Incorporating-Polarization-aware-Physical-Inference-and-SAM-for-Image-Dehazing

Inference

python execute/infer_full.py -r checkpoint/full.pth --data_dir <path_to_input_data> --result_dir <path_to_result_data> default

Visualization

Since the file format we use is .npy, we provide scrips for visualization:

• use scripts/visualize_polarized_img.py to visualize the polarized hazy images
• use scripts/visualize_img.py to visualize the unpolarized hazy images and synthetic results
• use scripts/visualize_real_img.py to visualize real results

Preprocess your own data

Note that in our code implementation, the network input contains three components: {I_alpha, I_hat, delta_I_hat}:

• I_alpha: three polarized hazy images
• I_hat: the calculated unpolarized hazy image
• delta_I_hat: the calculated unpolarized hazy image multiplied by the degree of polarization

So, we should preprocess the data first to get the network input:

• for synthetic images (training and inference)

  1. use scripts/preprocess_cityscapes.py to preprocess the Cityscapes Dataset (require leftImg8bit, gtFine, and leftImg8bit_transmittanceDBF) for generating {image, depth, segmentation} (or choose other source dataset if you want)
  2. use scripts/make_dataset.py to generate the synthetic dataset from {image, depth, segmentation}

• for real images (inference only)

  1. use scripts/make_real_dataset_from_raw_format.py to generate the real dataset from images (in .raw format) captured by a polarization camera (Lucid Vision Phoenix polarization camera (RGB) in our paper)

Training your own model

1. python sam.py
   
   python execute/train.py -c config/subnetwork1.json
   

• All config files (config/*.json) and the learning rate schedule function (MultiplicativeLR) at get_lr_lambda in utils/util.py could be edited