Parallax correction via disparity estimation in a multi-aperture camera

Janne Mustaniemi*, Juho Kannala, Janne Heikkilä

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)

Abstract

In this paper, an image fusion algorithm is proposed for a multi-aperture camera. Such camera is a feasible alternative to traditional Bayer filter camera in terms of image quality, camera size and camera features. The camera consists of several camera units, each having dedicated optics and color filter. The main challenge of a multi-aperture camera arises from the fact that each camera unit has a slightly different viewpoint. Our image fusion algorithm corrects the parallax error between the sub-images using a disparity map, which is estimated from the single-spectral images. We improve the disparity estimation by combining matching costs over multiple views using trifocal tensors. Images are matched using two alternative matching costs, mutual information and Census transform. We also compare two different disparity estimation methods, graph cuts and semi-global matching. The results show that the overall quality of the fused images is near the reference images.

Original languageEnglish
Pages (from-to)1313-1323
Number of pages11
JournalMACHINE VISION AND APPLICATIONS
Volume27
Issue number8
Early online date25 May 2016
DOIs
Publication statusPublished - Nov 2016
MoE publication typeA1 Journal article-refereed

Keywords

  • Census transform
  • Graph cuts
  • Mutual information
  • Semi-global matching
  • Trifocal tensor

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