针对现有算法难以利用单帧红外图像实现空间邻近目标(CSO)的超分辨问题，提出了一种基于稀疏重构理论的单帧超分辨方法。该方法充分利用了目标在焦平面阵列(FPA)分布的稀疏性以及光学系统点扩展函数(PSF)的结构特性，通过对FPA离散化网格采样构造稀疏量测模型，并将建立的1范数正则化问题转化为二阶锥规划问题求解；然后针对稀疏度过估计的重构结果，采用贝叶斯信息准则(BIC)实现模型选择，最终获得对目标个数和位置的准确估计。多组仿真场景验证了算法的有效性和超分辨能力；相比于已有算法，所提算法不仅提高了分辨正确率和位置估计精度，同时大幅缩减了计算耗时。

Since the state-of-the-art methods barely have the capability of super-resolving the closely spaced objects (CSOs) using only single frame data, a super-resolution method based on the sparse reconstruction technique is proposed. The proposed method combines the sparsity of the distribution of CSOs on the focal plane array (FPA) and the structure characteristic of the point spread function (PSF) to construct a sparsely represented measurement model by discretizing the image plane with sampling grids. Then the 1-norm regularization problem is efficiently solved by a second order cone programming framework. For the overestimated sparsity after reconstruction, the Bayesian information criterion (BIC) is utilized for the model selection. The estimated number and positions of CSOs are precisely ascertained at last. Several scenes are set to inspect the efficiency and the super-resolution capability of the proposed method. It indicates that the sparse reconstruction-based method outperforms the existing methods in the ratio of correct detection, the precision of position estimation and the computation load.