# shadow-removal

**Repository Path**: AI52CV/shadow-removal

## Basic Information

- **Project Name**: shadow-removal
- **Description**: Auto-Exposure Fusion for Single-Image Shadow Removal
论文：https://github.com/tsingqguo/exposure-fusion-shadow-removal
代码原地址：https://github.com/tsingqguo/exposure-fusion-shadow-removal
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 1
- **Forks**: 0
- **Created**: 2021-04-05
- **Last Updated**: 2021-04-05

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# 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://arxiv.org/abs/2103.01255

![Framework](./images/framework.png)

## Dataset

- ISTD https://github.com/DeepInsight-PCALab/ST-CGAN
- ISTD+ https://github.com/cvlab-stonybrook/SID
- SRD

## 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

```shell
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

```shell
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.

![Framework](./images/vis_compare.png)


- Penumbra comparsion between ours and SP+M Net

![Framework](./images/edge_comparsion.png)

- 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}
}
```