基于压缩感知和红外玫瑰线扫描的红外图像重构

红外亚成像制导技术是由点源探测技术到成像制导技术的一种过渡, 由单元探测器和光机扫描装置组成.红外玫瑰线扫描亚成像系统是亚成像制导中的一种, 红外玫瑰线扫描亚成像系统按照特定的图案采集视场中的部分数据并得到一幅含有目标位置信息的亚图像.受单像素相机的启发, 主要研究红外玫瑰线扫描亚成像系统中的压缩成像.压缩感知可以在更少的采样数据条件下重构红外图像, 其应用到红外亚成像制导系统中一个关键的问题就是观测矩阵的构造.关于随机观测矩阵的研究已经比较广泛, 但随机矩阵很难实现.本文提出了一种简单的适用于红外玫瑰线扫描亚成像系统的确定性观测矩阵.此外还提出了一种快速有效的恢复算法, 称为优化子空间追踪算法.仿真结果显示构造的观测矩阵能够压缩和重构红外图像, 且重构效果优于随机高斯观测矩阵和随机伯努利观测矩阵, 提出的恢复算法也具有较好的表现.

Infrared image reconstruction based on compressed sensing and infrared rosette scanning

Infrared (IR) sub-imaging guidance technique, which combines the single detector and optical scanning device, is a transition from point-source detection technique to imaging guidance technique. Infrared rosette scan sub-imaging system (IRSSIS) is a class of sub-imaging guidance system. The IRSSIS samples part data of the field of view (FOV) according to a specific pattern and obtains a sub-image including the position information of targets. Compressive imaging in the IRSSIS was studied inspired by the single pixel camera. Compressed sensing (CS) will help to reconstruct IR image in the condition of much fewer samples. The key problem of CS applied to the IRSSIS is the measurement matrix construction. While random measurement matrix has been studied intensively, it is hard to implement. A simple deterministic measurement matrix was proposed for the IRSSIS. Furthermore, a fast and effective recovery algorithm, optimized subspace pursuit algorithm (OSP), was proposed. Simulation results show that the proposed measurement matrices can compress and reconstruct IR image prior to the random Gaussian measurement matrices and random Bernoulli measurement matrices. The proposed recovery algorithm also has a better performance.