Image information for single polarization parameters is weak, low contrast and the common visible light intensity image detail fuzzy problems, in order to further improve the target detection of polarization imaging detection system identification capability, put forward a kind of based on the sampling of shear wave transformation under visible light image fusion algorithms, intensity and polarization parameters can effectively improve the identification of targets in complex background. Polarization degree of the image and visible light intensity image using the sampling shear wave transformation under the decomposed high frequency subband and low-frequency subband, then low frequency subband image fusion rules design based on region distance energy weighted algorithm, and the high frequency subband image fusion rule is designed to combine guide take large filtering area of energy, will eventually high low frequency subband image by the NSST finally fused image is obtained by inverse transformation refactoring. By comparing the fusion results of this algorithm with those of other methods that adopt the same decomposition transformation method but choose different fusion rules, the experiment proves that this algorithm not only has the best visual effect, but also has the advantage in the objective evaluation index value.
The false alarm generated by the background object in the complex sky background is a difficult problem in the detection of infrared small targets. The problem is particularly prominent when the target signal is weak. In order to effectively suppress the false alarm rate and improve the detection rate, a weak target detection algorithm based on infrared polarization map is proposed. Firstly, the data is collected by the infrared polarization detecting device, and then the polarization component map with the best image quality is analyzed. Finally, the filtering algorithm combined with Top- Hat morphological filtering and median filtering is used, and the global component threshold detection method is used to complete the polarization component map. Weak target detection. The experimental results show that the proposed method has higher detection rate and lower false alarm rate than the Top-Hat algorithm, maximum mean filtering algorithm, and LCM algorithm. An effective method for detecting weak targets in the background.
Aiming at the accuracy and speed of image change detection, an improved registration algorithm combining wavelet transform and SURF algorithm is proposed, and image change detection is completed by an image adaptive constraint threshold method. Firstly, the image is decomposed based on wavelet transform and the low-dimensional components are coarsely registered by SURF. Then the image is dimension reduced by PCA, and the obtained feature points are coarsely registered according to the bidirectional registration criterion. Then the RANSAC algorithm is used to select the exact one. The registration point is the least-squares fitting registration of the image. Finally, the image is detected by the adaptive constraint threshold method based on the mean ratio difference map based on the precise registration. The experimental results show that the accuracy and speed of the registration algorithm are better than those of SIFT and SURF. The detection method is better and the detection accuracy is improved.
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