Image Processing Framework for Performance Enhancement of Low-Light Image Sensors
Image Processing Framework for Performance Enhancement of Low-Light Image Sensors
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DOI：
10.1109/jsen.2020.3044392
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发表时间：
2021-03
影响因子：
4.3
通讯作者：
M. Purohit;A. Chakraborty;Ajay Kumar;Brajesh Kumar Kaushik
中科院分区：
文献类型：
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作者：
M. Purohit;A. Chakraborty;Ajay Kumar;Brajesh Kumar Kaushik
夜视设备用于在弱光条件下($<；，\，\，10^{-1}$lux)捕获图像，这与标准照明下的图像有很大不同。与其他夜视设备相比，基于像增强管的夜视设备具有功率和成本优势，例如用于近距离监视的热成像。然而，这些视觉设备会受到闪烁噪声的影响，这会使产生的图像在非常弱的光线条件下产生噪声。本文提出了一种综合的方法，利用一个统一的图像处理模块来提高基于像增强器的夜视设备采集的微光图像的性能。所提出的模块是一个两阶段图像处理框架的组合。首先，使用基于小波和递归帧平均的时空滤波器来最小化帧间和帧内噪声。然后，在应用用于显示映射的图像色调调整之前，执行图像增强，然后使用局部空间滤波器来降低残余噪声。所提出的模块的结果表明，夜视设备在受控的模拟光条件下以及在野外条件下的性能都得到了提高。客观和主观分析还表明，该模块可以有效地降低噪声，并显著提高视觉图像质量20%-30%。
Night vision devices are used for capturing images under low light conditions ( $ < \,\,10^{-1}$ lux), which are significantly different from images under standard illumination. The night vision devices based on image intensifier tube have power and cost advantages over other night vision devices, such as thermal imaging for short-range surveillance. However, these vision devices suffer from scintillation noise, which makes the resulting images noisy under very low light conditions. This paper presents a comprehensive methodology that uses a consolidated image processing module to improve the performance of low-light images acquired using image-intensifier-based night vision devices. The proposed module is a combination of a two-stage image processing framework. First, a spatio-temporal filter (based on wavelet and recursive frame averaging) is used to minimize inter-frame and intra-frame noise. Then, image enhancement is performed followed by residual noise reduction using a local spatial filter before applying image tone adjustment for display mapping. The results of the proposed module demonstrate performance enhancement of night vision devices under controlled simulated light conditions as well as field conditions. Objective and subjective analysis also reveals that the proposed module can effectively reduce noise and significantly improve visual image quality by 20–30%.