DreamClear presents a cutting-edge approach to real-world image restoration, ensuring high capacity and privacy-safe dataset curation. Our collaborative research brings together experts in AI to unveil sophisticated techniques that enhance image quality without compromising user privacy, setting a new standard in data ethics and restoration technology.
DreamClear is a cutting-edge solution for high-capacity real-world image restoration, developed with a focus on privacy-safe dataset curation. This project aims to enhance the quality of low-quality (LQ) images through advanced techniques, making it ideal for researchers and developers in the field of image processing and restoration.
Key Features:-
- High-Capacity Restoration: Achieve remarkable improvements in image quality to restore clarity and detail in low-resolution images.
- Privacy-Safe Dataset Curation: Emphasizes the importance of privacy by ensuring dataset handling is secure and responsible.
- User-Friendly Inference Codes: The upcoming releases will offer streamlined inference codes and accessible demo versions for improved usability.
Recent Updates:-
- October 2024: Released segmentation and detection code alongside pre-trained models.
- October 2024: Introduced the
RealLQ250benchmark comprising 250 real-world low-quality images, supporting comprehensive image restoration testing. - October 2024: Recent updates include code for training and inference at resolutions from 256 to 1024.
Real-World Results
DreamClear has demonstrated significant efficacy in real-world image restoration. For visual examples and results achieved using our method, see below:
How to Get Involved
If you find DreamClear helpful for your projects, please give us a star on GitHub to support ongoing development. Your contributions and feedback are invaluable as we continue to improve this tool.
For full documentation, including detailed training instructions and evaluation benchmarks, please refer to the README in this repository.
Citation
If you find this work beneficial for your research, consider citing us:
@article{ai2024dreamclear,
title={DreamClear: High-Capacity Real-World Image Restoration with Privacy-Safe Dataset Curation},
author={Ai, Yuang and Zhou, Xiaoqiang and Huang, Huaibo and Han, Xiaotian and Chen, Zhengyu and You, Quanzeng and Yang, Hongxia},
journal={Advances in Neural Information Processing Systems},
year={2024}
}
We appreciate your interest in DreamClear, and we look forward to your contributions and collaborations!