Combined controlled LDM (CLDM) with Restormer/SwinIR/SCUnet/VIT/Resnet/CNN for imaging restoration
It is a enhanced version (multi stage 1 model) of DiffBIR
Still under construction
A modified version of code of the Paper:
https://doi.org/10.1007/978-3-031-72089-5_39
@InProceedings{Li_MoCoDiff_MICCAI2024, author = { Li, Feng and Zhou, Zijian and Fang, Yu and Cai, Jiangdong and Wang, Qian}, title = { { MoCo-Diff: Adaptive Conditional Prior on Diffusion Network for MRI Motion Correction } }, booktitle = {proceedings of Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024}, year = {2024}, publisher = {Springer Nature Switzerland}, volume = {LNCS 15006}, month = {October}, page = {411 -- 421} } Which based on these codes: Mainly based on DiffBIR
https://github.com/lllyasviel/ControlNet
@misc{zhang2023adding, title={Adding Conditional Control to Text-to-Image Diffusion Models}, author={Lvmin Zhang and Anyi Rao and Maneesh Agrawala}, booktitle={IEEE International Conference on Computer Vision (ICCV)} year={2023}, }
https://github.com/swz30/Restormer
@inproceedings{Zamir2021Restormer, title={Restormer: Efficient Transformer for High-Resolution Image Restoration}, author={Syed Waqas Zamir and Aditya Arora and Salman Khan and Munawar Hayat and Fahad Shahbaz Khan and Ming-Hsuan Yang}, booktitle={CVPR}, year={2022} }
https://github.com/JingyunLiang/SwinIR
@article{liang2021swinir,
title={SwinIR: Image Restoration Using Swin Transformer},
author={Liang, Jingyun and Cao, Jiezhang and Sun, Guolei and Zhang, Kai and Van Gool, Luc and Timofte, Radu},
journal={arXiv preprint arXiv:2108.10257},
year={2021}
}
https://github.com/XPixelGroup/DiffBIR @misc{lin2024diffbir, title={DiffBIR: Towards Blind Image Restoration with Generative Diffusion Prior}, author={Xinqi Lin and Jingwen He and Ziyan Chen and Zhaoyang Lyu and Bo Dai and Fanghua Yu and Wanli Ouyang and Yu Qiao and Chao Dong}, year={2024}, eprint={2308.15070}, archivePrefix={arXiv}, primaryClass={cs.CV} }
https://github.com/xinntao/Real-ESRGAN @InProceedings{wang2021realesrgan, author = {Xintao Wang and Liangbin Xie and Chao Dong and Ying Shan}, title = {Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data}, booktitle = {International Conference on Computer Vision Workshops (ICCVW)}, date = {2021} }
https://github.com/cszn/SCUNet @article{zhang2023practical, author = {Zhang, Kai and Li, Yawei and Liang, Jingyun and Cao, Jiezhang and Zhang, Yulun and Tang, Hao and Fan, Deng-Ping and Timofte, Radu and Gool, Luc Van}, title = {Practical Blind Image Denoising via Swin-Conv-UNet and Data Synthesis}, journal = {Machine Intelligence Research}, DOI = {10.1007/s11633-023-1466-0}, url = {https://doi.org/10.1007/s11633-023-1466-0}, volume={20}, number={6}, pages={822--836}, year={2023}, publisher={Springer} }
https://github.com/cszn/BSRGAN @inproceedings{zhang2021designing, title={Designing a Practical Degradation Model for Deep Blind Image Super-Resolution}, author={Zhang, Kai and Liang, Jingyun and Van Gool, Luc and Timofte, Radu}, booktitle={IEEE International Conference on Computer Vision}, pages={4791--4800}, year={2021} }