Divide-and-Conquer: Frequency-Domain Decoupling Strategy for Low-Light Remote Sensing Image Enhancement

Low-light remote sensing images typically cover vast areas. They contain objects of various scales and have localized light sources. This makes it challenging to enhance brightness while preserving fine image structures. Existing approaches are primarily designed in the spatial domain. However, due to the tight coupling between illumination degradation and structural information, these methods often struggle to achieve effective enhancement. In this paper, we propose a divide-and-conquer frequency domain decoupling enhancement strategy. Specifically, by exploring the decoupling properties of the frequency domain, we design a Light Contrastive Regularization that constrains the model to focus solely on brightness distribution in the contrastive space while reducing interference from redundant information. Additionally, we introduce a novel Phase Mamba Enhancement Network, which leverages the unique continuity of the frequency domain. By employing a continuous scanning mechanism, our model effectively captures long-range dependencies in low-light remote sensing images, enabling finer-grained structural restoration. Extensive experiments demonstrate that our method surpasses state-of-the-art approaches both qualitatively and quantitatively.