Image processing unit of the thermal imaging camera

In the complex system of a thermal imaging camera, the processing unit acts as a skilled artisan, carefully carving the digital signals output by the signal processing unit, converting them into intuitive, clear, and temperature-rich thermal images, crucial support for the effective application of thermal imaging technology in various fields.

Although the digital signals from the signal processing unit have been prelimarily denoised, amplified, and completed analog-to-digital conversion, the images may still have problems such as low contrast and blurred details. The image processing unit uses image enhancement techniques to improve image quality. By histogram equalization, the gray-level dynamic range of the image is expanded, allowing the details of the bright and dark in the image to be more clearly presented. For example, in industrial equipment thermal imaging inspection, the subtle differences in surface temperature of the equipment, which are difficult to originally, can be clearly presented with different temperature regions after histogram equalization processing, making it convenient for staff to quickly locate potential fault points. There are also edge detection filtering algorithms such as Laplacian operator and Gaussian filter, which can also highlight the contours and details of objects, enhancing the readability of the image.

The response of each pixel of the infrared sensor to infrared radiation is not completely consistent, which can lead to problems such as uneven brightness and noise artifacts in imaging images. The image processing unit will perform non-uniformity correction to eliminate these errors. Based on the two-point correction method, in the case of known high low temperature reference sources, the response of the sensor to the reference source is measured to establish a correction model, and the output signal of each pixel is corrected to ensure the same temperature area in the image presents consistent brightness and color, improving the accuracy of temperature measurement.

To more intuitively display the distribution of objects, the image processing unit maps the temperature values corresponding to the digital signals of each pixel and converts them into visualized color or gray-level values. In-color processing, according to the preset temperature-color mapping table, different temperature ranges correspond to different colors, such as using blue to represent low-temperature areas and red to high-temperature areas, making the temperature distribution clear at a glance. In medical thermal imaging diagnosis, doctors can quickly judge the abnormal temperature areas of the human body through pseudo-color thermal images, aiding in disease diagnosis.

In some high-end thermal imaging applications, the image processing unit has image fusion and object recognition functions. Image fusion is the fusion of thermal imaging images with visible light images, combining the advantages of both to obtain both the temperature information objects and a clear view of the objects' appearance and surrounding environment. In security monitoring, this allows security personnel to have a more comprehensive understanding of the monitoring scene. recognition uses machine learning algorithms, such as convolutional neural networks (CNN), to identify and classify target objects in thermal images. In forest fire monitoring, it can automatically fire sources and smoke, and issue alarms in time.