高效去模糊网络，兼顾画质与速度，竞赛第二名。
FSM-Net: An Efficient Frequency-Spatial Network for Real-World Deblurring
- 双域设计：频域注意力+线性复杂度的跨门控视觉模块。
- 在RSBlur上达33.144 dB PSNR，仅494万参数，159.35 GMACs。
- 适合移动端或嵌入式设备部署，资源受限场景首选。
真实世界图像去模糊需兼顾高保真恢复与计算效率，现有方法常难以平衡。本文提出FSM-Net（频率-空间多分支网络），一种高效解决方案，在NTIRE 2026高效真实世界去模糊挑战赛中获第二名。FSM-Net首创双域方法：新颖的频域注意力模块通过FFT显式恢复高频结构细节；瓶颈处的跨门控视觉E-Branchformer以线性复杂度捕捉全局依赖。为确保稳定收敛，采用由复合损失函数（多尺度Charbonnier、结构边缘、频域）引导的渐进式课程训练策略。在RSBlur基准测试中，FSM-Net在1920x1200分辨率下实现33.144 dB PSNR，仅需4.94M参数和159.35 GMACs。通过有效推动效率与质量的帕累托前沿，为资源受限的图像修复任务建立了强基准。
原文摘要 · Abstract (English)
Real-world image deblurring demands both high-fidelity restoration and computational efficiency, a balance existing methods often struggle to achieve. In this paper, we propose FSM-Net (Frequency-Spatial Multi-branch Network), a highly efficient solution that secured 2nd place in the NTIRE 2026 Challenge on Efficient Real-World Deblurring. FSM-Net pioneers a dual-domain approach: a novel Frequency Attention module explicitly recovers high-frequency structural details via FFT, while a Cross-Gated Vision E-Branchformer at the bottleneck captures global dependencies with linear complexity. To ensure robust convergence, we employ a progressive curriculum training strategy guided by a composite loss function (Multi-Scale Charbonnier, Structural Edge, and Frequency). Evaluated on the RSBlur benchmark, FSM-Net achieves an outstanding 33.144 dB PSNR with only 4.94M parameters and 159.35 GMACs (at 1920x1200 resolution). By effectively pushing the Pareto frontier of efficiency and quality, FSM-Net establishes a strong baseline for resource-constrained image restoration.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。