针对手术与深海机器人图像低频信息丢失问题，提出频域增强分割模型
Frequency Domain Enhanced U-Net for Low-Frequency Information-Rich Image Segmentation in Surgical and Deep-Sea Exploration Robots
- 基于生物视觉机制设计自适应频谱融合模块，平衡跨频段特征
- 在深海生物与息肉分割任务中达当前最优性能
- 适合低光照、高噪声环境下需保留低频细节的视觉任务
在深海探测与手术机器人场景中，环境光照与设备分辨率限制常导致高频特征衰减。针对卷积神经网络与人类视觉系统在频带敏感性上的差异（人眼对中频敏感，低频敏感度高于高频），我们实验量化了CNN的对比敏感度函数，并提出受生物视觉启发的波形自适应频谱融合（WASF）方法，以均衡跨频段图像特征。进一步设计感知频率块（PFB），集成WASF以增强频域特征提取能力。基于此构建了FE-UNet模型，采用SAM2骨干网络并融入微调的Hiera-Large模块，在保证分割精度的同时提升泛化能力。实验表明，FE-UNet在海洋生物分割与息肉分割等跨域任务中达到领先水平，展现出强鲁棒性与显著应用潜力。代码将尽快开源。
原文摘要 · Abstract (English)
In deep-sea exploration and surgical robotics scenarios, environmental lighting and device resolution limitations often cause high-frequency feature attenuation. Addressing the differences in frequency band sensitivity between CNNs and the human visual system (mid-frequency sensitivity with low-frequency sensitivity surpassing high-frequency), we experimentally quantified the CNN contrast sensitivity function and proposed a wavelet adaptive spectrum fusion (WASF) method inspired by biological vision mechanisms to balance cross-frequency image features. Furthermore, we designed a perception frequency block (PFB) that integrates WASF to enhance frequency-domain feature extraction. Based on this, we developed the FE-UNet model, which employs a SAM2 backbone network and incorporates fine-tuned Hiera-Large modules to ensure segmentation accuracy while improving generalization capability. Experiments demonstrate that FE-UNet achieves state-of-the-art performance in cross-domain tasks such as marine organism segmentation and polyp segmentation, showcasing robust adaptability and significant application potential. The code will be released soon.
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