Thermal cameras are an important tool for agricultural research because they
allow for non-invasive measurement of plant temperature, which relates to
important photochemical, hydraulic, and agronomic traits. Utilizing low-cost
thermal cameras can lower the barrier to introducing thermal imaging in
agricultural research and production. This paper presents an approach to
improve the temperature accuracy and image quality of low-cost thermal imaging
cameras for agricultural applications. Leveraging advancements in computer
vision techniques, particularly deep learning networks, we propose a method,
called $\textbf{VisTA-SR}$ ($\textbf{Vis}$ual \& $\textbf{T}$hermal
$\textbf{A}$lignment and $\textbf{S}$uper-$\textbf{R}$esolution Enhancement)
that combines RGB and thermal images to enhance the capabilities of
low-resolution thermal cameras. The research includes calibration and
validation of temperature measurements, acquisition of paired image datasets,
and the development of a deep learning network tailored for agricultural
thermal imaging. Our study addresses the challenges of image enhancement in the
agricultural domain and explores the potential of low-cost thermal cameras to
replace high-resolution industrial cameras. Experimental results demonstrate
the effectiveness of our approach in enhancing temperature accuracy and image
sharpness, paving the way for more accessible and efficient thermal imaging
solutions in agriculture.

通过结合 RGB 和热成像图像，采用计算机视觉技术中的深度学习网络，提出了一种名为 VisTA-SR（Visual & Thermal Alignment and Super-Resolution Enhancement）的方法，以增强低成本热成像相机的温度准确性和图像质量，进一步探索了低成本热成像相机在农业领域替代高分辨率工业相机的潜力，并证明了该方法在提高温度准确性和图像清晰度方面的有效性，为农业领域提供了更可靠和高效的热成像解决方案。

VisTA-SR: 提升农业低成本热像相机的准确度和分辨率

VisTA-SR: Improving the Accuracy and Resolution of Low-Cost Thermal  Imaging Cameras for Agriculture

Low-cost thermal cameras are inaccurate (usually $\pm 3^\circ C$) and have
space-variant nonuniformity across their detector. Both inaccuracy and
nonuniformity are dependent on the ambient temperature of the camera. The main
goal of this work was to improve the temperature accuracy of low-cost cameras
and rectify the nonuniformity.
A nonuniformity simulator that accounts for the ambient temperature was
developed. An end-to-end neural network that incorporates the ambient
temperature at image acquisition was introduced. The neural network was trained
with the simulated nonuniformity data to estimate the object's temperature and
correct the nonuniformity, using only a single image and the ambient
temperature measured by the camera itself. Results show that the proposed
method lowered the mean temperature error by approximately $1^\circ C$ compared
to previous works. In addition, applying a physical constraint on the network
lowered the error by an additional $4\%$.
The mean temperature error over an extensive validation dataset was
$0.37^\circ C$. The method was verified on real data in the field and produced
equivalent results.

低成本热成像摄像机的温度准确性和非均匀性受环境温度影响，本研究旨在提高低成本摄像机的温度准确性并纠正非均匀性，通过开发一个考虑环境温度的非均匀性模拟器和一个全新的神经网络，利用单张图像和摄像机自身测量的环境温度来估计目标温度和校正非均匀性，结果表明该方法相比以前的工作将平均温度误差降低了约 1 摄氏度，而对网络应用物理约束则进一步降低了 4% 的误差，经过广泛验证的平均温度误差为 0.37 摄氏度，该方法在实地验证中产生了相当的结果。