A simple and effective multi-focus image fusion method based on local standard deviations enhanced by the guided filter

Cheng Shu You, Suh Yuh Yang

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

This paper develops a simple and effective multi-focus image fusion method based on local standard deviations of the corresponding Laplacian images of source images and further enhanced by the guided filter. The underlying idea of this pixel-based approach is that the sharper pixels generally should have a comparatively higher local variance and hence higher local standard deviation. We first apply the Laplacian operation on each partially focused source image of the same scene, estimate the local standard deviation for each pixel, and enhance the local standard deviations using the guided filter. We then employ the filtered local standard deviation of the Laplacian image as an initial focus measure and combine it with the small region removal strategy to construct a decision map for pixel selection. Finally, according to the decision map, the target all-in-focus fused image is formed pixel-by-pixel. A variant of the proposed method with further guided filtering on the decision map is also developed. Numerical results demonstrate the proposed methods’ high performance compared with some state-of-the-art techniques.

Original languageEnglish
Article number102146
JournalDisplays
Volume72
DOIs
StatePublished - Apr 2022

Keywords

  • Guided filter
  • Local standard deviation
  • Local variance
  • Multi-focus image fusion

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