The current research status of micro scanning imaging technology is discussed in this article. It analyzes the basic principles and implementation methods of micro scanning imaging technology, the factors that affect the effect of microscanning super-resolution imaging. The results indicate that scan step size, registration algorithm, and reconstruction algorithm are the main factors affecting micro scanning super-resolution imaging. Changing the mode of micro scanning(reducing scan step size), improving the registration accuracy of registration algorithm, and improving the accuracy of reconstruction algorithm can improve the effect of super resolution. Finally, the low resolution images collected in2×2mode are reconstructed using POCS algorithm, and the improvement ability after micro scanning is calculated.
This article provides theoretical derivation of the mathematical model of an infrared imaging system, and research shows that changing the sampling process of the detector can improve the MTF of the system. Theoretical simulation analysis showed that the use of micro scanning technology can improve the MTF of the system, and finally, the super-resolution ability of cc scanning technology was verified through experiments. The results indicate that the MTF of the system can be improved through micro scanning technology, and the main factors affecting the improvement of MTF include the pixel size of the detector, fill rate, micro scanning method and so on. When designing the system, different scanning methods can be set according to requirements to achieve super-resolution effects.
For the linear array scanning infrared detection system, the reasonable design of the system hardware architecture and data processing flow is the key to ensure the system to achieve real-time target detection and fast recognition. Fast and effective target recognition algorithm is the core of the system design. The signal processing of the linear scanning infrared detection system designed in this paper adopts the hardware architecture of FPGA + DSP + GPU, and puts forward the false target discrimination method of sky and earth line based on semantic segmentation based on deep learning, which is different from the traditional threshold detection and segmentation method based on artificial template matching. The deep learning method uses the semantic information and spatial information of infrared image and has certain adaptability. Finally, the algorithm is implemented on the hardware system through the field measured data, and the effectiveness of the algorithm is verified.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.