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基于非下采样剪切波变换域中不同约束的红外与可见光图像融合

Infrared and Visible Image Fusion Based on Different Constraints in the Non-Subsampled Shearlet Transform Domain.

作者信息

Huang Yan, Bi Duyan, Wu Dongpeng

机构信息

Aeronautics and Astronautics Engineering College, Air Force Engineering University, Xi'an 710038, Shaanxi, China.

School of Management Engineering, Xi'an University of Finance and Economics, Xi'an 710100, Shaanxi, China.

出版信息

Sensors (Basel). 2018 Apr 11;18(4):1169. doi: 10.3390/s18041169.

Abstract

There are many artificial parameters when fuse infrared and visible images, to overcome the lack of detail in the fusion image because of the artifacts, a novel fusion algorithm for infrared and visible images that is based on different constraints in non-subsampled shearlet transform (NSST) domain is proposed. There are high bands and low bands of images that are decomposed by the NSST. After analyzing the characters of the bands, fusing the high level bands by the gradient constraint, the fused image can obtain more details; fusing the low bands by the constraint of saliency in the images, the targets are more salient. Before the inverse NSST, the Nash equilibrium is used to update the coefficient. The fused images and the quantitative results demonstrate that our method is more effective in reserving details and highlighting the targets when compared with other state-of-the-art methods.

摘要

在融合红外图像和可见光图像时存在许多人工参数,为了克服由于伪像导致融合图像细节不足的问题,提出了一种基于非下采样剪切波变换(NSST)域中不同约束的红外和可见光图像融合新算法。通过NSST分解得到图像的高频带和低频带。在分析这些频带的特征后,利用梯度约束融合高频带,融合后的图像可以获得更多细节;利用图像中的显著性约束融合低频带,目标更加显著。在进行NSST逆变换之前,使用纳什均衡来更新系数。融合图像和定量结果表明,与其他现有方法相比,我们的方法在保留细节和突出目标方面更有效。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b60e/5948564/c619dbc8632d/sensors-18-01169-g001.jpg

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