基于循环平移Contourlet变换的红外小目标检测方法

分析了在有噪声和背景干扰情况下检测红外小目标的方法，提出了一种将循环平移Contourlet变换去噪 方法和自适应阈值分割方法相结合的红外小目标检测算法。该方法首先对原始图像进行循环平移阈值去噪，再用原始图像减去 去噪图像，对得到的残差图像进行自适应阈值分割，分离出少量的候选目标点，最后利用目标运动的连续性和一致性检测出 目标。分别用Contourlet变换法、小波变换法和本文提出的检测法对小目标进行了检测。仿真结果表明，本文提出的检测方法 能较精确地检测出序列图像中的红外小目标，检测效果优于Contourlet变换法和小波变换法。

Small Infrared Object Detection Method Based on Cycle Spinning Contourlet Transform

The methods for detecting small infrared targets in the case of noise and background interference are analyzed. A small infrared target detection algorithm which combines the cycle spinning contourlet transform with the adaptive threshold segmentation is proposed. Firstly, the method uses the cycle spinning to de-noise the original images. Secondly, it obtains the residual images by subtracting the de-noised image from the original image. Thirdly, it uses the adaptive threshold segmentation to segment the residual images so as to separate a few of candidate target points. Finally, it uses the continuity and consistency of target motion to detect the target. The simulation result shows that the proposed detection method can detect the small infrared targets in the sequential images precisely and it is more effective than the Contourlet transform and wavelet transform methods in detection.