针对复杂背景下红外弱小目标检测效果不佳的问题, 结合多尺度分析法和各向异性扩散方程, 利用图像尺度和方向信息, 提出一种新的红外弱小目标检测算法.首先, 采用Surfacelet变换对图像进行多尺度、多方向分解, 得到不同尺度下高频子带系数和低频子带系数；其次根据不同频带的特点, 分别采用改进的各向异性扩散方程差分滤波和局部去均值滤波对高频子带系数和低频子带系数进行处理；最后, 采用逆Surfacelet变换重构处理后的子带系数, 并采用自适应阈值分割对重构的图像进行分割, 以实现目标检测.采用多组红外图像进行试验, 并与小波滤波以及各向异性扩散滤波进行比较, 实验结果显示, 该算法能有效抑制背景及其边缘, 可以获得比另外两种算法更好的红外弱小目标检测效果.

For the poor performance of dim and small infrared target detection under complex background, a novel target detection algorithm inspired by the anisotropic diffusion equation and the multi-scale analysis was presented. Firstly, the raw target image is decomposed by the surfacelet transform into multi-scale and mutli-direction details characteristics. Then, the improved anisotropic diffusion equation difference filter and the local mean removal filter are adopted to refine the high-frequency and the low-frequency subbands respectively. Reconstruct the refined subbands by the inverse surfacelet transform to obtain the result image with target enhancement and background suppression. Finally, the classical adaptive threshold segmentation method is used to implement target detection. Various infrared images with dim and small targets are used to verify the efficiency of the algorithm. Compared with wavelet filter and anisotropic diffusion equation filter, the experimental results show that the proposed algorithm can effectively suppress the background and its edge, and acquires a better performance of the target detection.