面向运动目标检测识别的高分辨光学压缩成像理论与技术

项目名称： 面向运动目标检测识别的高分辨光学压缩成像理论与技术

项目编号： No.61271375

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 无线电电子学、电信技术

项目作者： 陈靖

作者单位： 北京理工大学

项目金额： 70万元

中文摘要： 课题突破现有光学成像模式，研制新型高分辨压缩成像系统，并基于该成像系统开展稀疏域运动目标感知新方法研究。解决传统成像系统"高速采样"再"压缩"所造成的采样资源浪费，以及现有视觉算法需要对高维图像数据进行降维处理的计算资源浪费问题。课题采用分块编码孔径透镜阵列实现大视场范围的目标非自适应线性投影，在小面阵、低分辨的图像传感器阵列上实现稀疏采样图像的高分辨成像。针对压缩成像系统的实际物理器件约束，开展测量矩阵非负补偿算法，光能非负条件下的图像快速重构算法研究，根据压缩成像系统的数据获取体制，探索稀疏域的运动目标检测识别算法，为压缩成像机理下的目标特征理解提供新思路。课题的研究成果能够有效缓解现有数字成像系统高速采样实现的压力，减少数据存储、传输代价，缩短信号后处理所需的时间和计算成本。为推进现有目标感知模式向轻量化、普适化和网络化方向的发展奠定坚实的基础。

中文关键词： 压缩成像；编码孔径；运动目标检测；目标识别；信号重构

英文摘要： The emerging theory of compressed sensing has potentially powerful implications for the design of optical imaging devices and its application fields. In this project we aim to develop a high resolution compressive imaging system, which has the characteristics of small size, simple structure and low energy consumption. Based on this compressive imaging system, we will apply it into wide-area video surveillance fields and explore a completely new moving target perceptual modal. Actually, the state of art feature extraction method always need complex algorithm to reduce the data dimentation, which waste a lot of computation resources. In order to solve this problem, we propose to realize object detection and recognition algorithem with sparse imaging representation. Although compressed sensing theory suggests that one can recover a scene at a higher resolution than is dictated, this remarkable result comes with some important caveats, especially when practical issues associated with physical implementation are taken into account. In this project, we discusses compressed sensing in the context of optical imaging devices, emphasizing the practical hurdles related to building such devices, and offering optical algorithms for overcoming these hurdles. Coded aperture lens array are used to modulate the space light come

英文关键词： compressive imaging；coded aperture；object detection；object recognition；image reconstruction