Sébastien Herbreteau and Charles Kervrann
Here is the list of libraries you need to install to execute the code:
- Python 3.11
- Pytorch 2.2
- Torchvision 0.17
- Einops 0.7.0
To install in an environment using pip:
python -m venv .nlridge_env
source .nlridge_env/bin/activate
pip install /path/to/NL-Ridge
To denoise an image with NL-Ridge (remove --add_noise if it is already noisy):
python ./demo.py --sigma 15 --add_noise --in ./test_images/barbara.png --out ./denoised.png
Or use directly the Pytorch class NL-Ridge within your code:
m_nlridge = NLRidge() # instantiate the NL-Ridge class
y = 5 * torch.randn(1, 1, 100, 100) # image of pure Gaussian noise with variance 5^2
x_hat = m_nlridge(y, sigma=5, noise_type='gaussian-homoscedastic', constraints='affine', p1=7, p2=7, k1=18, k2=55, w=37, s=4)
(see the meaning of the parameters in the file nlridge.py, method set_parameters)
The average PSNR (dB) results of different methods on various datasets corrupted with Gaussian noise (sigma=15, 25 and 50). Best performance among each category is in bold. Second best is underlined.
The average PSNR (dB) results of NL-Ridge on Set12 dataset corrupted with additive white Gaussian noise.
| sigma | 2 | 5 | 10 | 15 | 20 | 25 | 35 | 50 |
|---|---|---|---|---|---|---|---|---|
| PSNR | 43.81 | 38.19 | 34.50 | 32.46 | 31.06 | 30.00 | 28.41 | 26.73 |
We want to emphasize that NL-Ridge is relatively fast. We report here the execution times of different algorithms. It is provided for information purposes only, as the implementation, the language used and the machine on which the code is run, highly influence the results. The CPU used is a 2,3 GHz Intel Core i7 and the GPU is a GeForce RTX 2080 Ti. NL-Ridge has been entirely written in Python with Pytorch so it can run on GPU unlike its traditional counterparts.
Running time (in seconds) of different methods on images of size 256x256. Run times are given on CPU and GPU if available.
| BM3D | NL-Bayes | NL-Ridge | Self2Self | DnCNN | LIDIA | |
|---|---|---|---|---|---|---|
| CPU | 1.68 | 0.21 | 0.66 | n/a | 0.35 | 21.08 |
| GPU | n/a | n/a | 0.162 | 3877 | 0.007 | 1.184 |
This work was supported by Bpifrance agency (funding) through the LiChIE contract. Computations were performed on the Inria Rennes computing grid facilities partly funded by France-BioImaging infrastructure (French National Research Agency - ANR-10-INBS-04-07, “Investments for the future”).