UMSPU：通过互自蒸馏和自适应提升集成分割器实现通用多尺寸相位解包裹
UMSPU: Universal Multi-Size Phase Unwrapping via Mutual Self-Distillation and Adaptive Boosting Ensemble Segmenters
AI总结：
针对传统及现有深度学习相位解包裹方法在大尺寸图像处理上的不足，提出含互自蒸馏机制和自适应提升集成分割器的UMSPU网络，实现256*256至2048*2048多尺寸图像高精度解包裹，性能优于现有方法。
摘要：
空间相位解包裹是提取相位信息以获取三维形态及其他特征的关键技术。现代工业测量场景对精度、图像尺寸和速度要求很高，但传统方法在抗噪性和处理速度上存在不足，当前深度学习方法受限于感受野大小和稀疏语义信息，对大尺寸图像效果不佳。为此，我们提出互自蒸馏（MSD）机制和自适应提升集成分割器，构建通用多尺寸相位解包裹网络（UMSPU）。MSD通过双向蒸馏实现分层注意力细化与跨层协同学习，确保不同尺寸图像的细粒度语义表示；自适应提升集成分割器将不同感受野的弱分割器组合为强分割器，保证空间频率上的稳定分割。实验表明，UMSPU突破图像尺寸限制，在256*256到2048*2048（8倍增长）的图像尺寸上均实现高精度，且在速度、鲁棒性和泛化性上优于现有方法，其实用性在结构光成像和InSAR中得到进一步验证。我们认为UMSPU为相位解包裹提供了通用解决方案，在工业应用中具有广泛潜力。
英文摘要：
Spatial phase unwrapping is a key technique for extracting phase information to obtain 3D morphology and other features. Modern industrial measurement scenarios demand high precision, large image sizes, and high speed. However, conventional methods struggle with noise resistance and processing speed. Current deep learning methods are limited by the receptive field size and sparse semantic information, making them ineffective for large size images. To address this issue, we propose a mutual self-distillation (MSD) mechanism and adaptive boosting ensemble segmenters to construct a universal multi-size phase unwrapping network (UMSPU). MSD performs hierarchical attention refinement and achieves cross-layer collaborative learning through bidirectional distillation, ensuring fine-grained semantic representation across image sizes. The adaptive boosting ensemble segmenters combine weak segmenters with different receptive fields into a strong one, ensuring stable segmentation across spatial frequencies. Experimental results show that UMSPU overcomes image size limitations, achieving high precision across image sizes ranging from 256*256 to 2048*2048 (an 8 times increase). It also outperforms existing methods in speed, robustness, and generalization. Its practicality is further validated in structured light imaging and InSAR. We believe that UMSPU offers a universal solution for phase unwrapping, with broad potential for industrial applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
