HOSVD_denoise_patch

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HOSVD_denoise_patch

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HOSVD denoising algorithm with different patch method

HOSVD_denoise_patch 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# Image denoising based on global image similar patches searching and HOSVD to patches tensor

### Cite this project/paper

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

## Install
`git clone https://www.github.com/KazukiAmakawa/HOSVD_denoise_patch `

## Usage
### Easy Mode
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                     |

* 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

* 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                      |

### Full Mode
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<br/>
For more help, submit issue in this project or connect this E-mail: GeorgeKahChen@gmail.com

If you want to using this project as close source project, please connect us.

## Reference
[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

## Contact

If you have any problem about the code, please contact: (Mr.) Huayan Chen: GeorgeKahChen@gmail.com

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