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.
--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
./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
../data_processing/test_mask_generation.py
.
The result will be put in ./ISTD/mask_threshold
.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.
Modify the corresponding path in file OE_train.sh
and run the following script
sh OE_train.sh
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.
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
${REPO_PATH}/log/${Name}
.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.
The testing results on dataset ISTD+, ISTD, SRD are:results
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}
}