关键词: 图像处理, 红外弱小目标, Facet小面模型, 双向扩散滤波, 信号检测

Abstract:

Aiming at the difficulty of detecting infrared week-dim targets against the nonstationary strong cluttered background in an infrared image, a novel adaptive filter based on surface fitting bidirectional diffusion regularization technology is proposed to detect a week-dim target in such a strong cluttered background. Firstly, the facet model is applied to fit the underlying intensity surface of the image, and then, an average directional derivative gradient operator(ADDG) is designed to describe the multi-degree multi-orientation gradient character of the image. Combining with the ADDG operator, a novel regularizing bidirectional diffusion filter for background suppression is developed. Compared with traditional background suppression algorithms, our method can switch adaptively between forward(background suppression) and backward(targets enhancement) diffusion processes according to the character of the target and background to enhance the signal of interest targets and remove clutter simultaneously. Experimental results show that this method can provide good filtering performance with the advantages of its logical structure simple and easy to implemente in a real-time system.

Key words: image processing, infrared week-dim target, Facet cubic model, bidirectional diffusion, signal detection