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1、红外图像非均匀性校正和增强技术研究工学硕士学位论文红外图像非均匀性校正和增强技术研究硕士学位论文摘 要由于红外成像系统具有抗干扰性能强、目标识别能力强、被 动工作等特点，已被广泛的应用于军事和民用领域，是具有广阔 发展前景的一种成像技术。因此，研制出高性能的红外成像系统 具有重要的意义。 红外焦平面阵列是成像系统的最重要的器件，它的性能直接 影响着整个系统的运行。但是，受到当前技术水平和工艺水平的 限制，红外焦平面阵列存在严重的非均匀性，并且红外图像具有 对比度低、目标与背景区分不明显的缺点，针对红外焦平面阵列 存在的这些问题，本文给出了有效的解决方法，主要工作如下： （1）针对传统基于神经网
2、络校正算法结果存在图像模糊和 伪像的问题，本文在分析问题产生原因的基础上，提出了有效的 改进算法：使用可变参数非线性滤波器代替原算法中的均值滤波 器。改进算法中使用的非线性滤波器参数可以根据局部图像细节 信息的不同作相应的变化，校正结果不仅在清晰度方面有明显的 改善，而且有效的消除了传统算法中存在伪像的问题。 （2）在分析了盲元产生原因以及盲元与点目标之间区别的 基础上，提出了基于二维线性外推理论的盲元检测算法，并在实 验中对其进行了验证，有很好的检测效果。 （3）针对红外图像具有整体亮度偏暗、对比度较低、目标 与背景区分不明显的特点，结合模糊集理论、灰度变换和多分辨 率图像融合等理论，提出了
3、一种新的基于模糊集理论的红外图像 增强算法。它在提高图像对比度的同时，能够更好的保留原始图 像的细节信息，使得处理后的图像边缘信息更加明显。 通过本文的研究，达到了红外图像非均匀性校正、盲元补偿 和图像增强的目的，这也为后续的图像处理和应用打下了坚实的 基础。 关键词：红外焦平面阵列；非均匀性校正；盲元补偿；图像增强红外图像非均匀性校正和增强技术研究ABSTRACTABSTRACTInfrared imaging system has the characteristic of strong anti-jamming performance ，target recognition capabi
4、lity and also passive work,. It has been widely used in military and civilian fields. The imaging technique has broad prospects for development. So, there is important significance for the development of a high performance infrared imaging system. Infrared focal plane array is a key part of thermal
5、imaging system. Its performance affects the operation of the system directly. However, limited by current technology, infrared focal plane arrays has the problem of non-uniformity, and infrared images have the features of low contrast; it is hard to distinguish between target and background. This pa
6、per presents many effective methods to solve these problems which exist in the infrared focal plane arrays. In this paper, more contexts are given: (1) The results obtained by traditional neural network correction algorithm, have the problems of blurring effect and some existing artifacts. After ana
7、lysis the reasons of the problems that the traditional neural network correction algorithm may cause, in this article an improved algorithm is proposed: replace the mean filter, which used in the traditional algorithm by the nonlinear filter. And the parameters of nonlinear filter are variable accor
8、ding to the local image detail changes in the improved algorithm. The corrected images by the improved algorithm achieve not only a significant improvement in image clarity, but also effectively eliminate the problem of artifacts caused by the traditional algorithms.硕士学位论文(2) After analyzed the caus
9、e of blind pixels and the difference between blind pixels and point targets, an effective detection algorithm was proposed which based on 2D linear extrapolation theory. The experiment shows that the proposed detection algorithm was more effective than the original algorithm, it verify the effective
10、 of the detection algorithm. (3) Infrared images have the features of dark and low contrast; it is hard to distinguish between target and background. This article combining with the fuzzy set theory, gray-scale transformation theory and multi- resolution image fusion theory, proposed a new image enh
11、ancement algorithm. The algorithm not only can improve the contrast of infrared images, but also can keep details of original images. So, the edge information of the processed image more apparent. In this article we achieve the purpose of Infrared image non-uniformity correction, Blind pixel compens
12、ation and Image Enhancement. And also lay the foundation for our future work. KeyKey wordswords: Infrared focal plane array; Non-uniformity correction; Blind pixels compensation; Image enhancement; 红外图像非均匀性校正和增强技术研究目 录第一章 绪论.1 1.1 红外热成像技术研究现状 .1 1.2 课题的研究目的和意义 .2 1.3 红外图像预处理算法综述 .3 1.3.1 红外图像非均匀性校正研
13、究现状 .3 1.3.2 红外图像盲元检测的研究现状 .4 1.3.3 红外图像增强的研究现状 .5 1.4 本文的主要工作及内容安排 .6 第二章 红外成像非均匀性产生的原因和定义.7 2.1 红外焦平面阵列成像特点 .7 2.2 红外焦平面阵列非均匀性产生的原因 .8 2.2.1 系统自身因素带来的非均匀性 .8 2.2.2 外界影响引入的非均匀性 .8 2.3 红外焦平面阵列空间响应非均匀性的定量评价 .9 2.4 本章小结 .10 第三章 红外图像非均匀性校正算法.11 3.1 基于定标的非均匀性校正算法 .11 3.1.1 一点温度定标法 .11 3.1.2 两点温度定标法 .13 3.2 基于场景的非均匀性校正算法 .14 3.2.1 基于卡尔曼滤波的非均匀性校正算法 .14 3.2.2 基于神经网络的非均匀性校正算法 .19 3.2.3 改进的神经网络校正算法 .24 3.3 本章小结 .28 第四章 盲元检测算法分析.30 4.1 盲元的定义 .30 4.2 盲元的特点 .31 4.3 盲元的检测和补偿 .32 4.3.1 基于统计量的检测 .32 4.3.2 基于双参考源的检测算法 .33 4.3.3 本文算法 .33硕士学位论文4.4 实验结果分析 .36 4.5 本章小结 .37 第五章 红外图像细节增强.
