| MPPC low-level-light imaging enhancement algorithm based on sub-window box filtering北大核心CSCD |
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| 引用本文： | 汤祥裕,尹丽菊,周辉,邹国锋,崔玉敏,邓玉林,张言华.MPPC low-level-light imaging enhancement algorithm based on sub-window box filtering北大核心CSCD[J].应用光学,2022,43(6):1097-1106. |
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| 作者姓名： | 汤祥裕 尹丽菊 周辉 邹国锋 崔玉敏 邓玉林 张言华 |
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| 作者单位： | 山东理工大学 电气与电子工程学院，山东 淄博 255049 |
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| 基金项目： | 国家自然科学基金（62101310）；山东自然科学基金（ZR2020MF127） |
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| 摘 要： | 针对多像素光子计数器(MPPC)进行微光成像时，图像受光照不足和噪声影响出现的图像亮度低、对比度差、边缘模糊等问题，提出一种基于子窗口盒式滤波的自适应微光图像处理算法。为了减少算法运行时间的同时突出图像的边缘细节信息，利用子窗口盒式滤波器对图像进行分层得到基础层和细节层；对基础层图像采用自适应阈值直方图均衡化拉伸对比度，细节层图像采用自适应增益控制方式进行增强；根据基础层图像中有效灰度值个数占总灰度的比值自适应确定融合系数，将基础层图像与细节层图像融合得到增强后图像。通过微光实验平台设置3组不同照度的微光环境进行实验仿真，验证了本文算法在保持边缘信息和增强细节方面获得了更好的效果。实验结果表明本文算法在标准差、信息熵、平均梯度等客观评价方面优于改进前算法，提升了微光图像的成像效果。
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| 关 键 词： | 图像增强 微光环境 子窗口滤波 图像分层 自适应融合 |
| 收稿时间： | 2022-05-05 |
MPPC low-level-light imaging enhancement algorithm based on sub-window box filtering |
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| Tang X.Yin L.Zhou H.Zou G.Cui Y.Deng Y.Zhang Y..MPPC low-level-light imaging enhancement algorithm based on sub-window box filtering[J].Journal of Applied Optics,2022,43(6):1097-1106. |
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| Authors: | Tang XYin LZhou HZou GCui YDeng YZhang Y |
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| Affiliation: | College of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255049, China |
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| Abstract: | Aiming at the problems of low image brightness, poor contrast, and blurred edges caused by insufficient illumination and noise during low-level-light imaging for multi-pixel photon counter (MPPC), an adaptive low-level-light image processing algorithm based on sub-window box filtering was proposed. To reduce the algorithm running time while highlighting the edge detail information of the image, the sub-window box filter was used to layer the image to obtain the basic layer and detail layer. For the image of basic layer, the adaptive threshold histogram equalization was used to stretch the contrast, and the image of detail layer was enhanced by adaptive gain control method. The fusion coefficient was determined adaptively based on the ratio of the number of effective gray values to the total gray in the image of basic layer, and the image of basic layer was fused with the image of detail layer to obtain the enhanced image. Three sets of low-level-light environments with different illumination levels were set by the low-level-light experimental platform for experimental simulation, which verified that the algorithm obtained better results in maintaining edge information and enhancing details. Experimental results show that the proposed algorithm is superior to the previous algorithm in objective evaluation of standard deviation, information entropy, and average gradient, which improves the imaging effect of low-level-light image. |
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| Keywords: | adaptive fusion image enhancement image layering low-level-light environment sub-window filtering |
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