In order to improve the accuracy of polarization target detection, the multi-parameter polarization contrast model is proposed after analyzing the typical polarization features of the polarization images. It utilizes both of the polarization degree and the polarization angle parameters. Then the fast polarizer angle detection method is designed according to this model to calculate and drive the motor to rotate the polarizer to the most appropriate deviation angle so as to maximize the contrast between the target and the background. Experimental results show that the proposed method can improve the contrast between the target and the background in the polarized image significantly, which makes the polarization detection more efficiently and lays a foundation for detecting the moving targets.
Because the images are always contaminated by different kinds of noise in the courses of image acquisition, transmission and storage process, the image denoising is a very important step of image restoration. The key of denoising algorithm is making recovery image reserve as much as possible edge details when eliminating noise. Because noise and image details both are part of the high frequency components of image, to some extent, these two sides are contradictory. If the selection of the criterion and treatment for noise and marginal are inappropriate , denoising will make image details ( especially the marginal) become more vague, which must reduce the quality of the image and increase greatly the complexity of subsequent image processing. Since the quantum process and imaging process have the similar characteristics in the probability and statistics fields, a kind of soft threshold denoising algorithm is proposed based on the concept of quantum computation such as the quantum bit, superposition and collapse, etc. This filter algorithm can generate an adaptive template according to the characteristic of the edge of local image. Due to the algorithm is sensitive to the shape of edge, the balance is obtained between the noise suppression and the edge preserving.
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