基于鲁棒主成分分析的红外图像小目标检测

鲁棒的小目标检测是红外目标搜索与跟踪的关键技术，提出一种改进的单帧红外图像小目标检测算法。该方法将原始红外图像通过预处理变换到新的红外块图像模式，在红外块图像上，将红外图像小目标检测问题转换为低秩矩阵和稀疏矩阵分离的鲁棒主成分分析(RPCA)问题。考虑到红外图像中噪声和杂波的存在，用交替方向方法求解带噪声的RPCA问题，获得稀疏目标图像，并对获得的稀疏目标图像采用简单的图像分割算法进行目标检测。对空天、海天、天云、海面4种不 同场景的红外图像小目标检测，进行仿真实验，结果验证了所提出算法的有效性。

Small Target Detection in a Single Infrared Image Based on RPCA

The robust infrared small target detection is one of the key techniques of infrared search and track systems. An improved algorithm is presented for small target detection in single-frame infrared image. The infrared image model is generalized to a new infrared patch-image model, and based on the new model, the small target detection is formulated as an optimal robust principal component analysis (RPCA) problem of separating low-rank and sparse matrices. Considering the presence of noises and clutter in infrared image, an alternating direction algorithm is used for solving the RPCA problem to obtain sparse target image, and a simple image segmentation method is used to segment the target image for target detection. The small infrared targets in different scenes, such as sky, sea-sky, cloud and sea surface, are detected and simulated. Experimental results demonstrate the effectiveness of the proposed method.