Variable Step Autofocus Design for Infrared Telescopes
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摘要: 在远距离目标检测和跟踪的过程中,成像清晰起着至关重要的作用。红外望远镜系统的成像距离远、景深短、失焦引起图像模糊。由于大气折射,望远镜所成的像处于不断变化之中,造成传统对焦算法对焦成功率、效率偏低。为了提高自动对焦的成功率和速度,采用了一种具备变步长的爬山法,利用多次求图像清晰度取其中位数的方法保证清晰度评价的准确性,利用带动量和加速度的爬山法降低了对焦过程中的不稳定性,减少了粗对焦过程所需的步数。算法在实际中波红外望远镜系统中得到应用,实验结果表明,该算法在粗对焦阶段所需的对焦步数比传统爬山法减少了12.8%,满足红外望远镜系统的需要。Abstract: In long-range target detection and tracking, image clarity plays a critical role. An infrared telescope system has a long imaging distance and a short depth of field, and the image blur caused by defocusing tends to be more severe in this system. In addition, because of the atmospheric refraction, the image derived from the telescope constantly changes. This results in a low focusing success rate and low efficiency in traditional focusing algorithms. To improve both the success rate and speed of autofocus, a mountain climbing algorithmic method with a variable step size was proposed in this study. Image clarity was obtained several times, and its median was calculated to ensure image clarity accuracy. Using the mountain climbing algorithm with momentum and acceleration reduces focusing instability as well as the number of steps required for the coarse focusing process. The algorithm was applied in an actual medium-wave infrared telescope system. Experimental results revealed that the focusing steps required by the algorithm for the coarse focusing stage were reduced by 12.8%, in comparison with the traditional mountain climbing method, meeting the requirements of an infrared telescope system.
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Key words:
- autofocus /
- infrared telescope /
- evaluation function /
- momentum /
- acceleration
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表 1 不同对焦评价函数的实时性
Table 1. Real time performance of different focusing evaluation functions
Function Time/ms Tenengrad 46.37 Brener 5.59 Laplacian 42.68 Variance 15.55 Paper[9] 250.9 Squared 11.64 Roberts 53.5 表 2 使用对比方法和使用本文提出的变步长自动对焦算法完成对焦所需的步数对比
Table 2. The comparison of the number of steps needed to complete the focusing between the compared method and using the variable step size autofocus algorithm proposed in this paper
Number of out-of-focus pulses Steps needed to finish autofocus Contrast algorithm Proposed algorithm 20 30 40 20 30 40 100 11.3 9.0 9.0 7.0 8.5 8.5 200 16.3 11.8 12.3 11.3 11.0 12.8 300 21.3 17.0 15.3 14.0 14.0 14.5 400 25.7 19.0 18.0 17.8 15.0 15.8 表 3 使用两种算法对焦前后的红外图像清晰度对比(其中Tenengrad数值均为原数值除以1×109后的结果)
Table 3. The comparison of clarity of the infrared image between the two methods(where all the Tenengrad values were divided by 1×109)
Number of out-of-focus pulses Tenengrad value before autofocus Tenengrad value after autofocus Contrast algorithm Clarity improved Proposed algorithm Clarity improved 100 7.6771 9.4609 23.24% 9.3857 22.26% 200 6.7049 9.3238 39.06% 9.3512 39.47% 300 6.1801 9.6894 56.78% 9.4935 53.61% 400 5.8859 9.3488 58.83% 9.8968 68.14% -
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