PyTorch implementation of CVPR 2020 paper: "Light Field Spatial Super-resolution via Deep Combinatorial Geometry Embedding and Structural Consistency Regularization".
- Python 3.6
- PyTorch 1.1
- 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
.
To reproduce final SR reconstruction results in the paper, run:
python demo_LFSSR.py --model_dir pretrained_models --save_dir results --scale 2 --test_dataset Kalantari --angular_num 7 --save_img 1 --crop 1 --feature_num 64 --layer_num 5 2 2 3 --layer_num_refine 3
To reproduce intermediate all-to-one model results in the paper, run:
python demo_ATO.py --model_dir pretrained_models --save_dir results --scale 2 --test_dataset Kalantari --angular_num 7 --save_img 1 --crop 0 --feature_num 64 --layer_num 5 2 2 3
The training code will be released soon.