Fusion algorithm of visible and infrared image based on anisotropic diffusion and image enhancement (capitalize only the first word in a title (or heading), the first word in a subtitle (or subheading), and any proper nouns)

PLoS One. 2021 Feb 19;16(2):e0245563. doi: 10.1371/journal.pone.0245563. eCollection 2021.

Abstract

Aiming at the situation that the existing visible and infrared images fusion algorithms only focus on highlighting infrared targets and neglect the performance of image details, and cannot take into account the characteristics of infrared and visible images, this paper proposes an image enhancement fusion algorithm combining Karhunen-Loeve transform and Laplacian pyramid fusion. The detail layer of the source image is obtained by anisotropic diffusion to get more abundant texture information. The infrared images adopt adaptive histogram partition and brightness correction enhancement algorithm to highlight thermal radiation targets. A novel power function enhancement algorithm that simulates illumination is proposed for visible images to improve the contrast of visible images and facilitate human observation. In order to improve the fusion quality of images, the source image and the enhanced images are transformed by Karhunen-Loeve to form new visible and infrared images. Laplacian pyramid fusion is performed on the new visible and infrared images, and superimposed with the detail layer images to obtain the fusion result. Experimental results show that the method in this paper is superior to several representative image fusion algorithms in subjective visual effects on public data sets. In terms of objective evaluation, the fusion result performed well on the 8 evaluation indicators, and its own quality was high.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Humans
  • Image Enhancement / methods*
  • Image Processing, Computer-Assisted / methods*
  • Infrared Rays*

Grants and funding

This work is supported in part by the Science and Technology Department Project of Sichuan Provincial of China, under Grant 2017GZ0303, in part by Academician (Expert) Workstation Fund Project of Sichuan Province of China, under Grant 2016YSGZZ01, in part by Special Fund for Training High Level Innovative Talents of Sichuan University of Science and Engineering, under Grant B12402005, in part by the Academician Workstation Project of Sichuan Province of China, under Grant 2017YSGZZ04, and Sichuan University of Science and Engineering for Talent introduction project, under Grant 2017RCL59.