基于小波变换的自适应多模红外小目标检测

小波变换是一种有效的红外小目标检测方法。然而,在不同的子带、不同方向上,信号和噪声所呈现的特性不同,采用单一的阈值往往无法得到一个令人满意的检测结果。针对这一情况,提出了一种基于小波变换的自适应多模红外小目标检测算法。该算法可以根据不同尺度和方向上噪声的分布自动调整阈值,使得检测结果更加有效。其中分别采用了自适应Bayes Shrink阈值和广义交叉验证阈值处理每个子带的小波系数,接着再利用处理后的系数重构小波图像,最后通过一个简单的全局阈值分割得到红外小目标。实验结果表明,与对照方法相比,所提出的算法具有更好的检测性能和鲁棒性。

Adaptive multi-mode infrared small target detection based on wavelet transform

Wavelet transform is an effective method for infrared small target detection. The characteristics of signal and noise are different in different sub-bands and different directions,so the single threshold is often unable to get a satisfactory result. To solve this problem,an adaptive multi-mode infrared small target detection algorithm was presented based on wavelet transform. The algorithm can automatically adjust the threshold according to the distribution of noise in different scales and directions,which makes the detection results more effective. Adaptive Bayes shrink threshold and generalized cross validation threshold are used to deal with the wavelet coefficients of each sub-band,and then the wavelet images are reconstructed by the obtained coefficients. Finally,the infrared small target is gotten by one simple global threshold segmentation. The experimental results demonstrate that the proposed algorithm has better detection performance and robustness than the other method.