Bracketing Image Restoration and Enhancement with High-Low Frequency Decomposition

• URL: http://arxiv.org/abs/2404.13537v2
• Date: Wed, 24 Apr 2024 09:51:35 GMT
• Title: Bracketing Image Restoration and Enhancement with High-Low Frequency Decomposition
• Authors: Genggeng Chen, Kexin Dai, Kangzhen Yang, Tao Hu, Xiangyu Chen, Yongqing Yang, Wei Dong, Peng Wu, Yanning Zhang, Qingsen Yan,
• Abstract summary: HLNet is a Bracketing Image Restoration and Enhancement method based on high-low frequency decomposition. We employ two modules for feature extraction: shared weight modules and non-shared weight modules. 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.
• Score: 44.80645807358389
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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