标题： FDTR图像增强器：结合物理信息去卷积和微观结构感知深度学习以增强热图像 显示英文标题

标题： FDTRImageEnhancer: Combining Physics-Informed Deconvolution and Microstructure-Aware Deep Learning to Enhance Thermal Images

摘要： 我们提出FDTRImageEnhancer，这是一个开源的计算框架，通过将基于物理的高斯卷积抽象与微观结构感知深度学习相结合，提高了从频域热反射（FDTR）相位数据中进行热导率映射的能力。高斯核模拟了泵浦和探测光束的空间平均效应，而高分辨率结构图像的k均值聚类减少了反演建模的参数空间。一种物理信息神经网络同时最小化相位数据误差和与解析恢复的导热系数图的偏差，使得晶界热导率下降能够被检测到，这些下降在传统FDTR反演中是视觉上被掩盖的。该框架在有限元生成的合成数据上进行了演示，能够在不到0.5%的误差范围内恢复体材料值，并且尽管图像分辨率有限，仍能定性地解析晶界效应。提供了完整的Python代码和数据集以实现可重复性，该方法可轻松适应其他反演热传输问题。 显示英文摘要

摘要： We present FDTRImageEnhancer, an open-source computational framework that improves thermal conductivity mapping from Frequency Domain ThermoReflectance (FDTR) phase data by integrating a physics-based Gaussian convolution abstraction with microstructure-aware deep learning. The Gaussian kernel models the spatial averaging effects of pump and probe beams, while k-means clustering of high-resolution structural images reduces the parameter space for inverse modeling. A physics-informed neural network jointly minimizes phase-data error and deviation from analytically recovered conductivity maps, enabling the detection of grain boundary thermal conductivity drops visually obscured in conventional FDTR inversions. Demonstrated on finite element-generated synthetic data, the framework recovers bulk values within less than 0.5% error and qualitatively resolves grain boundary effects despite limited image resolution. Full Python code and datasets are provided for reproducibility, with the methodology readily adaptable to other inverse thermal transport problems.