| 摘要: |
| 利用红外成像系统跟踪空间邻近目标时，目标在红外像平面的成像相互交叠形成簇状像斑，导致跟踪系统无法有效分辨这些目标。基于稀疏重构的超分辨方法是一种将稀疏重构技术应用于处理邻近空间目标分辨的单帧超分辨方法，能够有效估计目标个数和位置等参数。针对二阶锥规划方法求解基于稀疏重构的超分辨模型计算复杂度大、效率低的问题，提出采用分裂Bregman方法求解该超分辨模型，先通过引入辅助变量将原问题转化为一系列易于求解的子优化问题，然后利用交替最小化方法求解每个子问题，最后分析正则化参数的合理设置，从而实现对超分辨模型的最优化求解。仿真实验结果表明，所提方法能够有效求解基于稀疏重构的超分辨模型，在保证分辨像斑中目标个数和位置参数的前提下，计算耗时缩短了8%，有效提高了求解效率，便于工程实现。 |
| 关键词: 空间邻近目标 红外成像 稀疏表示 分裂Bregman |
| DOI： |
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| 基金项目: |
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| An Infrared Super-resolution Algorithm Using Split Bregman for Closely Spaced Objects |
| ZUO Zhiyong |
| (Southwest China Institute of Electronic Technology,Chengdu 610036,China) |
| Abstract: |
| When the infrared imaging system is used to track the closely spaced objects(CSO),the images of the objects in the image plane may overlap each other to form cluster image spots,which makes the tracking system cannot effectively distinguish these objects.The super-resolution model based on the sparse reconstruction technique is an efficient single frame super-resolution method to estimate the number and location of objects,however,the second order cone programming(SOCP) method has high computational complexity and low efficiency in solving this model.In this paper,the split Bregman method is used to solve the super-resolution model.Firstly,the original problem is transformed into a series of sub-optimization problems by introducing auxiliary variables.Then the alternative minimization method is used to solve each sub-problem.Finally,the rational setting of regularization parameters is analyzed to realize the optimal solution of the super-resolution model.Simulation results show that the proposed method can effectively solve the super-resolution model.The time-consuming time is reduced by 8%,which effectively improves the solution efficiency and is convenient for engineering implementation. |
| Key words: closely spaced object infrared imaging sparse representation split Bregman |
《电讯技术》编辑部 