提升红外测温精度，通过联合优化图像增强与辐射校准。
Temperature calibration of surface emissivities with an improved thermal image enhancement network
- 用对称跳连网络和发射率感知注意力模块统一处理温度校正与图像增强。
- 在不同工况下实现温度误差低于1.5℃的精准校准。
- 适合工业红外检测场景，尤其适用于复杂背景下的高温设备监测。
红外热成像因材料发射率差异导致温度测量不准确，现有方法常忽略辐射校准与图像退化的联合优化。本文提出一种物理引导的神经框架，采用对称跳连卷积网络和发射率感知注意力模块，统一温度修正与图像增强。预处理阶段对图像区域进行分割并初步校正辐射率；设计新型双约束损失函数，通过均值-方差对齐与基于Kullback-Leibler散度的直方图匹配，强化目标区与参考区的统计一致性。模型动态融合热辐射特征与空间上下文信息，在抑制发射率伪影的同时恢复结构细节。在多种工况下的工业鼓风机系统验证表明，该方法实现了热辐射特性与空间背景的动态融合，具备良好的工业适应性与高精度校准能力。
原文摘要 · Abstract (English)
Infrared thermography faces persistent challenges in temperature accuracy due to material emissivity variations, where existing methods often neglect the joint optimization of radiometric calibration and image degradation. This study introduces a physically guided neural framework that unifies temperature correction and image enhancement through a symmetric skip-CNN architecture and an emissivity-aware attention module. The pre-processing stage segments the ROIs of the image and and initially corrected the firing rate. A novel dual-constrained loss function strengthens the statistical consistency between the target and reference regions through mean-variance alignment and histogram matching based on Kullback-Leibler dispersion. The method works by dynamically fusing thermal radiation features and spatial context, and the model suppresses emissivity artifacts while recovering structural details. After validating the industrial blower system under different conditions, the improved network realizes the dynamic fusion of thermal radiation characteristics and spatial background, with accurate calibration results in various industrial conditions.
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