Shadow-Aware Dynamic Convolution for Shadow Removal
We propose a Shadow-Aware Dynamic Convolution (SADC) to resolve the contradiction between the shadow region and non-shadow region for shadow removal. Please refer to the paper for details.
Pretrained Models & Testing Results
We conduct experiments on the ISTD dataset and SRD dataset without reshaping, i.e., 640x480 for the ISTD dataset and 840x640 for the SRD dataset.
Modify the dataset path and experiments name in file
script/train.sh and run the following script
cd script sh ./train.sh GPU_ID VISDOM_PORT_ID
The usage of visdom can be referred to link. Once visdom is enabled in the python environment, the visualization of the training can be referred to
Similar to the training phase, please modify the dataset path and experiments name in file
script/test.sh and run the following script, and make sure the name used in testing exists in the checkpoint folder. Testing MATLAB code is borrowed from G2R Network.
cd script sh ./test.sh GPU_ID
Note: the testing size for the ISTD dataset and SRD dataset is different, please modify the size to match the input size. The SRD dataset requires large RAM of GPU when testing, please switch to CPU testing (as we do in our experiments) if no such large GPU is available.
The comparison between our method and current SOTA methods is shown as below,