To improve the imaging resolution of catadioptric omnidirectional,this project researches on catadioptric omnidirectional compressive imaging and super-resolution reconstruction based on compressed sensing. Firstly, the optical structure of catadioptric omnidirectional is designed for super-resolution reconstruction, the compressive imaging model is builded for analyzing the performance of compressive imaging. Secondly, a nonuniform measurement matrix is proposed for catadioptric omnidirectional compressive imaging and the optimization method of measure matrix is studied. Finally, the method for nonuniform super-resolution reconstruction is proposed. The characteristics and innovation of this project lie in apllying the principles of compressed sensing for catadioptric omnidirectional imaging, compressive imaging with nonuniform measurement matrix, solving simultaneously the low image resolution and the nonuniform resolution between external ring and internal ring in omnidirectional image through super-resolution reconstruction. Research results will significantly expand the space of omnidirectional vision theoretical research and practical application, and will further promote the application space of catadioptric omnidirectional imaging system in many fields, such as in military reconnaissance, robots navigation, panoramic video surveillance, virtual scene walk.
本项目从提高折反射全向成像系统成像分辨率出发,研究基于压缩感知的折反射全向压缩成像及超分辨率重构方法。首先,研究设计适用于超分辨率重构的折反射全向压缩成像系统光学结构,建立压缩成像模型,分析压缩成像的性能。其次,提出适合折反射全向压缩成像的非均匀测量矩阵,并对测量矩阵的优化方法进行研究。最后,提出折反射全向非均匀超分辨率重构方法。本项目的特色与创新之处在于将压缩感知原理引入折反射全向成像中,采用非均匀测量矩阵压缩成像,通过超分辨率重构同时解决折反射全向图像相对分辨率低和内外环分辨率不均匀的问题。研究成果将扩展全向视觉理论研究和实际应用空间,对折反射全向成像系统在军事远景侦察、机器人视觉导航、全景视频监控、虚拟场景漫游等众多领域的广泛应用产生积极的促进作用。
本课题从提高折反射全向成像系统成像分辨率出发,研究基于压缩感知的折反射全向压缩成像及超分辨率稀疏重构方法。首先,构造适用于折反射全向压缩成像的装置,获取经压缩采样后的全向投影观测像。其次,提出适合折反射全向图像的非均匀测量矩阵,并对测量矩阵的优化方法进行研究。最后,提出折反射全向投影观测像超分辨率稀疏重构方法。本课题的特色与创新之处在于将压缩感知原理引入折反射全向成像中,在折反射成像系统的光学结构设计上进行创新,从成像原理上同时解决折反射全向图像中相对分辨率和内外环分辨率不均匀的问题。研究成果将极大地拓宽全向视觉的理论研究和实际应用空间,对折反射全向成像系统在军事远景侦察、机器人视觉导航、全景视频监控、虚拟场景漫游、远程视频会议等军事和经济社会众多领域的广泛应用和深入推广产生积极的促进作用。
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数据更新时间:2023-05-31
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