- folder base: model and basic dataset
- Each reconstruction algorithm configuration files
- datasets (data preprocessing)
- models (reconstruction algorithms)
- utils (universial function, such as PSNR SSIM Calculation)
- train.py (Model Training)
- test_deeplearning.py (Testing end to end deep learning algorithms or deep unfolding algorithms on grayscale or colored simulation dataset)
- test_iterative.py (Testing iterative algorithms or plug and play algorithms on grayscale simulation dataset)
- test_color_iterative.py (Testing iterative algorithms or plug and play algorithms on colored simulation dataset)
- real_data (Testing real data)
- params_flops.py (Statistics of model parameters and FLOPs)
- video_gif (images to video and images to gif transfer)
- onnx_tensorrt (onnx, tensorrt model transfer and testing)
- mask (mask for different models)
- simulation (Testing results for 6 benchmark grayscale simulation dataset)
- middle_scale (Testing results for 6 benchmark colored simulation dataset)
- real_data (real data, compress ration from 10 to 50)