In infrared detection system, the range of field of view is a key index to judge the system. How to detect long distance targets with large field of view by technical means on the basis of existing devices has become an urgent problem to be solved. In this paper, the imaging principle of optical wedge scanning is firstly introduced, then infrared target is used to calibrate the infrared camera, and the distortion of the scanned image is corrected by using the calibration results. Finally, the optical wedge scanning can double the imaging field of view through the semi-physical simulation.
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.
The efficient detection of “low-altitude, small, slow” UAV is always a difficult problem to overcome. In this paper, we are aiming at the application and threat cases of “low-altitude, small, slow” UAV, analyze the basic principle and development status of several kinds of methods in the detection of this target, and then makes an outlook on the future development direction of “low-altitude, small, slow” UAV detection.
If Toeplitz matrix is used for compression-aware ghost imaging, the imaging quality will be very low. In order to solve the problem, a new experimental scheme for ghost imaging is proposed in this paper. The scheme first extracts the Toeplitz matrix elements randomly and sparsely using a revolving matrix, and then modulates the illumination light field. The sampling is performed by using the Toeplitz matrix as a fixed matrix and another measurement matrix as a revolving matrix. The revolving matrix rotates around its center at a constant angular velocity. The fixed Toeplitz matrix is superimposed with the revolving matrix to form the optical field modulation matrix. The results of simulation experiments show that light field modulation scheme of the rotational random extraction of Toeplitz matrix can modulate the light field with more randomness. The use of this scheme in ghost imaging experiments results in high quality images with low distortion.
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.
In order to improve the performance of low-quality noise grayscale image edge detection, using the principle that phase consistency is invariant to changes in grayscale and contrast, a noise image edge detection based on the fusion of multi-angle morphology filtering and phase consistency is proposed. The algorithm improves the defects of the previous edge detection algorithms that only rely on a single gray gradient difference or only use fixed direction weights and experimental results show that our algorithm is more accurate in noise suppression and edge detection of low-quality noise images than traditional algorithms.
The differences of conventional imaging and correlated imaging are discussed in this paper. The mathematical model of lensless ghost imaging system is set up and the image of double slits is computed by mathematical simulation. The results are also testified by the experimental verification. Both the theory simulation and experimental verifications results shows that the mathematical model based on statistical optical principle are keeping consistent with real experimental results.
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