Infrared (IR) cameras are widely used for temperature measurements in various
applications, including agriculture, medicine, and security. Low-cost IR camera
have an immense potential to replace expansive radiometric cameras in these
applications, however low-cost microbolometer-based IR cameras are prone to
spatially-variant nonuniformity and to drift in temperature measurements, which
limits their usability in practical scenarios.
To address these limitations, we propose a novel approach for simultaneous
temperature estimation and nonuniformity correction from multiple frames
captured by low-cost microbolometer-based IR cameras. We leverage the physical
image acquisition model of the camera and incorporate it into a deep learning
architecture called kernel estimation networks (KPN), which enables us to
combine multiple frames despite imperfect registration between them. We also
propose a novel offset block that incorporates the ambient temperature into the
model and enables us to estimate the offset of the camera, which is a key
factor in temperature estimation.
Our findings demonstrate that the number of frames has a significant impact
on the accuracy of temperature estimation and nonuniformity correction.
Moreover, our approach achieves a significant improvement in performance
compared to vanilla KPN, thanks to the offset block. The method was tested on
real data collected by a low-cost IR camera mounted on a UAV, showing only a
small average error of $0.27^\circ C-0.54^\circ C$ relative to costly
scientific-grade radiometric cameras.
Our method provides an accurate and efficient solution for simultaneous
temperature estimation and nonuniformity correction, which has important
implications for a wide range of practical applications.

通过深度学习架构和光学图像获取模型，我们提出了一种新颖的方法，能够从低成本的微测温红外相机拍摄的多帧图像中同时估计温度和进行非均匀性校正，该方法在实验证明相比昂贵的科学级热像仪，具有显著的性能提升和准确度。

多帧同时温度估计与非均匀性校正

Simultaneous temperature estimation and nonuniformity correction from  multiple frames

Accurate temperature measurements are essential for the proper monitoring and
control of industrial furnaces. However, measurement uncertainty is a risk for
such a critical parameter. Certain instrumental and environmental errors must
be considered when using spectral-band radiation thermometry techniques, such
as the uncertainty in the emissivity of the target surface, reflected radiation
from surrounding objects, or atmospheric absorption and emission, to name a
few. Undesired contributions to measured radiation can be isolated using
measurement models, also known as error-correction models. This paper presents
a methodology for budgeting significant sources of error and uncertainty during
temperature measurements in a petrochemical furnace scenario. A continuous
monitoring system is also presented, aided by a deep-learning-based measurement
correction model, to allow domain experts to analyze the furnace's operation in
real-time. To validate the proposed system's functionality, a real-world
application case in a petrochemical plant is presented. The proposed solution
demonstrates the viability of precise industrial furnace monitoring, thereby
increasing operational security and improving the efficiency of such
energy-intensive systems.

本文提出了一种基于辐射测温技术在石化炉炉内测量温度时误差和不确定性的预算方法，并引入了基于深度学习的测量校正模型构建的连续监测系统，实现了精确监控石化炉的操作，提高了系统效率。