CT Scan Denoising Model Development

I am working on a denoising project involving 25 CT scan datasets from different patients. Each dataset contains a series of images that need to have artificial noise added for testing. The project aims to train a denoising model at three different noise levels and then evaluate the results both qualitatively and quantitatively. For the quantitative assessment, we’ll use PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity Index) metrics.
The denoising methods we’ll apply in this project include:
* BM3D
* BM4D
* SCUNet
* DnCNN
Requirements for this project include Python programming skills and a solid understanding of deep learning principles.