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
- ISTD https://github.com/DeepInsight-PCALab/ST-CGAN
- ISTD+ https://github.com/cvlab-stonybrook/SID
- SRD
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.
Modify the corresponding path in file OE_train.sh
and run the following script
sh OE_train.sh
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 cvlab-stonybrook/SID#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.
- 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: https://drive.google.com/drive/folders/1ubLj5r_ZMzWew4h2bNX7pQL6D62mM-dl?usp=sharing
More details are coming soon
@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}
}