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.
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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):217–231
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 48:145–156
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)
Nayak, N. R., & Ray, A. (2013) Intelligent seam tracking for robotic welding. Springer-Verlag press, 8-30
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):1355–1364
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):229–243
Guido H, Kuhlenkoetter B (2014) A stateful robotic weldment geometry measuring system. Int J Mater Prod Technol 48(1–4):167–178
Lertrusdachakul I, Mathieu A, Aubreton O (2015) Vision-based control of wire extension in GMA welding. Int J Adv Manuf Technol 78(5–8):1201–1210
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. 94451V
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 37:251–261
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):1247–1254
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):1096–1108
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):4031–4038
Aviles-Viñas JF, Lopez-Juarez I, Rios-Cabrera R (2015) Acquisition of welding skills in industrial robots. Ind Robot 42(2):156–166
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):989–999
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):751–761
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):13–29
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):1841–1849
Wang Z (2014) Monitoring of GMAW weld pool from the reflected laser lines for real-time control. IEEE Trans Ind Inf 10(4):2073–2083
Wan g, Z. (2015). An imaging and measurement system for robust reconstruction of weld pool during arc welding. IEEE Trans Ind Electron, 62(8), 5109-5118
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):1741–1749
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):21–32
Tsai HC, Lin HJ, Leou JJ (2015) Multiexposure image fusion using intensity enhancement and detail extraction. J Vis Commun Image Represent 33:165–178
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): 161–171
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):2519–2532
Shen R, Cheng I, Basu A (2013) QoE-based multi-exposure fusion in hierarchical multivariate Gaussian CRF. IEEE Trans Image Process 22(6):2469–2478
Shen J, Zhao Y, Yan S, Li X (2014) Exposure fusion using boosting Laplacian pyramid. IEEE Trans Cybern 44(9):1579–1590
Itti L (2004) Automatic foveation for video compression using a neurobiological model of visual attention. IEEE Trans Image Process 13(10):1304–1318
Hou X, Harel J, Koch C (2012) Image signature: highlighting sparse salient regions. IEEE Trans Pattern Anal Mach Intell 34(1):194–201
He K, Sun J, Tang X (2013) Guided image filtering. IEEE Trans Pattern Anal Mach Intell 35(6):1397–1409
Li S, Kang X, Hu J (2013) Image fusion with guided filtering. IEEE Trans Image Process 22(7):2864–2875
Li Z, Wei Z, Wen C, Zheng J (2017) Detail-enhanced multi-scale exposure fusion. IEEE Trans Image Process 26(3):1243–1252
Burt P, Adelson E (1983) The Laplacian pyramid as a compact image code[J]. IEEE Trans Commun 31(4):532–540
Fattal R, Agrawala M, Rusinkiewicz S (2007) Multiscale shape and detail enhancement from multi-light image collections. ACM Trans. Graph. 26(3), Article 51
Hou X, Zhang L (2007) Saliency detection: a spectral residual approach. Computer vision and pattern recognition, 1–8
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, 31:369–378
Robertson MA, Borman S, Stevenson RL (2003) Estimation-theoretic approach to dynamic range enhancement using multiple exposures[J]. J Electron Imag 12(2):219–228
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):94–109
Liu Y, Liu S, Wang Z (2015) A general framework for image fusion based on multi-scale transform and sparse representation. Information Fusion 24:147–164
Li Y, Liu G (2009) Digital images clarity quality evaluation using non-subsampled contourlet transform. International Symposium on Computational Intelligence and Design. IEEE, 318–321
We believe that three aspects of the work will make it interesting to general readers at least.
1) A novel multi-scale hybrid weight fusion framework is proposed to overcome the inherent defects of detail loss during reconstruction process, which is based on Laplacian pyramid and the hybrid weight including the local weight, the global weight considering the gradient vectors between different exposure images, and saliency weight based on spectral residual model.
2) The advantages of the proposed fusion algorithm over individual weight are first exhibited in detail from a theoretical point of view, and then experimental results demonstrate that the proposed fusion framework can obtain state-of-the-art performance in multi-exposure image for welding region.
3) The proposed method can effectively alleviate the strong interference of welding process to obtain clear and high-quality images of welding region for many existing cameras with low dynamic range in the industrial application, which help to ensure quality and productivity in the weld process. The method results are given in our manuscript.
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Chen, H., Ren, Y., Cao, J. et al. Multi-exposure fusion for welding region based on multi-scale transform and hybrid weight. Int J Adv Manuf Technol 101, 105–117 (2019). https://doi.org/10.1007/s00170-018-2723-1
- Multi-exposure fusion
- High dynamic range
- Welding image
- Visual saliency
- Guided filter
- Hybrid weight