Fourier-Based Decoupling Network for Joint Low-Light Image Enhancement and Deblurring
Luwei Tu, Jiawei Wu, Chenxi Wang, Deyu Meng, Zhi Jin
Abstract
Nighttime handheld photography is often simultaneously affected by low light and blur degradations due to object motion and camera shake. Previous methods typically design specific modules to restore the degradations in the spatial domain independently. However, the interdependence of low light and blur degradations in the spatial domain makes it difficult for these approaches to effectively decouple the degradations, limiting the performance of the designed modules. In this paper, we observe that in the Fourier domain, low light and blur degradations can be represented independently in the amplitude and phase of the image. Through an in-depth analysis of the underlying physical degradation process, we discover that low light degradation exhibits distinct characteristics across different frequency bands in amplitude, while blur degradation is characterized by phase correlation. Leveraging these insights, we mathematically derive a frequency attention mechanism and a filtering mechanism for learning decoupled representations of these degradations, proposing a Fourier-based Decoupling Network for joint low-light image enhancement and deblurring. Experimental results demonstrate that our method achieves the state-of-the-art performance on both synthetic and real-world datasets and exhibits significantly sharper edges. Code is available at https://github.com/Jabruson/FDN-TIP2025.