For the disadvantage of traditional target detection methods with low detection rate and difficulties in distinguishing the small temperature difference and camouflage targets, this paper presents a kind of visible polarization image fusion method using non-subsampled Shearlets transform. Firstly, we obtain four polarization status images by multi-detector camera, where the direction of polarization angle is 0° 、45°、90°、135° separately. The Stokes vectors are calculated by polarization status images. Then, the extracted target polarization feature images and light intensity image are decomposed into several sub frequency bands by NSST with fine multi-scale decomposition characteristics. Meanwhile, the fusion coefficients are determined based on high-frequency energy window and low-frequency mean in the frequency domain. At last, the final fusion image is obtained after NSST inverse transform and target enhancement. Experimental results illustrate that the proposed approach could obtain better fusion images with rich details, high contrast, highlighting the polarization characteristic of the target to improve the ability of scene perception and target detection.
Autonomous celestial navigation based on stellar refraction has attracted widespread attention for its high accuracy and full autonomy.In this navigation method, establishment of accurate stellar refraction measurement model is the fundament and key issue to achieve high accuracy navigation. However, the existing measurement models are limited due to the uncertainty of atmospheric parameters. Temperature, pressure and other factors which affect the stellar refraction within the height of earth's stratosphere are researched, and the varying model of atmosphere with altitude is derived on the basis of standard atmospheric data. Furthermore, a novel measurement model of stellar refraction in a continuous range of altitudes from 20 km to 50 km is produced by modifying the fixed altitude (25 km) measurement model, and equation of state with the orbit perturbations is established, then a simulation is performed using the improved Extended Kalman Filter. The results show that the new model improves the navigation accuracy, which has a certain practical application value.
Although high dynamic range (HDR) images contain large amounts of information, they have weak texture and low contrast. What's more, these images are difficult to be reproduced on low dynamic range displaying mediums. If much more information is to be acquired when these images are displayed on PCs, some specific transforms, such as compressing the dynamic range, enhancing the portions of little difference in original contrast and highlighting the texture details on the premise of keeping the parts of large contrast, are needed. To this ends, a multi-scale guided filter enhancement algorithm which derives from the single-scale guided filter based on the analysis of non-physical model is proposed in this paper. Firstly, this algorithm decomposes the original HDR images into base image and detail images of different scales, and then it adaptively selects a transform function which acts on the enhanced detail images and original images. By comparing the treatment effects of HDR images and low dynamic range (LDR) images of different scene features, it proves that this algorithm, on the basis of maintaining the hierarchy and texture details of images, not only improves the contrast and enhances the details of images, but also adjusts the dynamic range well. Thus, it is much suitable for human observation or analytical processing of machines.
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