Learning Enriched Features via Selective State Spaces Model for Efficient Image Deblurring
Since we are preparing to extend the paper to journals, the code will be published together with the journal paper after it is accepted. Our core code does not modify the SSM module and can be self-written according to our network architecture diagram.
To test the pre-trained models Google Drive
The visual result(ALGNet-32, trained only on GoPro) Google Drive.
If our code helps your research or work, please consider citing our paper. The following is a BibTeX reference:
@inproceedings{
gao2024learning,
title={Learning Enriched Features via Selective State Spaces Model for Efficient Image Deblurring},
author={Hu Gao and Bowen Ma and Ying Zhang and Jingfan Yang and Jing Yang and Depeng Dang},
booktitle={ACM Multimedia 2024},
year={2024},
}
Should you have any question, please contact [email protected]