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Multi-exposure fusion for welding region based on multi-scale transform and hybrid weight

  • Haiyong Chen
  • Yafei Ren
  • Junqi Cao
  • Weipeng Liu
  • Kun LiuEmail author
ORIGINAL ARTICLE
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Abstract

The multi-exposure fusion is an effective image enhancement technique for high dynamic range (HDR) scene. In this paper, a novel multi-scale hybrid weight fusion framework is proposed to overcome the inherent defects of detail loss during the reconstruction process. Firstly, a novel hybrid weight method is developed by employing the local weight of a single image, the global weight between different exposure images, and the saliency weight from spectral residual model. Secondly, a new multi-scale hybrid weight image fusion algorithm based on Laplacian pyramid is proposed by applying the hybrid weight at each scale. The advantages of the proposed fusion algorithm over individual weight are analyzed from a theoretical point of view and then experimentally verified with multi-exposure image in welding region. Furthermore, the guided filter is utilized to smooth the reconstruction image, Laplacian pyramid image, and saliency weight maps for all the low dynamic range (LDR) images, which can effectively keep the edge information and reduce artifacts of weld seam region. Finally, by comparing our results comprehensively with other methods subjectively and objectively, the proposed fusion framework is verified that it can obtain better performance.

Keywords

Multi-exposure fusion High dynamic range Welding image Visual saliency Guided filter Hybrid weight 

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Notes

Funding information

This work was supported in part by National Natural Science Foundation (NNSF) of China under Grant 61873315, 61403119, Natual Science Foundation of Hebei Province under Grant F201402166, F2018202078, Special Correspondent Technology Plan of Tianjin under Grant 15JCTPJC55500, Science and Technology Project of Hebei Province under Grant 17211804D, and Talent Support Project in Hebei Province.

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  • Haiyong Chen
    • 1
  • Yafei Ren
    • 1
  • Junqi Cao
    • 1
  • Weipeng Liu
    • 1
  • Kun Liu
    • 1
    Email author
  1. 1.School of Artificial IntelligenceHebei University of TechnologyTianjinPeople’s Republic of China

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