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题名	非制冷红外图像时空噪声去除方法研究
作者	张盛伟
学位类别	硕士
答辩日期	2017-05-24
授予单位	中国科学院沈阳自动化研究所
授予地点	沈阳
导师	向伟
关键词	非均匀性校正 条纹噪声 引导滤波器 随机噪声
其他题名	Research of Temporal-Spatial Noise Removal Method for Infrared Images
学位专业	模式识别与智能系统
中文摘要	在红外成像系统中，红外焦平面阵列探测器接收外红外辐射信号并将其转换成电信号，以电压或者电流的形式经探测器的读出电路输出，信号处理电路采集电信号并将其处理成可用于显示或存储的红外图像。理想情况下，对于固定辐射目标，焦平面阵列探测器输出的电信号应该是一个稳定值，但在实际情况中输出值通常会产生随机的波动，这种随机的波动就是生成红外图像中的时域随机噪声。同时红外焦平面阵列中不同列像元在读出电路中对应不同的列通道，每个列通道内的放大器具有不同1/f噪声特征，这些1/f噪声反映在红外图像中就是空域条纹噪声。时空噪声的存在降低了图像质量，影响图像的视觉效果。本文主要研究的是非制冷红外图像时空噪声的去除方法，目的是提高红外图像的信噪比，提高图像中目标物体检测、识别的准确率。对于空域条纹噪声的去除，本文首先深入分析了一种利用均值滤波器处理模糊图像提取条纹噪声校正项的方法，并针对这种方法的不足提出了基于引导滤波的红外图像条纹噪声去除方法。在本文方法中，首先对噪声图像进行运动检测和竖直方向边缘信息检测，对边缘信息较少的运动图像进行时域低通滤波得到模糊图像；然后利用引导滤波器处理模糊图像提取条纹噪声校正项；最后利用该噪声校正项对噪声红外图像进行校正。对于时域随机噪声的去除，本文在传统时域递归滤波的基础上提出了结合运动补偿的改进时域递归滤波法，用来抑制静止场景图像和缓慢运动场景图像中的随机噪声。其中运动补偿可以避免运动场景图像中产生拖尾现象，高斯函数可以根据图像帧间差值自适应改变滤波系数。 同时，基于课题组现有的非制冷红外成像硬件系统，本文给出了以红外图像时空噪声去除方法为核心的非制冷红外成像软件系统工程化设计方案。在该软件系统设计方案中集成了非均匀性校正、盲元替换、时空噪声去除以及动态范围压缩等模块。最后，利用C++语言结合MFC库编程实现了该软件系统。
英文摘要	In the infrared imaging system, the infrared focal plane array detector receives the external infrared radiation signal and converts it into an electrical signal, which is exported in the form of voltage or current through the detector's readout circuit. The signal processing circuit collects and processes the electrical signal into an infrared image that can be used for display or storage. Ideally, for fixed radiation targets, the electrical signal output by the focal plane array detector should be a stable value, but in actual cases the output value usually produces a random fluctuation. This random fluctuation is the temporal domain random noise in the infrared image. At the same time, different column pixels in the infrared focal plane array correspond to different column channels in the readout circuit. The amplifiers in each column channel have different 1/f noise characteristics. These 1/f noise characteristics is the spatial domain stripe noise in the infrared image. The presence of temporal-spatial noise reduces the image quality and affects the visual effect of the image. The purpose of this paper is to improve the signal-to-noise ratio of infrared image and improve the accuracy of target detection and recognition in the image. For the removal of the spatial domain stripe noise, this paper firstly analyzes a method of using the mean filter to extract the fringe noise correction term from the blurred image, and then proposes a stripe noise removal method based on guided filtering. In our method, the motion image detection and the vertical direction edge information detection are carried out the infrared image, and the moving image with less edge information is subjected to temporal-domain low-pass filtering to obtain the blurred image; then the stripe noise correction term can be extracted from blurred image using guided filter; finally, the stripe noise correction term is used to correct the noise infrared image. For the removal of the temporal domain random noise, this paper proposes an improved temporal recursive filtering based on motion compensation carried on traditional time domain iterative filtering to suppress random noise in still scene images and slow motion scene images. The motion compensation avoids the occurrence of tailing in the motion scene image, and the Gaussian function can adaptively change the filter coefficient according to the inter-frame difference. At the same time, based on the existing uncooled infrared imaging hardware system of the research group, this paper gives the engineering design scheme of the uncooled infrared imaging software system with the infrared image space-time noise removal method as the core. In the software system design scheme, non-uniformity correction, blind element replacement, space-tim e noise removal and dynamic range compression module are integrated. Finally, the software system is realized by using C++ language and MFC library programming.
语种	中文
产权排序	1
内容类型	学位论文
源URL	[http://ir.sia.cn/handle/173321/20537]
专题	沈阳自动化研究所_光电信息技术研究室
推荐引用方式 GB/T 7714	张盛伟. 非制冷红外图像时空噪声去除方法研究[D]. 沈阳. 中国科学院沈阳自动化研究所. 2017.

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