利用相邻相关像素进行噪音抑制的模糊小目标检测算法

提出了一种利用相邻相关像素对红外数字图像中的可疑小目标进行检测的算法. 该算法首先利用自适应全局阈值检测图像中的亮像素,并借助相邻相关像素信息滤除结果中的亮噪音点;然后依据亮像素的相关性,对剩余的亮像素进行加强,并再次抑制噪音,获得可观的信噪比增益.相对于传统的Top-Hat变换,该算法能够在有效提高待检测目标信号强度的同时很好地抑制噪音,有效地保留了目标图像的边缘细节.

Dim Small Targets Detection with Noise Suppression Utilizing Adjacent Relevant Pixels Information

An infrared dim target detecting algorithm called the adjacent relevant pixels detection is proposed. Firstly, an adaptive local threshold is used to detect bright pixels, and then eliminate the noise with the information of adjacent relevant pixels. The remaining suspicious pixels are enhanced by the relevant bright pixels, by which the noise is suppressed again and considerable SNR gains are obtained. ARPD method is compared with classic Top-Hat transformations with and without 8-neigboorhood clustering. The target point in images processed by ARPD method receives high signal-to-clutter ratio gain, and the detectability of the target is enhanced.