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被动毫米波图像超分辨复原算法及DSP实现
【作者】 李晶；
【导师】 杨建宇；
【作者基本信息】 电子科技大学 ， 信号与信息处理（专业学位）， 2012， 硕士
【摘要】 被动毫米波成像是通过接收物体毫米波频段的辐射能量差异来实现成像。与光学或X光成像技术相比,因其穿透性强、无辐射、隐蔽性高,在军事侦察、航空安检、反恐斗争等领域具有重要应用价值,也是国内外研究的热点。超分辨复原是被动毫米波成像信号处理的关键技术,能恢复由成像系统低通效应所丢失的带外的高频信息,提高成像分辨率。本文针对被动毫米波图像超分辨复原算法及DSP实现问题,主要研究内容如下：1.分析对比Richardson-Lucy算法、投影Landweber算法、图像空间重构ISRA算法等基于统计优化理论的超分辨复原算法,给出了各算法的计算复杂度,为超分辨算法DSP实现奠定基础。2.提出了基于PCA和小波分解的自适应正则化超分辨算法,能够有效利用毫米波图像局部灰度相关性,与传统超分辨算法相比,有更好复原效果。3.基于ADSP TS-201硬件平台,完成了实时信号处理单元方案设计,实现了数据传输通信,定标通道校正、图像数据重排、通道均衡去条带等图像预处理及超分辨算法函数库等功能。4.完成了被动毫米波成像系统的联调测试,以及针对多种目标和场景的单通道和多通道系统外场成像实验。最后通过仿真和实测数据验证了基于PCA和小波分解的自适应正则化超分辨复原算法的有效性和成像系统信号处理单元的功能性能。
【Abstract】 Passive millimeter-wave (PMMW) imaging system receives radiation energy differences of the objects in the millimeter wave band to achieve imaging. Compared with the optical or X-ray imaging technology, because of its strong penetrating, no radiation and concealment, PMMW imaging has an unparalleled advantage in the field of the military reconnaissance, aviation security and the counter-terrorism. It has become a hot topic of research at home and abroad.Super-resolution recovery is a key technology for passive millimeter-wave imaging signal processing. It can restore the high-frequency information lost by the low-pass effect of the imaging system, to improve the resolution of the imaging system.This paper focuses on passive millimeter-wave image super-resolution restoration and DSP implementation. The main contents are as follows:1. Comparative analysis the super-resolution recovery algorithms based on statistical optimization theory, including Richardson-Lucy algorithm, projection Landweber algorithm, image space reconstruction algorithm and so on. Estimate computational complexity of the algorithms to lay the foundation for DSP implementation of the super-resolution algorithms.2. Propose an adaptive regularization super-resolution algorithm based on PCA and wavelet decomposition, effective use of a priori information of the millimeter-wave image, better recovery results than the traditional super-resolution algorithm.3. Based on the ADSP TS-201hardware platform, accomplish the design of the real-time signal processing unit of the PMMW imaging system, achieving the functions of data transmission communication, image preprocessing which includes calibration channel correction, image data rearrangement and channel equalization to remove stripe noise, and the super-resolution image recovery function library.4. Accomplish the joint commissioning of the PMMW imaging system; accomplish the field imaging experiments with single and multi channel for a variety of objectives and scenarios.At last, the simulations and experiments validate the effectiveness of the adaptive regularization super-resolution algorithm based on PCA and wavelet decomposition and the signal processing unit functions of the PMMW imaging system.