Image-Denoising-using-Deep-Learning

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Image-Denoising-using-Deep-Learning

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# Image-Denoising-using-Deep-Learning

In this repo I have implemented three different deep learning architectures for image denoising, 
REDNet; https://arxiv.org/pdf/1606.08921.pdf 
Multi-level Wavelet CNN (MWCNN); https://arxiv.org/pdf/1805.07071.pdf 
PRIDNet; https://arxiv.org/pdf/1908.00273.pdf

I found that PRIDNet was giving the best results as compared to the other two, giving me the best PSNR and SSIM scores along with best visualizations which were very close to the ground truth images.

![Screenshot](https://raw.githubusercontent.com/chintan1995/Image-Denoising-using-Deep-Learning/main/Results/Result_scores.png)

## PRIDNet Results

![Screenshot](https://raw.githubusercontent.com/chintan1995/Image-Denoising-using-Deep-Learning/main/Results/PRIDNet_Prediction_2.png)
![Screenshot](https://raw.githubusercontent.com/chintan1995/Image-Denoising-using-Deep-Learning/main/Results/PRIDNet_Prediction_1.png)
![Screenshot](https://raw.githubusercontent.com/chintan1995/Image-Denoising-using-Deep-Learning/main/Results/PRIDNet_Prediction_3.png)
Notice the level of detail in the eye ball.

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