This repo is for Jinan University 2023 Spring Mathematical Modeling Project: Color image denosing
Original paper: Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
We are using ModelArts AI platform provided by 广州人工智能公共算力中心.
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Using user ma-user
EulerOS 2.0 (SP8), CANN-6.3.RC1.alpha001- 24 ARM cores
- 96 GB RAM
- Ascend 910 (32GB HBM) AI Processor (npu) * 1
- Python 3.7.10
- numpy 1.21.6
- opencv-contrib-python 4.7.0.72
- torch 1.11
- torchvision 0.12.0
- torch-npu 1.11
- create_patch.py : create patches for training (default 40*40)
- train_color.py : train model for color image denoising
- dataset_color.py : load the data for training
- denoise_color.py : dednoise the noisy image
- psnr_experiment.py : calculata the average psnr between denoised images and original images
You can check the folder dataset for the download link for the datasets we used for training or testing.
Warning: The code in this repository is written for the NPU platform. If you are using CUDA acceleration, you will need to modify some of the code in order to run it.
- Clone the repo
git clone https://github.com/c0rnP1ex/DnCNN.git-
Download the datasets, you can find the download links of them in DataSets/dataset.md
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Create patches for training, you may need to modify the create_patch.py
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Train the model, you may need to modify the train_color.py
Just run the denoise_color.py, it can denoise single color image.
Run the psnr_experiment.py, it can calculate the average psnr between denoised images and original images. Make sure you set the correct path for the noisy images and original images.
We denoise an image of an adorable cat with noise level 25.
