HLNet, which secured fourth place in the NTIRE 2024 Challenge on Bracketing Image Restoration and Enhancement - Track 2 BracketIRE+ Task, has now been accepted by the CVPR 2024 Workshop.
In real-world scenarios, due to a series of image degradations, obtaining high-quality, clear content photos is challenging. While significant progress has been made in synthesizing high-quality images, previous methods for image restoration and enhancement often overlooked the characteristics of different degradations. They applied the same structure to address various types of degradation, resulting in less-than-ideal restoration outcomes. Inspired by the notion that high/low frequency information is applicable to different degradations, we introduce HLNet, a Bracketing Image Restoration and Enhancement method based on high-low frequency decomposition. Specifically, we employ two modules for feature extraction: shared weight modules and non-shared weight modules. In the shared weight modules, we use SCConv to extract common features from different degradations. In the non-shared weight modules, we introduce the High-Low Frequency Decomposition Block (HLFDB), which employs different methods to handle high-low frequency information, enabling the model to address different degradations more effectively. Compared to other networks, our method takes into account the characteristics of different degradations, thus achieving higher-quality image restoration.
3. Comparison of other methods in track 2 of the Bracketing Image Restoration and Enhancement Challenge.
@article{chen2024bracketing,
title={Bracketing Image Restoration and Enhancement with High-Low Frequency Decomposition},
author={Chen, Genggeng and Dai, Kexin and Yang, Kangzhen and Hu, Tao and Chen, Xiangyu and Yang, Yongqing and Dong, Wei and Wu, Peng and Zhang, Yanning and Yan, Qingsen},
journal={arXiv preprint arXiv:2404.13537},
year={2024}
}
If you have any questions, feel free to contact Genggeng Chen at [email protected].