Bracketing Image Restoration and Enhancement with High-Low Frequency Decomposition

Genggeng Chen, Kexin Dai, Kangzhen Yang, Tao Hu, Xiangyu Chen, Yongqing Yang, Wei Dong, Peng Wu, Yanning Zhang, Qingsen Yan; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 6097-6107

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.