dshqd / exposure-fusion-shadow-removal

We propose a new method for effective shadow removal by regarding it as an exposure fusion problem.

Auto-exposure fusion for single-image shadow removal

We propose a new method for effective shadow removal by regarding it as an exposure fusion problem. Please refer to the paper for details: https://openaccess.thecvf.com/content/CVPR2021/papers/Fu_Auto-Exposure_Fusion_for_Single-Image_Shadow_Removal_CVPR_2021_paper.pdf.

Dataset

• ISTD ISTD
• ISTD+ ISTD+
• SRD

1. For data folder path (ISTD), train_A: shadow images, train_B: shadow masks, train_C: shadow free images, organize them as following:

--ISTD+
   --train
      --train_A
          --1-1.png
      --train_B
          --1-1.png 
      --train_C_fixed_official 
          --1-1.png
      --train_params_fixed  # generate later
          --1-1.png.txt
   --test
      --test_A
          --1-1.png
      --test_B
          --1-1.png
      --test_C
          --1-1.png
      --mask_threshold   # generate later
          --1-1.png

2. Run the code ./data_processing/compute_params.ipynb for exposure parameters generation. The result will be put in ./ISTD/train/train_params_fixed. Here, names train_C_fixed_official and train_params_fixed are for ISTD+ dataset, which are consitent with self.dir_C and self.dir_param in ./data/expo_param_dataset.py .
3. For testing masks, please run the code ./data_processing/test_mask_generation.py. The result will be put in ./ISTD/mask_threshold.

Pretrained models

We release our pretrained model (ISTD+, SRD) at models

pretrained model (ISTD) at models

Modify the parameter model in file OE_eval.sh to Refine and set ks=3, n=5, rks=3 to load the model.

Train

Modify the corresponding path in file OE_train.sh and run the following script

sh OE_train.sh

1. For the parameters:

      DATA_PATH=./Datasets/ISTD or your datapath
      n=5, ks=3 for FusionNet,
      n=5, ks=3, rks=3 for RefineNet.
      model=Fusion for FusionNet training,
      model=Refine for RefineNet training.

The trained models are saved in ${REPO_PATH}/log/${Name}, Name are customized for parameters setting.

Test

In order to test the performance of a trained model, you need to make sure that the hyper parameters in file OE_eval.sh match the ones in OE_train.sh and run the following script:

sh OE_eval.sh

1. The pretrained models are located in ${REPO_PATH}/log/${Name}.

Evaluation

The results reported in the paper are calculated by the matlab script used in other SOTA, please see evaluation for details. Our evaluation code will print the metrics calculated by python code and save the shadow removed result images which will be used by the matlab script.

Results

• Comparsion with SOTA, see paper for details.

• Penumbra comparsion between ours and SP+M Net

• Testing result

The testing results on dataset ISTD+, ISTD, SRD are:results

More details are coming soon

Bibtex

@inproceedings{fu2021auto,
      title={Auto-exposure Fusion for Single-image Shadow Removal}, 
      author={Lan Fu and Changqing Zhou and Qing Guo and Felix Juefei-Xu and Hongkai Yu and Wei Feng and Yang Liu and Song Wang},
      year={2021},
      booktitle={accepted to CVPR}
}