针对红外图像中弱小目标的检测问题, 提出了一种基于图像稀疏表示的自适应杂波抑制方法。首先, 采集500帧红外图像样本, 通过训练学习构造包含图像各个层次结构特征的多成分超完备字典; 然后, 通过红外图像的协方差自适应地选择与图像子块对应的超完备字典对图像进行稀疏表示, 利用匹配追踪算法得到子图像在超完备目标字典下的最佳表示系数; 最后, 根据表示系数以及对应的原子向量对图像子块进行重构, 从而得到突出红外小目标的高信噪比重构图像, 实现杂波抑制。不同环境下的多项实验表明, 该算法可在复杂背景下自适应地抑制杂波, 提高图像的信噪比; 通过简单的阈值分割可以分开目标和背景, 为之后的目标检测处理奠定基础。得到的性能评价指标显示：本算法计算量较小, 实时性较强, 鲁棒性较强, 易于硬件实现。

In accordance with the detection of small targets in an infrared image, an adaptive clutter suppression method based on image sparse representation was proposed. First, 500 frames of infrared images were sampled, and an over complete and multi-component dictionary containing characteristics of every image layers was constructed through learning and training. Then, the over complete dictionary corresponding to the image subblock was selected adaptively to represent the image sparsely through the covariance of the infrared image, and the optimum representative coefficients of the subimage under the over-complete target dictionary were obtained through matching the tracking algorithm. Finally, the image subblock was reconstructed according to the representative coefficients and the corresponding atomic vector and the high SNR reconstructed image which protruded the infrared small targets were acquired, and the clutter was suppressed. Many experiments under different circumstances indicate that the algorithm proposed in this paper can suppress the clutter under complex backgrounds and can raise the SNR. The target and background can be separated through simple threshold division, which lays foundation for the target detection process that follows up. Obtained results show that the method has smaller computation costs, stronger robustness and is easy to be realized by hardware.