Table
| parameter | Intro |
|---|---|
| para_sigma | Sigma of noise |
| para_betta | Relaxation parameter (Learning Rate) |
| para_gamma | Scaling factor controlling |
| para_patch_size | Size of every patch |
| para_patch_stack | Length of the tensor in SVD processing |
| para_iteration | Iteration times |
| test_switch | Print the image on screen or not |
| patch_method | Patch Analysis method |
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We had found best para_betta and para_gamma in sigma = 10, 30 and 50.
-
We just trained the GMM pre-trained model in 7, 8, 9 and 10 patch size
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patch_method list
| Code | Intro |
|---|---|
| 1 | Original method NNM patch search |
| 21/22 | Pre-trained Gaussian Mixture Model method only |
| 31/32/33/34/35 | Pre-trained GMM and K-means method |
| 21/31 | BFS after classification search |
| 22/32 | Virtual reference patch method |
| 33 | All patch in class combined tensor |
| 34 | Reference patch in classes to build tensor |
| 35 | Double port search on image |
You can using HOSVD_Denoising.m directly for your denoising.
Or, add new patch methods in Block_matching.m to test your new patch method with HOSVD
Source Code: https://www.github.com/KazukiAmakawa/HOSVD
For more help, submit issue in this project or connect this E-mail: GeorgeKahChen@gmail.com
Copyright (c) by KazukiAmakawa(Huayan Chen), all right reserved.
GNU GENERAL PUBLIC LICENSE Version 3
If you want to using this project as close source project, please connect us.
[1] GARW: Group Attribute Random Walk: https://www.github.com/KazukiAmakawa/GARW-Class
(This is a project working on non-linear classify method in deep learning with pytorch structure, still developing)
[2] F. Chen, L. Zhang and H. Yu, "External Patch Prior Guided Internal Clustering for Image Denoising," 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, 2015, pp. 603-611. doi: 10.1109/ICCV.2015.76
(We used GMM and K-means solution from code of this paper)
[3] R. Movchan and Z. Shen, "Adaptive thresholding hosvd algorithm with iterative regularization for image denoising," 2017 IEEE International Conference on Image Processing (ICIP), Beijing, 2017, pp. 2991-2995. doi: 10.1109/ICIP.2017.8296831
If you have any problem about the code, please contact: (Mr.) Huayan Chen: GeorgeKahChen@gmail.com