https://github.com/g0bel1n/mc_ising
MC_Ising is an implementation of Monte-Carlo methods for image denoising using the Ising Model
https://github.com/g0bel1n/mc_ising
image-denoising ising-model-2d monte-carlo
Last synced: 3 months ago
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MC_Ising is an implementation of Monte-Carlo methods for image denoising using the Ising Model
- Host: GitHub
- URL: https://github.com/g0bel1n/mc_ising
- Owner: g0bel1n
- Created: 2022-04-26T18:31:58.000Z (about 3 years ago)
- Default Branch: hyperpriors
- Last Pushed: 2022-05-10T20:41:20.000Z (about 3 years ago)
- Last Synced: 2025-01-22T09:52:58.894Z (5 months ago)
- Topics: image-denoising, ising-model-2d, monte-carlo
- Language: Python
- Homepage:
- Size: 39.2 MB
- Stars: 0
- Watchers: 1
- Forks: 1
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
Image denoising using ISING MODEL and GIBBS SAMPLING
Mathis Matthieu, Benjamin Pipaud, Lucas Saban. MC course @Ensae
---
Ising Model denoising using hyperparameter estimation and MCMC techniques.
## Examples
With systematic scan on the left and randomized scan on the right:
## ⚡️ On Boarding
```shell
pip install -r requirements.txt
```
Then :
```shell
main.py --findsigma True --alpha 0.0 #etc...
```
The arguments available are :
- `alpha` : The alpha parameter of the Ising Model, default value is 0
- `beta`: The beta parameter of the Ising Model, default value is 1.3
- `sigma` : Variance of the gaussian noise, default value is 179 (for 8 bit images)
- `findsigma` : If set to True, the denoising will be done without being given the value of sigma, default value False
- `g` : If set to true a gif will be produced. Default value to True.
- `b` : Number of burn in steps. Default to 40
- `ns`: Number of sampling steps. Default to 5
- `imp`: Path of the raw image. Default to 'data/input/test_img.jpeg'