【作者】 陈立；

【导师】 熊金涛；

【作者基本信息】 电子科技大学 ， 信号与信息处理， 2013， 硕士

【摘要】 被动毫米波成像系统利用物体自身的毫米波段辐射的能量来差异实现成像。其对于雨、雾和衣物等具有良好的穿透性，且能在各种气候条件下全天时进行成像，因而其在场景监视和安全检查等方面具有重要而广泛的应用前景。但是，由于其成像系统体制上的特点，和硬件系统实现上的一些限制，也使其具有一些技术上的缺点。一方面，受限于波长及系统天线孔径，毫米波成像往往具有较低的空间分辨率，且通过改进硬件来提高被动毫米波成像的分辨率十分困难且代价高昂。另一方面，为满足实际的应用需求，系统往往采用多通道成像体制，但受限于接收通道技术及其制造工艺，使得各通道的响应参数存在不一致性和时变性，这将导致所成图像存在条带噪声。本论文针对毫米波图像的条带噪声抑制问题及超分辨率问题开展了以下工作：1.研究了被动毫米波成像原理，分析了系统成像的数学模型。研究了统计学习理论，分析了影响学习模型性能的关键因素。2.对多通道体制下图像条带噪声的成因进行了研究，推导和建立了相关的数学模型。在此基础上，使用正则化方法，研究并设计了基于最小二乘准则的条带噪声抑制方法，同时还根据场景连续性设计了行均值优化算法，并进行了实验验证。3.对毫米波图像的特点进行了研究，并在此基础上，对毫米波图像的先验模型进行了研究和建模。4.研究毫米波图像特点，建立了被动毫米波成像系统点扩展函数模型。结合毫米波图像的稀疏线梯度（SLG）先验模型，设计了盲超分辨率复原算法，并通过实验验证了其性能。5.研究了支持向量回归方法，分析了影响超分辨率复原算法处理效果的因素，以及学习模型与计算复杂度间的关系。在此基础上，将信号的稀疏分解引入到超分辨率复原当中。通过仿真实验，验证了算法的有效性。综上所述，本论文中研究的算法，能够有效地提高被动毫米波成像的质量和图像处理的效果，具有重要的学术意义和优良的应用价值。更多还原

【Abstract】 Passive millimeter wave imaging system using objects millimeter-wave radiationenergy differences to achieve imaging, with good penetrating for rain, fog and clothing,and can all-time imaging in a variety of weather conditions, so it has broad applicationprospects in the scene monitoring and security checks. However, due to thecharacteristics of the imaging system, it also has some technical shortcomings. On theone hand, subject to the wavelength and the antenna aperture, the millimeter waveimage obtained tend to have a lower spatial resolution, and the fuzzy extent. To improvethe resolution of the passive millimeter wave imaging by improving the hardware isvery difficult and costly. On the other hand, in order to meet the applicationrequirements, there are often multi-channel imaging system, but was limited by toaccept channel technology and its manufacturing process, making the responseparameters of each channel there are inconsistencies and time-varying, which will resultinto an image the presence of noise bands. In this thesis, the following work which formillimeter wave image stripe noise suppression and super-resolution had carried out:1. Passive millimeter wave imaging theory, the mathematical model of the imagingsystem. Statistical learning theory, analysis of the key factors that affect learningmodel performance.2. The causes of stripe noise for multi-channel system, derivation and establish of themathematical model. On this basis, design stripe noise suppression algorithm usingthe regularization and least squares criterion. and line mean optimization algorithmusing the scene continuity, and its effectiveness is verified by experiment.3. The characteristics of millimeter wave images were studied, and on this basis, apriori model of the millimeter wave image and modeling.4. Study the reconstructed based method super-resolution algorithm, established thepoint spread function of passive millimeter wave imaging system, and designed theblind super-resolution algorithm using the sparse line gradient(SLG) priori model,and its performance is verified by experiment. 5. Research the support vector regression method, analysis the factor which affectedthe super-resolution algorithm performance, and the relationship between learningmodel and computing complexity. Introduced the sparse decomposition into thesuper-resolution, and its performance is verified by experiment.In summary, the algorithm described in this paper, can effectively improve theeffect of passive millimeter wave imaging quality and image processing, with importantacademic significance and excellent value for the research and design of millimeterwave imaging and its image processing algorithms.更多还原