This project implements the AOD-Net : All-in-One Network for Dehazing for image dehazing using Python and PyTorch. The model is capable of removing haze, smoke, and water impurities from images."
The repository includes:
Python 3.6, Pytorch 0.4.0 and other common packages
To build synthetic hazy dataset, you'll also need:
$ cd make_dataset
$ python create_train.py --nyu {Your NYU Depth V2 path} --dataset {Your trainset path}
$ python random_select.py --traindir {Your trainset path} --valdir {Your valset path}
$ python train.py --dataroot {Your trainset path} --valDataroot {Your valset path} --cuda
$ python test.py --input_image /test/canyon1.jpg --model /model_pretrained/AOD_net_epoch_relu_10.pth --output_filename /result/canyon1_dehaze.jpg --cuda