A Wavelet-Domain Consistency-Constrained Compressive Sensing Framework Based on Memory-Boosted Guidance Filtering
The new version (With a non-negative constraint on regularization parameters) of our codes are publicly released, which can be download from GoogleDrive and FeiShu and BaiduYun.
The pre-trained models of the new version can be download from BaiduYun.
The old version (Without a non-negative constraint on regularization parameters) of our codes are publicly released, which can be download from GoogleDrive and FeiShu and BaiduYun.
The pre-trained models of the old version can be download from BaiduYun.
The training dataset can be download from BaiduYun.
*****Our codes are benefited from the project of OPINE-Net. Thank the authors of OPINE-Net for sharing their codes.
If you find our work useful for your research, please cite us:
@article{wang2024MDGF-Net,
title={A Wavelet-Domain Consistency-Constrained Compressive Sensing Framework Based on Memory-Boosted Guidance Filtering},
author={Xinlu Wang, Lijun Zhao*, Jinjing Zhang, Anhong Wang, Huihui Bai},
journal={IEEE Transactions on Instrumentation and Measurement},
volume={xx},
pages={xx-xx},
year={2024},
publisher={IEEE}
}
@inproceedings{wang2023wdu,
title={WDU-Net: Wavelet-Guided Deep Unfolding Network for Image Compressed Sensing Reconstruction},
author={Xinlu Wang, Lijun Zhao*, Jinjing Zhang, Yufeng Zhang, Anhong Wang},
booktitle={Chinese Conference on Pattern Recognition and Computer Vision (PRCV)},
pages={79--91},
year={2023},
organization={Springer}
}