exposure-fusion-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://arxiv.org/abs/2103.01255

Dataset

Model

We release our pretrained model (ISTD+, SRD) at https://drive.google.com/drive/folders/1riTtYvHpffYu-nqSizqSF4fhbZ2txrp5?usp=sharing

pretrained model (ISTD) at https://drive.google.com/drive/folders/1qECA9EjUSLMtUpN5fFZMjltQPzjp2gL9?usp=sharing

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

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_test.sh

The results reported in the paper are calculated by the matlab script used in other SOTA, please see https://github.com/cvlab-stonybrook/SID/issues/1 for details. Our evaluation code will print the metrics calculated by python code and save the result images which will be used by the matlab script.

Results

  • Comparsion with SOTA, see paper for details.

vis_compare

  • Penumbra comparsion between ours and SP+M Net

edge_comparsion

  • Testing result

The testing results on dataset ISTD+, ISTD, SRD are:
https://drive.google.com/drive/folders/1ubLj5r_ZMzWew4h2bNX7pQL6D62mM-dl?usp=sharing

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}
}

GitHub