【摘要】 为了提高图像融合算法的速度,改善融合图像的质量,提高观察者对场景的认知能力,提出了一种介于像素级融合与特征级融合之间的基于红外特征和区域相似的图像融合方法.该方法利用目标的红外特征对红外图像进行目标区域分割,从而实现对目标区域和背景区域的分别融合;同时利用图像的结构相似度对源图像中的目标区域进行相似判定,并利用不同的融合规则对冗余区域和互补/冲突区域分别进行融合.与目前常用的基于多分辨率分解的图像融合方法相比,它既充分利用了目标的红外特征和可见光图像的细节信息,更好地对图像中亮暗相反的区域进行了处理,改善了融合效果,又提高了运算速度.更多还原

【Abstract】 An infrared feature and image similarity-based image fusion scheme,which combines aspects of feature-level fusion with pixel-level fusion,is proposed to improve the computing speed and the quality of the fused image,thus making it easier for the observer to interpret the scene.In the proposed scheme,the infrared image is segmented based on the infrared features of the targets,and the target regions and the background are fused respectively;in the fusion process of the target regions,the image structural similarity is used to measure the similarity between the corresponding target regions in the source images,on the basis of which different fusion rules are applied to the redundant and complementary/conflicting regions.Compared with some commonly used multiresolution image fusion methods,the scheme presented is much faster,with an improved fusion effect,which is due to the full use of the infrared features of the infrared image and the detailed information of the visible image.更多还原