Yan Lei, Hao Qun, Cao Jie, Saad Rizvi, Li Kun, Yan Zhengang, Wu Zhimin
Key Laboratory of Biomimetic Robots and Systems, School of Optics and Photonics, Beijing Institute of Technology, Ministry of Education, Beijing, 100081, China.
State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instruments, Tsinghua University, Beijing, 100084, China.
Sci Rep. 2021 Jan 13;11(1):1235. doi: 10.1038/s41598-020-80189-1.
Image fusion integrates information from multiple images (of the same scene) to generate a (more informative) composite image suitable for human and computer vision perception. The method based on multiscale decomposition is one of the commonly fusion methods. In this study, a new fusion framework based on the octave Gaussian pyramid principle is proposed. In comparison with conventional multiscale decomposition, the proposed octave Gaussian pyramid framework retrieves more information by decomposing an image into two scale spaces (octave and interval spaces). Different from traditional multiscale decomposition with one set of detail and base layers, the proposed method decomposes an image into multiple sets of detail and base layers, and it efficiently retains high- and low-frequency information from the original image. The qualitative and quantitative comparison with five existing methods (on publicly available image databases) demonstrate that the proposed method has better visual effects and scores the highest in objective evaluation.
图像融合将(同一场景的)多幅图像中的信息进行整合,以生成一幅(信息量更大的)适合人类和计算机视觉感知的合成图像。基于多尺度分解的方法是常用的融合方法之一。在本研究中,提出了一种基于八度高斯金字塔原理的新型融合框架。与传统的多尺度分解相比,所提出的八度高斯金字塔框架通过将图像分解为两个尺度空间(八度空间和间隔空间)来获取更多信息。与具有一组细节层和基础层的传统多尺度分解不同,所提出的方法将图像分解为多组细节层和基础层,并有效地保留了原始图像的高频和低频信息。与五种现有方法(在公开可用的图像数据库上)进行的定性和定量比较表明,所提出的方法具有更好的视觉效果,并且在客观评估中得分最高。