Views: 7 Author: Site Editor Publish Time: 2025-03-28 Origin: Site
Working Principle of Infrared Imaging Systems:
An infrared imaging system is a device that can convert optical signals into electrical signals. Different from the working principle of visible light imaging systems, infrared imaging systems can convert infrared radiation from a scene into grayscale values, and then further into infrared image. Areas with greater radiation intensity in a natural scene correspond to higher grayscale values in the infrared image, resulting in increased image brightness as perceived by the human eye.
The working principle of common infrared imaging system involves focusing the scene through a telephoto lens, which then projects onto a line-scanning mirror. The line-scanning mirror reflects the light to a frame mirror, which in turn reflects the light to a convex lens. The light is ultimately focused onto an infrared detector, which converts the optical signal into electrical signal. Finally, this electrical signal is then processed by an electronic processing system and output to a display device.
Working Process of Infrared Imaging Systems:
Infrared imaging system mainly includes optical imaging system, infrared detector and video signal amplifier and other parts. The working process is as follows: the natural scene emits infrared radiation, which is transmitted through the atmosphere to the optical components of the optical imaging system. After focusing by the optical components, the radiation is sent to the infrared detector, which converts the received radiation into electrical signal. The signal is then amplified by the video amplifier and output to an external display device.
Characteristics of Infrared Imaging Systems Compared to Visible Light Imaging Systems:
a) Infrared imaging systems have a strong penetration capability, allowing them to detect targets beyond the visual range of the human eye.
b) Infrared imaging systems detect the temperature difference between the target and its surrounding environment, providing better recognition capabilities compared to visible light imaging systems.
c) Infrared imaging systems are far superior to visible light imaging systems in adapting to nighttime environments.
d) Infrared imaging systems are more compact and consume less energy than visible light imaging systems.
These features enable infrared imaging systems to operate continuously under various environmental conditions, overcoming the limitations of visible light imaging systems in low visibility scenarios such as fog or nighttime.
Characteristics of Infrared Images:
Infrared images represent the distribution of infrared radiation from the scene, which mainly depends on the object's emissivity and temperature distribution. The grayscale fluctuations mainly depend on the weaker radiation changes in the background radiation of the scene. A large amount of research has shown that infrared images have the following characteristics:
a) Low Signal-to-Noise Ratio (SNR): Infrared images are prone to noise due to multiple factors, including molecular thermal motion. Therefore, it is necessary to pre-process infrared images to remove noise while analyzing the causes of the noise.
b) Low contrast: In natural conditions, the temperature differences between objects are typically small due to the heat exchange, thermal radiation, and absorption, resulting in low contrast between the target and background in infrared images.
Given these characteristics, infrared image preprocessing should address noise elimination and contrast enhancement to improve image quality.
Research on Noise Suppression Methods:
Infrared target detection and recognition rely on effective infrared image preprocessing. During detection and recognition, the small imaging area of distant targets makes them susceptible to background clutter interference. Furthermore, due to the low SNR (signal-to-noise ratio) of the image, only weak signals can be detected, increasing the difficulty of target detection. For these reasons, it is necessary to perform background clutter suppression and other preprocessing steps before target detection. It will also improve the performance of subsequent target detection.
A large number of researches show that background grayscale changes in infrared images generally occur in the low-frequency part, while the grayscale of noise and targets fluctuates rapidly, mainly in the high-frequency part. The background grayscale shows strong correlation across different image frames, whereas noise is randomly distributed and unrelated to the background.
Since the background changes slowly in space and noise is randomly distributed, noise can be suppressed through multi-frame accumulation. When performing image preprocessing, the key is to distinguish between the low-frequency background and high-frequency noise and targets. By eliminating the low-frequency parts of the background and the high-frequency parts of the noise, the SNR of foreground targets can be improved, facilitating more accurate target detection and reduced false alarms.
Contrast Enhancement Based on Eliminating Temperature Nonlinear Distribution:
An experiment conducted by Ohio State University’s Thermal Pedestrian Database showed significant improvements in the processed image compared to the original image. The background was well suppressed, and the target region was well preserved, enhancing the contrast between the target and the background.
The experimental results indicated that this method effectively suppresses the slowly changing background and enhances the contrast between pedestrian targets and the background. However, while eliminating the background, the brightness of the pedestrian target was also affected, which reduced detection accuracy.
This method improves the contrast between the target and the background, so the contrast can be improved using multiple iterations. Try multiple iterations and the experimental effects are shown in the figures below. After each iteration, the background becomes less obvious, and the target area becomes more prominent.
Experimental results show that as the number of iterations increases, target-background contrast is enhanced, and low-brightness background regions gradually fade until they are eliminated.
References:
[1] Paul Viola,;Michael J. Jones.Robust Real-Time Face Detection.].international Journal of Computer Vision,2004(2):137-154.
[2] Paul Viola::Michael J. Jones::Daniel Snow.Detecting Pedestrians Using Patterns of Motion and Appearance.Ul.International Journal of Computer Vision,2005(2):153-161.
