SIA OpenIR  > 光电信息技术研究室
自适应红外成像处理方法研究
其他题名Adaptive Infrared Image Processing
张红辉1,2
导师罗海波
分类号TP391.41
关键词红外焦平面阵列 非均匀性校正 盲元检测 多尺度
索取号TP391.41/Z32/2014
页数107页
学位专业模式识别与智能系统
学位名称博士
2014-05-28
学位授予单位中国科学院沈阳自动化研究所
学位授予地点沈阳
作者部门光电信息技术研究室
摘要红外热成像技术是一项集光、微电子以及信息处理于一体的综合技术,近年来得到了飞速发展,在军事和民用领域得到了广泛应用。红外热成像系统中最重要的元件是红外探测器,然而由于红外探测器在制造过程中对工艺参数比较敏感,普遍存在非均匀性响应和盲元等问题,使得系统的温度分辨率及空间分辨率降低。另一方面,为提高图像层次感,抑制噪声,提高图像信噪比,以及红外图像边缘细节部分增强,从而在提高MRTD的同时不降低红外成像系统的温度分辨率,红外图像的灰度映射(自适应红外图像对比度调节)也是红外成像处理技术中的一项重要研究内容。本文围绕红外成像处理技术中的非均匀性校正、盲元像元检测/补偿以及红外热图像灰度映射三项核心技术进行了自适应红外成像处理方法研究以及实验验证,研究内容主要包括: 围绕红外成像器件探测单元均匀辐照下的非线性响应问题,从满足图像处理任务的实时性出发,依据最小二乘法原理推导了基于多点标定的自适应非均匀性校正方法,并与传统非均匀性校正方法进行了比对,实验结果表明,该方法在满足实时性要求的同时,获得了较好的非均匀性校正效果,残余非均匀性得到了明显改善。在自适应校正方面,针对经典的基于神经网络的自适应非均匀性校正方法存在的诸如目标退化、伪像、对部分种类成像器件不适用等不足,研究并指出了存在问题的原因,提出了多点分段标定与基于双边滤波的神经网络相结合的非均匀性校正方法。结合斯特林制冷型红外成像器件实际图像数据的实验结果表明,该方法具有良好的校正效果。 为有效检测红外焦平面中的盲元,提出了一种基于校正后数据均值(MEAN)和标准差(STD)特征直方图的盲元检测方法。在分析了正常响应像元非均匀性校正后灰度分布模型的基础上,指出均匀辐照下红外焦平面阵列(IRFPA)正常响应像元非均匀校正后灰度图像的均值(MEAN)和标准差(STD)具有正态分布特性,将均值(MEAN)和标准差(STD)的直方图进行高斯分解,并按照3σ的原则进行分布区间合并,其区间之外即为盲元的分布区间。同时,红外图像上的坏点、坏斑或坏线也可以归结为图像的退化问题,针对图像退化问题的求解,提出了基于最大后验概率估计的方法,实验结果表明,该方法提高了图像质量和视觉效果。 针对由于红外成像机理、成像器件本身及外部环境影响导致的红外图像信噪比较低、高背景、低对比度、灰度范围窄等问题,本文以红外图像的特征及经典灰度变换方法为理论基础,分别从提高红外图像对比度、抑制噪声等角度出发,研究了红外图像的灰度映射算法。提出了基于多尺度变换的灰度映射方法,利用多尺度分析这一有力工具,将图像变换到频域进行多尺度分析,采用保留边缘的双边滤波,并利用双边滤波的权重信息来调整高频增强的比例,从而在增强图像边缘细节的同时,有效抑制噪声干扰。同时,通过模糊理论灰度变换的研究,用同等大小的模糊隶属度矩阵来表示原始图像,结合Ostu方法进行自动阈值选取,提高了图像效果。 针对红外成像处理系统,本文围绕自适应图像预处理的快速性以及有效性这一主题开展了理论和技术研究,取得了一些较有意义的研究成果,促进了红外成像理论、方法和技术的发展。
其他摘要Modern infrared technology have got quick and strong development, infrared imaging systems are used in a wider range scope of applications both in military and civilian sectors. The Infrared Focal Plane Array(IRFPA) is an important part of Infrared imaging system.and its spatial photo response nonuniformity and blind pixels mostly affect the performance of the IR imaging system,How to improve the signal noise ratio (SNR) also is a problem. In IR imaging information processing system,nonuniformity correction(NUC) , blind pixels detection for IRFPA and image enhancement techniques are important development issues. This dissertation is arranged around the three issues cited above, The main development are given below: In order to realize the IRFPA non-uniformity (NU) correction with fast convergence speed and high precision, a new method to correct the non-uniformity in infrared images is introduced. The new algorithm takes into account a linear equation between the excusion and corrected data along with the variation of the application surrounding temperature, adaptive correction factor according to the principle of least square. According to the practical application, this algorithm has the advantage of the strong ability to eliminate image degradation, fast convergence speed and high precision. The traditional non-uniformity correction algorithm of infrared image based on neural network exist problems of the ghosting artifact and the target fade-out. To overcome these problems, we propose the enhancement neural network method, which firstly MUL-PONIT correction, then obtains expected values by the edge-preserving filters, in order to guide correction coefficient updating by using the edge of the picture information, and stabilize and accelerate the learning process by using self-adaptive learning rate. The simulating experiment indicates that the new algorithm not only overcomes the problems of the ghosting artifact and the target fade-out, but also fairly reduces the non-uniformity. To detecting the blind pixels effectively, we regard this problem is a image degradation process, So we propose a Image restoration algorithm that uses the maximum a-posteriori method to estimate the original image. Experiments demonstrate that the proposed algorithm achieves better results. Also based on the analysis of the model for the effective pixel corrected response, the approach proposes that, when the corrected response is uniform, the means(MEAN) and the standard deviation (STD) are both normal distributed random variables for all effective pixels. Through adaptive projective decomposition of MEAN and STD histograms , the MEAN and STD distribution range of effective pixel can be effectively estimated, and the detection rule for blind-pixel is got finally. The experiment result by applying real infrared image shows the proposed method for IRFPA blind-pixel detection is scientific and effective. High background, low contrast ratio, narrow gray-level range and low signal noise ratio are the characteristic of IR image. Aimed on these problems, A multi-scale image enhancement algorithm is proposed. On the basis of the multi-scale image decomposition, We use an edge-preserving spatial filter and Contrast is enhancement by applying nonlinear amplification. Experiment results show that the proposed method can enhance the original infrared image effectively and improve the contrast, moreover, it also can reserve the details and edges of the image well.Also the method based fuzzy logic integrating Ostu is designed. Experiment results show that the proposed method can improve the entropy.
语种中文
产权排序1
文献类型学位论文
条目标识符http://ir.sia.cn/handle/173321/14800
专题光电信息技术研究室
作者单位1.中国科学院沈阳自动化研究所
2.中国科学院大学
推荐引用方式
GB/T 7714
张红辉. 自适应红外成像处理方法研究[D]. 沈阳. 中国科学院沈阳自动化研究所,2014.
条目包含的文件
文件名称/大小 文献类型 版本类型 开放类型 使用许可
自适应红外成像处理方法研究.pdf(4995KB) 开放获取CC BY-NC-SA请求全文
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[张红辉]的文章
百度学术
百度学术中相似的文章
[张红辉]的文章
必应学术
必应学术中相似的文章
[张红辉]的文章
相关权益政策
暂无数据
收藏/分享
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。