PyTorch implementation of IEEE TPAMI 2021 paper: "Deep Spatial-angular Regularization for Light Field Imaging, Denoising, and Super-resolution". The video demo is here
- Python 3.6.10
- PyTorch 1.7.1
- Matlab (for training/test data generation)
We provide MATLAB code for preparing the training and test data. Please first download light field datasets, and put them into corresponding folders in LFData.
We provide the pre-trained models for tasks 1 -> 49, 2 -> 49, and 4 -> 49 on the Lytro dataset. Enter the LFCA folder and run:
Task 1 -> 49
python lfca_test.py --measurementNum 1
Task 2 -> 49
python lfca_test.py --measurementNum 2
Task 4 -> 49
python lfca_test.py --measurementNum 4
Enter the LFCA folder and run:
python lfca_train.py
We provide MATLAB code for preparing the training and test data. Please first download light field datasets, and put them into corresponding folders in LFData. We used the same dataset, noise synthesis and preprocessing protocol as APA
We provide the pre-trained models for adding zero-mean Gaussian noise with the standard variance varying in the range of 10, 20, and 50 on the Lytro dataset. Enter the LFDN folder and run:
Noise level 10
python lfdn_test.py --sigma 10
Noise level 20
python lfdn_test.py --sigma 20
Noise level 50
python lfdn_test.py --sigma 50
Enter the LFCA folder and run:
python lfdn_train.py
We provide MATLAB code for preparing the training and test data. Please first download light field datasets, and put them into corresponding folders in LFData. We used the same dataset and protocol as those of Pseudo-4D to generate low-resolution LF images.
We provide the pre-trained models for tasks 2x and 4x on the Lytro dataset. Enter the LFSSR folder and run:
Task 2x
python lfssr_test.py --scaleFactor 2
Task 4x
python lfssr_test.py --scaleFactor 4
Enter the LFSSR folder and run:
python lfssr_train.py