WaveSeg: Enhancing Segmentation Precision via High-Frequency Prior and Mamba-Driven Spectrum Decomposition

• URL: http://arxiv.org/abs/2510.21079v1
• Date: Fri, 24 Oct 2025 01:41:31 GMT
• Title: WaveSeg: Enhancing Segmentation Precision via High-Frequency Prior and Mamba-Driven Spectrum Decomposition
• Authors: Guoan Xu, Yang Xiao, Wenjing Jia, Guangwei Gao, Guo-Jun Qi, Chia-Wen Lin,
• Abstract summary: We propose a novel decoder architecture, WaveSeg, which jointly optimize feature refinement in spatial and wavelet domains.<n>High-frequency components are first learned from input images as explicit priors to reinforce boundary details.<n>Experiments on standard benchmarks demonstrate that WaveSeg, leveraging wavelet-domain frequency prior with Mamba-based attention, consistently outperforms state-of-the-art approaches.
• Score: 61.3530659856013
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While recent semantic segmentation networks heavily rely on powerful pretrained encoders, most employ simplistic decoders, leading to suboptimal trade-offs between semantic context and fine-grained detail preservation. To address this, we propose a novel decoder architecture, WaveSeg, which jointly optimizes feature refinement in spatial and wavelet domains. Specifically, high-frequency components are first learned from input images as explicit priors to reinforce boundary details at early stages. A multi-scale fusion mechanism, Dual Domain Operation (DDO), is then applied, and the novel Spectrum Decomposition Attention (SDA) block is proposed, which is developed to leverage Mamba's linear-complexity long-range modeling to enhance high-frequency structural details. Meanwhile, reparameterized convolutions are applied to preserve low-frequency semantic integrity in the wavelet domain. Finally, a residual-guided fusion integrates multi-scale features with boundary-aware representations at native resolution, producing semantically and structurally rich feature maps. Extensive experiments on standard benchmarks demonstrate that WaveSeg, leveraging wavelet-domain frequency prior with Mamba-based attention, consistently outperforms state-of-the-art approaches both quantitatively and qualitatively, achieving efficient and precise segmentation.

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