中文摘要：

为了增强红外或者可见光图像数据中的弱小目标检测，提出了一种采用模糊C均值（FCM）聚类与迭代最小二乘（RLS）自适应滤波相结合的背景抑制方法。假设待检测目标在图像帧上具有极小的空域扩展度，且受到强背景杂波的干扰。对输入的图像首先采用FCM聚类划分为灰度准平稳的子域，再将整帧图像均匀划分为相同的子块，然后在每个子块中针对每类子域利用RLS滤波估计背景杂波并另以去除，结果只剩下目标信号与残留噪声。大量仿真试验表明与其它传统方法相比具有更好的检测性能。

英文摘要：

A method of background suppression using fuzzy c - means （ FCM ） clustering and recursive least square （RLS） filter is proposed to enhance the detection of dim small targets in IR or visual - light image data. The target to be detected is assumed to have a small spatial spread in a frame, and is obscured by heavy background clut- ter. The input data is firstly partitioned using FCM clustering, and each cluster is thought as a gray - level quasi - stationary subset. Secondly the image is partitioned to some sub - images uniformly, and then a RLS filter is applied to estimate background for each subset in each sub - image. Thus the background can be subtracted from input data, leaving components of the target signal in the residual noise. Many experiment results show better performance of de- tection by the method than by other traditional methods.