IEEE Trans Image Process. 2013 Dec;22(12):4996-5009. doi: 10.1109/TIP.2013.2281420.
The robust detection of small targets is one of the key techniques in infrared search and tracking applications. A novel small target detection method in a single infrared image is proposed in this paper. Initially, the traditional infrared image model is generalized to a new infrared patch-image model using local patch construction. Then, because of the non-local self-correlation property of the infrared background image, based on the new model small target detection is formulated as an optimization problem of recovering low-rank and sparse matrices, which is effectively solved using stable principle component pursuit. Finally, a simple adaptive segmentation method is used to segment the target image and the segmentation result can be refined by post-processing. Extensive synthetic and real data experiments show that under different clutter backgrounds the proposed method not only works more stably for different target sizes and signal-to-clutter ratio values, but also has better detection performance compared with conventional baseline methods.
稳健的小目标检测是红外搜索和跟踪应用中的关键技术之一。本文提出了一种新的单幅红外图像小目标检测方法。首先，利用局部构建方法将传统的红外图像模型推广到新的红外补丁图像模型。然后，由于红外背景图像的非局部自相关特性，基于新模型，小目标检测被表述为一个恢复低秩和稀疏矩阵的优化问题，可以使用稳定的主成分追踪有效地解决。最后，采用简单的自适应分割方法对目标图像进行分割，并通过后处理对分割结果进行细化。大量的合成和真实数据实验表明，在不同的杂波背景下，该方法不仅对不同的目标大小和信噪比具有更稳定的工作性能，而且与传统的基线方法相比具有更好的检测性能。
