TurbulentWater
Code for "Learning to See through Turbulent Water" WACV 2018
Data and pretrained models are available at http://cseweb.ucsd.edu/~viscomp/projects/WACV18Water/
Instructions
- download train.zip, val.zip and test.zip from http://cseweb.ucsd.edu/~viscomp/projects/WACV18Water/
- unzip train.zip into DATAROOT/Water
- unzip val.zip into DATAROOT/Water (note these images correspond to the ImageNet test set)
- unzip test.zip into DATAROOT/Water_Real
- dowanload the origional ImageNet training and test sets to DATAROOT/ImageNet/
Training
python main.py --dataroot DATAROOT
Testing
python main.py --test --dataroot DATAROOT --exp-name warp_L1 --no-color-net --weight-Y-VGG 0
python main.py --test --dataroot DATAROOT --exp-name warp_L1VGG --no-color-net
python main.py --test --dataroot DATAROOT --exp-name color_L1VGG --no-warp-net --weight-Z-Adv 0
python main.py --test --dataroot DATAROOT --exp-name color_L1VGGAdv --no-warp-net
python main.py --test --dataroot DATAROOT --exp-name both_L1VGGAdv
python main.py --test --dataroot DATAROOT --exp-name warp_L1VGG_synth --no-color-net
Minor modifications from the paper
- added a reconstruction (L1) and perceptual loss (VGG) to the output of the WarpNet
- all networks are trained for 3 epochs with all the losses from the start, with a constant learning rate of .0002
- all hyper-parameter weights are set to 1.0 except the perceptual and adversarial losses of the final output which are set to 0.5 and 0.2 respectively
- replaced transposed convolutions with nearest neighbor upsampling
- replaced instance normalization (and final layer denormalization) with group normalization
- added a 7x7 conv layer (to project from RGB to features and vice versa) to the begining and end of the networks
Results
