Rapid wide dynamic non-uniformity correction algorithm for infrared radiation measurement system
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摘要:
本文针对红外辐射测量系统需要积分时间连续变化的需求，提出一种快速宽动态的非均匀性校正算法。该算法考虑了积分时间效应和光学系统杂散辐射的影响，并利用25 mm口径的制冷型中波红外辐射特性测量系统进行试验验证。将本文所提算法与经典算法进行对比，结果表明，校正效率较传统非均匀性校正算法提高了3.4倍。本文还利用剩余残差评价原始图像以及两种算法的图像校正效果，利用多个积分时间（0.6 ms，3 ms和3.5 ms）模拟连续变化的积分。结果显示本文算法在各个积分时间下剩余残差均表现稳定且校正图像都具有良好的校正效果。
Abstract:In this paper, a rapid and wide dynamic non-uniformity correction algorithm is proposed for the requirement of continuous change of integration time in infrared radiation measurement system. The algorithm considers the impact of integration time effect and stray radiation of the optical system. The experimental verification was conducted by employing cooled mid-wave infrared radiation characteristic measurement system with a 25 mm aperture. The correction efficiency of the classical algorithm and the proposed algorithm are compared. The results indicate that the proposed algorithm is 3.4 times more efficient than the traditional non-uniformity correction algorithm. On the above basis, we evaluate the effect of the two algorithms on the image correction using residual non-uniformity. Multiple integration times (0.6 ms, 3 ms and 3.5 ms) are used to simulate the continuous change of integration. The results indicate that the residual non-uniformity of the proposed algorithm is consistent and the image has been effectively corrected.
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Key words:
- infrared imaging /
- focal plane array /
- radiation calibration /
- radiation measurement
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图 4 在不同积分时间下，辐射亮度与系统灰度的关系
Figure 4. The relationship between radiation brightness and system grayscale at different integration times
图 5 在不同黑体温度下积分时间与系统灰度的关系
Figure 5. The relationship between integration time and system grayscale at different blackbody temperatures
图 8 快速宽动态非均匀性算法的效率分析
Figure 8. Efficiency analysis of rapid wide dynamic non-uniformity algorithm
图 9 原始图像、传统NUC以及本文所提算法不同积分时间的校正残差对比
Figure 9. Comparison of corrected residuals of the original image, conventional NUC, and the algorithms mentioned in this paper at different integration times
图 10 在相同积分时间（0.6 ms）的不同算法的校正效果对比。（a）原始图像；（b）传统NUC算法结果；（c）本文所提NUC算法结果
Figure 10. Comparison of correction effects by different algorithms with the same integration time (0.6 ms). (a) Raw image; (b) the traditional NUC algorithm results; (c) the proposed NUC algorithm results
图 11 不同积分时间下不同算法的校正效果比较。（a）t=3 ms；（b）t=3.1 ms；（c）t=3.5 ms。（i）原始图像；（ii）传统NUC算法结果；（iii）本文所提NUC算法结果
Figure 11. Comparison of calibration effects by different algorithms with different integration times. (a) t=3 ms; (b) t=3.1 ms; (c) t=3.5 ms. (i) Raw image; (ii) traditional NUC algorithm results; (iii) the proposed NUC algorithm results
表 1 系统的基本参数
Table 1. Basic parameters of the proposed infrared system
Parameter Value Response band/μm 3.7~4.8 Pixel numbers 320×256 Pixel size/μm 30 NETD/mK 15 Number resolution (bit) 14 Cooled temperature/K 77 Focal length/mm 50 F/# 2 
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表 2 辐射定标参数的平均值
Table 2. Average value of radiometric calibration parameters
Parameters Value $ \overline {{R_n}} $ 573 $ \overline {{d_t}} $ 192 $ \overline {{d_{{{in}}}}} $ 1251
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