基于多波段光学成像及链路优化的微弱目标探测技术研究
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摘要
微弱目标光学成像探测技术是一项在军事和民用等领域有着广泛而重要应用前景的技术。目标可以反射太阳或其他光源照射的光波,同时也发射各种红外光波,在光学链路成像探测中,存在诸多退化因素,如光学系统的像差、传感器的电子类噪声、景物与传感器的相对运动等,这些都会造成目标成像信杂、噪比的降低；此外,远距离的成像导致目标在图像中所占面积小像素少、缺乏诸多细节特征,易淹没在复杂背景中。这些因素共同造成了目标的微弱特性。随着探测距离的日益增大、对伪目标的探测要求的不断提高,利用传统方式进行光学成像微弱目标探测遇到了不小的挑战,严重制约了目标探测技术的发展与应用。如何解决传统光学成像探测问题、有效提高复杂背景下微弱目标的探测能力是本文重点研究的内容。本文通过优化技术对成像链路中存在的退化进行补偿,利用多种手段提高目标图像信杂、噪比,并研究具有优秀性能的目标探测方法,多模式地提升微弱目标探测的能力,在低虚警率下获得尽可能高的探测率,以适应各类应用的需求。
分析了微弱目标多模式探测技术的框架。根据链路化成像的原理,分析了多模式目标探测的可行性。讨论与分析了典型目标与背景的光学辐射反射特性等,这些特性可用来作为目标探测与背景抑制的参考因素。给出了多模式微弱目标探测的实验设计,包括图像信息获取设备,信息采集平台与获取系统,室内实验设计等等。分析了微弱目标测定与判定的方法,设定判别的要求,为具体研究微弱目标探测提供基础。
为了提升成像信杂、噪比,针对成像链路中存在的模糊退化,进行了各种补偿机制的分析与研究。分析了典型的图像退化模型。在介绍多种典型的图像复原方法,分析这些复原模型、介绍常用图像质量评价方法的基础上,提出了一种参考型的基于梯度的波纹图像质量评价方法,提出了一种无参考型的结合边缘宽度和模糊度衡量的评价算法。针对图像复原,提出了局部约束的Richardson-Lucy方法,引入空间加权矩阵以实现局部约束以解决振铃抑制与细节保留的平衡问题；提出了梯度约束规整化快速复原方法,在保留重要细节的同时极大地抑制了噪声。
为了提升红外成像信杂、噪比,对红外成像中的噪声抑制与优化补偿进行了研究。分析了红外非均匀性噪声退化的原理,并推导了非均匀性校正模型。以图像优化为突破口,提出了基于场景的单幅图像条纹非均匀性校正方法；在校正模型的基础上,提出了基于场景的序列图像法非均匀性校正方法,对于354×236红外图像,每帧处理时间20ms左右,完全满足实时性要求。
在微弱目标增强与双波段图像信息的融合方面展开了深入地研究。首先,提出了一种利用局部频率调谐显著性法实现微弱目标增强的方法,其能够针对性地提升微弱目标与背景的强度对比,适用于实时处理系统。其次,为了综合利用多波段的图像信息,提出了结合多尺度分析与显著性提取的双波段图像信息融合的思路,原始图像的信息能够得到保留甚至增强。
根据目标在图像中的特征特点,提出了多种目标探测的新思路。根据目标区域往往相对对比度较大的特性,提出了基于傅里叶变换法显著性提取的目标探测方法获取目标；考虑到目标通常存在于局部差异性大的区域,提出了局部相似度差异的目标探测方法；为了精确探测目标,提出了一种使用稀疏表征理论的自动目标探测方法；为了实时目标探测应用,提出了一种基于显著性提取与形态学抑制噪声的实时目标探测方法。
The weak target detection technology using optical imaging is widely applied in military and civil fields. There exist lots of degradation factors in the optical chain imagery detection, such as aberration of pptical system, electronic noise of imaging sensor, relative movements between scene and sensor, which would lead to the low signal-to-noise (SNR) and signal-to-clutter ratio (SCR) of target image. Moreover, because of long-distance imaging, target is small and lack of detail characteristic, target signal is so weak relative to background clutter and noise. All of those factors lead to the weak characteristics of target. With the increasement of demand for long-distance detection and low false alarm rate, traditional optical detection methods have been unable to meet demand. How to solve the traditional optical imaging detection problem under complicated background and effectively improve the ability of weak target detection is the key research point in this paper. Using image pre-processing technology for optimization and compensation in imaging chain, multiple means are adopted to improve the SNR of target image. And the author tries his best to design excellent algorithm for target detection, to achieve high Probability of Detection (PD) with low Probability of False Alarm (PFA), and improve the ability of weak target detection to satisfy the demand of many kinds of application.
The framework of multi-mode weak target detection technology is analyzed. Based on pricinple of full-link imaging, the author analyzes the feasibility of multi-mode target detection technology. We have discussed the optical reflection and radiation character of typical target and background, which could be used for target detection and background suppression. And we give out the experimental design for multi mode weak target detection, including image sensor, experimental platform and system, indoor experiment design and so on. Finally, we figure out the discriminant rules for weak target.
In order to improve imaging SNR. we try to analyze compensation mechanism based on degradation in imaging chain. The typical models of image degradation are introduced. Several typical image restoration methods are introduced and analyzed. We propose one full-reference method and one no-reference algorithm for image quality assessment, named gradient-based ripple image quality assessment approach and the algorithm combined edge width and blurring, respectively. For image restoration, we propose local constrained Richardson-Lucy deconvolution approach, introducing spatial weight matrix for local constraint which can solve the balance between ringing suppression and details preserving. And we also propose the gradient constraint regularized fast image restoration methos, which does well in edge preserving and reduce noise.
In order to improve infrared imaging SNR, we also try our best to analyze the noise suppression and compensation in infrared imagery. We deduce the non-uniformity correction model. In the field of non-uniformity correction, two methods are designed, the one is based on single image, and the other need sequence images. The sequence images based approach works real time,0.02seconds for each frame with size of354x236.
A lot of work has been done in weak target enhancement and multi-band image fusion. Firstly, with the help of local frequency based saliency extraction, we propose an enhancement method, which could strength the contrast between target and background. Secondly, in order to well utilize the information of multi-band images, we combine multi scale analysis and saliency extraction technologies together to design the image fusion algorithm, which can keep even enhance the details of original image.
According to the features of target in image, we propose several ideas for target detection. Considering the high constrast in target area, a target detection method based on saliency detection in Fourier domain is proposed. Because those targets usually exist in region with large local difference, local similarity difference is used for target detection. In order to detect target accurately, we make use of sparse representation theory for e automatic target detection. Finally, a real-time automatic small target detection method using saliency extraction and morphological theory is designed.
分析了微弱目标多模式探测技术的框架。根据链路化成像的原理,分析了多模式目标探测的可行性。讨论与分析了典型目标与背景的光学辐射反射特性等,这些特性可用来作为目标探测与背景抑制的参考因素。给出了多模式微弱目标探测的实验设计,包括图像信息获取设备,信息采集平台与获取系统,室内实验设计等等。分析了微弱目标测定与判定的方法,设定判别的要求,为具体研究微弱目标探测提供基础。
为了提升成像信杂、噪比,针对成像链路中存在的模糊退化,进行了各种补偿机制的分析与研究。分析了典型的图像退化模型。在介绍多种典型的图像复原方法,分析这些复原模型、介绍常用图像质量评价方法的基础上,提出了一种参考型的基于梯度的波纹图像质量评价方法,提出了一种无参考型的结合边缘宽度和模糊度衡量的评价算法。针对图像复原,提出了局部约束的Richardson-Lucy方法,引入空间加权矩阵以实现局部约束以解决振铃抑制与细节保留的平衡问题；提出了梯度约束规整化快速复原方法,在保留重要细节的同时极大地抑制了噪声。
为了提升红外成像信杂、噪比,对红外成像中的噪声抑制与优化补偿进行了研究。分析了红外非均匀性噪声退化的原理,并推导了非均匀性校正模型。以图像优化为突破口,提出了基于场景的单幅图像条纹非均匀性校正方法；在校正模型的基础上,提出了基于场景的序列图像法非均匀性校正方法,对于354×236红外图像,每帧处理时间20ms左右,完全满足实时性要求。
在微弱目标增强与双波段图像信息的融合方面展开了深入地研究。首先,提出了一种利用局部频率调谐显著性法实现微弱目标增强的方法,其能够针对性地提升微弱目标与背景的强度对比,适用于实时处理系统。其次,为了综合利用多波段的图像信息,提出了结合多尺度分析与显著性提取的双波段图像信息融合的思路,原始图像的信息能够得到保留甚至增强。
根据目标在图像中的特征特点,提出了多种目标探测的新思路。根据目标区域往往相对对比度较大的特性,提出了基于傅里叶变换法显著性提取的目标探测方法获取目标；考虑到目标通常存在于局部差异性大的区域,提出了局部相似度差异的目标探测方法；为了精确探测目标,提出了一种使用稀疏表征理论的自动目标探测方法；为了实时目标探测应用,提出了一种基于显著性提取与形态学抑制噪声的实时目标探测方法。
The weak target detection technology using optical imaging is widely applied in military and civil fields. There exist lots of degradation factors in the optical chain imagery detection, such as aberration of pptical system, electronic noise of imaging sensor, relative movements between scene and sensor, which would lead to the low signal-to-noise (SNR) and signal-to-clutter ratio (SCR) of target image. Moreover, because of long-distance imaging, target is small and lack of detail characteristic, target signal is so weak relative to background clutter and noise. All of those factors lead to the weak characteristics of target. With the increasement of demand for long-distance detection and low false alarm rate, traditional optical detection methods have been unable to meet demand. How to solve the traditional optical imaging detection problem under complicated background and effectively improve the ability of weak target detection is the key research point in this paper. Using image pre-processing technology for optimization and compensation in imaging chain, multiple means are adopted to improve the SNR of target image. And the author tries his best to design excellent algorithm for target detection, to achieve high Probability of Detection (PD) with low Probability of False Alarm (PFA), and improve the ability of weak target detection to satisfy the demand of many kinds of application.
The framework of multi-mode weak target detection technology is analyzed. Based on pricinple of full-link imaging, the author analyzes the feasibility of multi-mode target detection technology. We have discussed the optical reflection and radiation character of typical target and background, which could be used for target detection and background suppression. And we give out the experimental design for multi mode weak target detection, including image sensor, experimental platform and system, indoor experiment design and so on. Finally, we figure out the discriminant rules for weak target.
In order to improve imaging SNR. we try to analyze compensation mechanism based on degradation in imaging chain. The typical models of image degradation are introduced. Several typical image restoration methods are introduced and analyzed. We propose one full-reference method and one no-reference algorithm for image quality assessment, named gradient-based ripple image quality assessment approach and the algorithm combined edge width and blurring, respectively. For image restoration, we propose local constrained Richardson-Lucy deconvolution approach, introducing spatial weight matrix for local constraint which can solve the balance between ringing suppression and details preserving. And we also propose the gradient constraint regularized fast image restoration methos, which does well in edge preserving and reduce noise.
In order to improve infrared imaging SNR, we also try our best to analyze the noise suppression and compensation in infrared imagery. We deduce the non-uniformity correction model. In the field of non-uniformity correction, two methods are designed, the one is based on single image, and the other need sequence images. The sequence images based approach works real time,0.02seconds for each frame with size of354x236.
A lot of work has been done in weak target enhancement and multi-band image fusion. Firstly, with the help of local frequency based saliency extraction, we propose an enhancement method, which could strength the contrast between target and background. Secondly, in order to well utilize the information of multi-band images, we combine multi scale analysis and saliency extraction technologies together to design the image fusion algorithm, which can keep even enhance the details of original image.
According to the features of target in image, we propose several ideas for target detection. Considering the high constrast in target area, a target detection method based on saliency detection in Fourier domain is proposed. Because those targets usually exist in region with large local difference, local similarity difference is used for target detection. In order to detect target accurately, we make use of sparse representation theory for e automatic target detection. Finally, a real-time automatic small target detection method using saliency extraction and morphological theory is designed.
引文
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