Bracketing Image Restoration and Enhancement with High-Low Frequency Decomposition
<|reference_start|>Bracketing Image Restoration and Enhancement with High-Low Frequency Decomposition: 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.<|reference_end|>
@article{chen2024bracketing,
    title={Bracketing Image Restoration and Enhancement with High-Low Frequency
  Decomposition},
    author={Genggeng Chen, Kexin Dai, Kangzhen Yang, Tao Hu, Xiangyu Chen,
  Yongqing Yang, Wei Dong, Peng Wu, Yanning Zhang, Qingsen Yan},
    journal={arXiv preprint arXiv:2404.13537},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.13537},
    primaryClass={eess.IV cs.CV}
}