基于超完备字典的图像稀疏表示是一种新的图像表示理论, 利用超完备字典的冗余性可以有效地捕捉图像的各种结构特征, 从而实现图像的有效表示.针对红外小目标检测问题, 提出了一种基于图像稀疏表示的检测方法, 该方法采用二维高斯模型生成样本图像, 继而构造超完备目标字典, 然后依次提取测试图像的图像子块并计算其在超完备字典中的表示系数, 背景和目标的表示系数有着显著的差异, 最后通过一个量化指标来判别该子图像块是否含有小目标, 实验结果证实了所提方法的有效性.

The sparse representation based on over-complete dictionary is a new image representation theory. The redundancy of over-complete dictionary can enable it effectively to capture the geometrical characteristics of the images. In this paper, a novel detection method based on image sparse representation was introduced. The over-complete target dictionary is first constructed with atoms which are produced by two-dimensional Gaussian model. Then the sub-image blocks of the test image are extracted successively and the corresponding coefficients are calculated with the constructed over-complete target dictionary. There is a significant difference between the coefficients of objective and background. Whether the sub-image block contains small target or not can be determined by the index of sparse concentration. Experimental results demonstrated the effectiveness of the proposed method.