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


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.


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


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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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  1. 1.
    Aviles-Viñas JF, Rios-Cabrera R, Lopez-Juarez I (2016) On-line learning of welding bead geometry in industrial robots. Int J Adv Manuf Technol 83(1–4):217–231CrossRefGoogle Scholar
  2. 2.
    Zhang G, Shi Y, Gu Y, Fan D (2017) Welding torch attitude-based study of human welder interactive behavior with weld pool in GTAW. Robot Comput Integr Manuf 48:145–156CrossRefGoogle Scholar
  3. 3.
    Mann S, Lo R C H, Ovtcharov K, Gu S, Dai D, Ngan C, Ai T (2012) Realtime HDR (high dynamic range) video for eyetap wearable computers, FPGA-based seeing aids, and glasseyes (eyetaps). In: Electrical & computer engineering (CCECE), 2012 25th IEEE Canadian Conference on (pp. 1–6)Google Scholar
  4. 4.
    Nayak, N. R., & Ray, A. (2013) Intelligent seam tracking for robotic welding. Springer-Verlag press, 8-30Google Scholar
  5. 5.
    Li W, Gao K, Wu J, Hu T, Wang J (2014) SVM-based information fusion for weld deviation extraction and weld groove state identification in rotating arc narrow gap MAG welding. Int J Adv Manuf Technol 74(9–12):1355–1364CrossRefGoogle Scholar
  6. 6.
    Dinham M, Fang G (2014) Detection of fillet weld joints using an adaptive line growing algorithm for robotic arc welding. Robot Comput Integr Manuf 30(3):229–243CrossRefGoogle Scholar
  7. 7.
    Guido H, Kuhlenkoetter B (2014) A stateful robotic weldment geometry measuring system. Int J Mater Prod Technol 48(1–4):167–178Google Scholar
  8. 8.
    Lertrusdachakul I, Mathieu A, Aubreton O (2015) Vision-based control of wire extension in GMA welding. Int J Adv Manuf Technol 78(5–8):1201–1210CrossRefGoogle Scholar
  9. 9.
    Lahdenoja O, Säntti T, Laiho M, Paasio A, Poikonen J K (2015) Seam tracking with adaptive image capture for fine-tuning of a high power laser welding process. In Seventh international conference on machine vision (ICMV 2014) (Vol. 9445), p. 94451VGoogle Scholar
  10. 10.
    He Y, Xu Y, Chen Y, Chen H, Chen S (2016) Weld seam profile detection and feature point extraction for multi-pass route planning based on visual attention model. Robot Comput Integr Manuf 37:251–261CrossRefGoogle Scholar
  11. 11.
    Chen Z, Gao X (2014) Detection of weld pool width using infrared imaging during high-power fiber laser welding of type 304 austenitic stainless steel. Int J Adv Manuf Technol 74(9–12):1247–1254CrossRefGoogle Scholar
  12. 12.
    Liu J, Fan Z, Olsen SI, Christensen KH, Kristensen JK (2017) Boosting active contours for weld pool visual tracking in automatic arc welding. IEEE Trans Autom Sci Eng 14(2):1096–1108CrossRefGoogle Scholar
  13. 13.
    Yu P, Xu G, Gu X, Zhou G, Tian Y (2017) A low-cost infrared sensing system for monitoring the MIG welding process. Int J Adv Manuf Technol 92(9–12):4031–4038CrossRefGoogle Scholar
  14. 14.
    Aviles-Viñas JF, Lopez-Juarez I, Rios-Cabrera R (2015) Acquisition of welding skills in industrial robots. Ind Robot 42(2):156–166CrossRefGoogle Scholar
  15. 15.
    Fan J, Jing F, Fang Z, Tan M (2017) Automatic recognition system of welding seam type based on SVM method. Int J Adv Manuf Technol 92(1–4):989–999CrossRefGoogle Scholar
  16. 16.
    Huang Y, Li G, Shao W, Gong S, Zhang X (2017) A novel dual-channel weld seam tracking system for aircraft T-joint welds. Int J Adv Manuf Technol 91(1–4):751–761CrossRefGoogle Scholar
  17. 17.
    Muhammad J, Altun H, Abo-Serie E (2018) A robust butt welding seam finding technique for intelligent robotic welding system using active laser vision. Int J Adv Manuf Technol 94(1–4):13–29CrossRefGoogle Scholar
  18. 18.
    Li Y, Li YF, Wang QL, Xu D, Tan M (2010) Measurement and defect detection of the weld bead based on online vision inspection. IEEE Trans Instrum Meas 59(7):1841–1849CrossRefGoogle Scholar
  19. 19.
    Wang Z (2014) Monitoring of GMAW weld pool from the reflected laser lines for real-time control. IEEE Trans Ind Inf 10(4):2073–2083CrossRefGoogle Scholar
  20. 20.
    Wan g, Z. (2015). An imaging and measurement system for robust reconstruction of weld pool during arc welding. IEEE Trans Ind Electron, 62(8), 5109-5118Google Scholar
  21. 21.
    Chen H, Liu W, Huang L, Xing G, Wang M, Sun H (2015) The decoupling visual feature extraction of dynamic three-dimensional V-type seam for gantry welding robot. Int J Adv Manuf Technol 80(9–12):1741–1749CrossRefGoogle Scholar
  22. 22.
    Gao X, Mo L, Xiao Z, Chen X, Katayama S (2016) Seam tracking based on Kalman filtering of micro-gap weld using magneto-optical image. Int J Adv Manuf Technol 83(1–4):21–32CrossRefGoogle Scholar
  23. 23.
    Tsai HC, Lin HJ, Leou JJ (2015) Multiexposure image fusion using intensity enhancement and detail extraction. J Vis Commun Image Represent 33:165–178CrossRefGoogle Scholar
  24. 24.
    Mertens T, Kautz J, Van Reeth F (2009) Exposure fusion: a simple and practical alternative to high dynamic range photography, computer graphics forum. Blackwell Publishing Ltd, 28(1): 161–171Google Scholar
  25. 25.
    Ma K, Li H, Yong H, Wang Z, Meng D, Zhang L (2017) Robust multi-exposure image fusion: a structural patch decomposition approach. IEEE Trans Image Process 26(5):2519–2532MathSciNetCrossRefGoogle Scholar
  26. 26.
    Shen R, Cheng I, Basu A (2013) QoE-based multi-exposure fusion in hierarchical multivariate Gaussian CRF. IEEE Trans Image Process 22(6):2469–2478MathSciNetCrossRefzbMATHGoogle Scholar
  27. 27.
    Shen J, Zhao Y, Yan S, Li X (2014) Exposure fusion using boosting Laplacian pyramid. IEEE Trans Cybern 44(9):1579–1590CrossRefGoogle Scholar
  28. 28.
    Itti L (2004) Automatic foveation for video compression using a neurobiological model of visual attention. IEEE Trans Image Process 13(10):1304–1318CrossRefGoogle Scholar
  29. 29.
    Hou X, Harel J, Koch C (2012) Image signature: highlighting sparse salient regions. IEEE Trans Pattern Anal Mach Intell 34(1):194–201CrossRefGoogle Scholar
  30. 30.
    He K, Sun J, Tang X (2013) Guided image filtering. IEEE Trans Pattern Anal Mach Intell 35(6):1397–1409CrossRefGoogle Scholar
  31. 31.
    Li S, Kang X, Hu J (2013) Image fusion with guided filtering. IEEE Trans Image Process 22(7):2864–2875CrossRefGoogle Scholar
  32. 32.
    Li Z, Wei Z, Wen C, Zheng J (2017) Detail-enhanced multi-scale exposure fusion. IEEE Trans Image Process 26(3):1243–1252MathSciNetCrossRefGoogle Scholar
  33. 33.
    Burt P, Adelson E (1983) The Laplacian pyramid as a compact image code[J]. IEEE Trans Commun 31(4):532–540CrossRefGoogle Scholar
  34. 34.
    Fattal R, Agrawala M, Rusinkiewicz S (2007) Multiscale shape and detail enhancement from multi-light image collections. ACM Trans. Graph. 26(3), Article 51Google Scholar
  35. 35.
    Hou X, Zhang L (2007) Saliency detection: a spectral residual approach. Computer vision and pattern recognition, 1–8Google Scholar
  36. 36.
    Debevec, P. E., & Malik, J. (1997) Recovering high dynamic range radiance maps from photographs. Proceedings of the 24th annual conference on Computer graphics and interactive techniques, 31:369–378Google Scholar
  37. 37.
    Robertson MA, Borman S, Stevenson RL (2003) Estimation-theoretic approach to dynamic range enhancement using multiple exposures[J]. J Electron Imag 12(2):219–228CrossRefGoogle Scholar
  38. 38.
    Liu Z, Blasch E, Xue Z, Zhao J, Laganiere R, Wu W (2012) Objective assessment of multiresolution image fusion algorithms for context enhancement in night vision: a comparative study. IEEE Trans Pattern Anal Mach Intell 34(1):94–109CrossRefGoogle Scholar
  39. 39.
    Liu Y, Liu S, Wang Z (2015) A general framework for image fusion based on multi-scale transform and sparse representation. Information Fusion 24:147–164CrossRefGoogle Scholar
  40. 40.
    Li Y, Liu G (2009) Digital images clarity quality evaluation using non-subsampled contourlet transform. International Symposium on Computational Intelligence and Design. IEEE, 318–321Google Scholar

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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