Code for HQSS: Shadow Removal by High-Quality Shadow Synthesis Paper

Dependencies

This code uses the following libraries

• python 3.7+
• pytorch 1.1+ & tochvision
• scikit-image
• focal-frequency-loss

Training code and data

Generate the training data using the original ISTD dataset and the code gettraindata.m

or download the training data from: GoogleDrive: ISTD | BaiduNetdisk: ISTD (Access code: 1111)

Your ~/AISTD/ folder should look like this

AISTD
├── train/
    ├── train_A/
    │   └── 90-1.png
    │   └── ...
    ├── train_B/
    │   └── ...
    └── ...

Pre-computed mask

We need to crop the original images to non-shadaow/shadow image. The suitable crop mask is computed as like G2R-ShadowNet

Please ensure mask pickle are exists. Lines 38/48 of datasets_decouple.py

You can download these pickle generated by us from here or use map_dict.py to produce the pickle.

Testing masks produced by BDRAR

GoogleDrive: ISTD

Train and test on the adjusted ISTD dataset

First, train a pseudo image generation network.

1. Set the paths: Lines 104/105 of train_shadow_generation_network.py
2. Set the paths: Lines 210-226 of utils.py
3. python train_shadow_generation_network.py

Second, train a shadow removal network.

1. Set the paths: Lines 134/135/217/222 of train_shadow_removal_network.py

2. python train_shadow_removal_network.py

Third, obtain test(predicted) images

1. Set the paths: Lines 30-34/56/57 of test.py/Lines 29-33/53/54 of test_SRD.py

2. python test.py/test_SRD.py

Evaluate

1. Set the paths of the shadow removal results and the dataset in evaluate.m/evaluate_SRD.m
2. Run evaluate.m/evaluate_SRD.m

The Best Models on ISTD

GoogleDrive: ISTD

The Best Models on SRD

GoogleDrive: SRD

Results of HQSS on ISTD

GoogleDrive: ISTD

Results of HQSS on SRD

GoogleDrive: ISTD

ISTD Results

Method	Shadow Region	Non-shadow Region	All
Le & Samaras (ECCV20)	10.4	2.9	4.0
G2R-ShadowNet (CVPR21)	8.9	2.9	3.9
HQSS (Ours)	8.48	2.82	3.72

Results in shadow and non-shadow regions are computed on each image first and then compute the average of all images in terms of RMSE.

Shadow Video Results

Method	RMSE	RMSE$_{40}$	PSNR	SSIM
Le & Samaras (ECCV20)	-	20.9	-	-
G2R-ShadowNet (CVPR21)	21.8	18.8	21.07	0.882
HQSS (Ours)	18.95	16.82	21.89	0.888

RMSE$_{40}$ denotes that the moving-shadow mask is computed with a threshold of 40. Other results use the moving-shadow mask which is computed with a threshold of 80.

Acknowledgments

Code is implemented based on G2R-ShadowNet