Spatial-Frequency Awareness for Low-Light Instance Segmentation

Abstract

The low-light instance segmentation task tends to suffer from degraded performance due to contrast deficiency and noise interference. Most existing methods primarily rely on image enhancement preprocessing or spatial-domain architectures, which suffer from semantic representation deficiency and cross-frequency noise issues. To address these issues, we propose SFNet, which innovatively integrates spatial-frequency awareness cues into the low-light instance segmentation task. Specifically, for the semantic representation deficiency issue, we introduce a cross-frequency fusion unit (CFU) that decomposes spatial features into high- and low-frequency components via the stationary wavelet transform (SWT) and then fuses them with frequency-aware attention to enrich the semantic representation. Furthermore, we design a multi-domain mixer (MDM) for complementary spatial-frequency feature fusion and enhancement. To tackle the cross-frequency noise issue, we design a learnable adaptive frequency-domain filtering mechanism after the SWT in the CFU, which effectively suppresses noise in both low- and high-frequency domains. In addition, we design a novel frequency consistency constraint (FCC) to reduce noise interference across different frequency bands. Our method effectively suppresses noise interference while enhancing the complementarity between spatial and frequency features. Extensive experiments on the LIS dataset demonstrate substantial accuracy improvements, and ablation studies further verify the effectiveness of each proposed component. The code is made publicly available at: https://github.com/LuCky-cv-14/SFNet.