Abstract
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.
Abstract (translated)
红外热成像技术在温度准确性方面面临持续挑战，主要是由于材料发射率的变化，而现有的方法往往忽视了辐射校准与图像退化联合优化的重要性。这项研究引入了一个物理引导的神经网络框架，通过对称跳跃式卷积神经网络（skip-CNN）架构和发射率感知注意力模块统一了温度修正与图像增强过程。在预处理阶段，该框架分割出感兴趣区域并初步调整了火线比率。一种新颖的双重约束损失函数增强了目标区域与参考区域之间的统计一致性，通过基于Kullback-Leibler散度的均值-方差对齐和直方图匹配实现。 该方法通过动态融合热辐射特征与空间上下文信息工作，并且模型能够抑制发射率伪影同时恢复结构细节。经过不同条件下工业鼓风机系统的验证后，改进后的网络实现了热辐射特性和空间背景的动态融合，在各种工业环境中均能提供精确的校准结果。
URL
https://arxiv.org/abs/2506.16803
