复杂海面背景下船舶红外偏振图像融合方法

An Infrared Polarization Image Fusion Method for Ships in Complex Sea Surface Background

针对海上船舶目标不清晰导致检测准确率低的问题,提出一种基于深度学习框架的船舶红外与红外偏振图像的融合方法来增强海面船舶弱目标,提高检测准确率。将源图像分为船舶轮廓部分和特征部分,轮廓部分通过加权平均策略进行融合,采用非局部均值对船舶偏振图像进行去噪;特征部分采用VGG网络提取,进而重建融合图像。与传统图像融合方法相比,所提方法能够保留更多的船舶红外与偏振特征,使融合后的图像信息得到增强,并在对比度和信噪比上均有较好提高,为复杂海面背景下的船舶目标检测提供新的方法。

Aiming at the problem of low detection accuracy due to unclear targets of ships at sea,this paper proposes a fusion method of ship infrared and infrared polarization images based on a deep learning framework to enhance the weak targets of ships at sea and improve the detection accuracy.Firstly,the source image is divided into ship contour part and feature part,and the contour part is fused by weighted average strategy,and the ship polarization image is denoised by using non-local mean.The feature part is extracted using VGG network,and then the fused image is reconstructed.Compared with the traditional image fusion methods,the proposed method can retain more infrared and polarization features of the ship,so that the fused image information is enhanced and has better contrast and signal-to-noise ratio,which provides a new method for ship target detection in complex sea surface background.