Abstract
Image fusion is the process of combining multiple images of a same scene to single high-quality image which has more information than any of the input images. In this paper, we propose a new fusion approach in a spatial domain using propagated image filter. The proposed approach calculates the weight map of every input image using the propagated image filter and gradient domain postprocessing. Propagated image filter exploits cumulative weight construction approach for filtering operation. We show that the proposed approach is able to achieve state-of-the-art results for the problem of multi-exposure fusion for various types of indoor and outdoor natural static scenes with varying amounts of dynamic range.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
M. R. Metwalli, A. H. Nasr, O. S. F. Allah, and S. El-Rabaie, “Image fusion based on principal component analysis and high-pass filter,” in Computer Engineering & Systems, 2009. ICCES 2009. International Conference on. IEEE, 2009, pp. 63–70.
G. Bhatnagar, Q. Wu, and Z. Liu, “Directive contrast based multimodal medical image fusion in nsct domain,” Multimedia, IEEE Transactions on, vol. 15, no. 5, pp. 1014–1024, 2013.
S. Li, X. Kang, J. Hu, and B. Yang, “Image matting for fusion of multi-focus images in dynamic scenes,” Information Fusion, vol. 14, no. 2, pp. 147–162, 2013.
J. Tian, L. Chen, L. Ma, and W. Yu, “Multi-focus image fusion using a bilateral gradient-based sharpness criterion,” Optics communications, vol. 284, no. 1, pp. 80–87, 2011.
X. Luo, J. Zhang, and Q. Dai, “A regional image fusion based on similarity characteristics,” Signal processing, vol. 92, no. 5, pp. 1268–1280, 2012.
K. Ma and Z. Wang, “Multi-exposure image fusion: A patch-wise approach,” in ICIP. IEEE, 2015.
S. Li and X. Kang, “Fast multi-exposure image fusion with median filter and recursive filter,” Consumer Electronics, IEEE Transactions on, vol. 58, no. 2, pp. 626–632, 2012.
W. Zhang and W.-K. Cham, “Gradient-directed multiexposure composition,” Image Processing, IEEE Transactions on, vol. 21, no. 4, pp. 2318–2323, 2012.
Z. G. Li, J. H. Zheng, and S. Rahardja, “Detail-enhanced exposure fusion,” Image Processing, IEEE Transactions on, vol. 21, no. 11, pp. 4672–4676, 2012.
T. Mertens, J. Kautz, and F. Van Reeth, “Exposure fusion: A simple and practical alternative to high dynamic range photography,” in Computer Graphics Forum, vol. 28, no. 1. Wiley Online Library, 2009, pp. 161–171.
J. Tian and L. Chen, “Adaptive multi-focus image fusion using a wavelet-based statistical sharpness measure,” Signal Processing, vol. 92, no. 9, pp. 2137–2146, 2012.
Q. Zhang and B.-l. Guo, “Multifocus image fusion using the nonsubsampled contourlet transform,” Signal Processing, vol. 89, no. 7, pp. 1334–1346, 2009.
P.-w. Wang and B. Liu, “A novel image fusion metric based on multi-scale analysis,” in Signal Processing, 2008. ICSP 2008. 9th International Conference on. IEEE, 2008, pp. 965–968.
R. Szeliski, “Image alignment and stitching: A tutorial,” Foundations and Trends in Computer Graphics and Vision, vol. 2, no. 1, pp. 1–104, 2006.
S. Mann and R. Picard, “Being undigital with digital cameras.” MIT Media Lab Perceptual, 1994.
P. E. Debevec and J. Malik, “Recovering high dynamic range radiance maps from photographs,” in ACM SIGGRAPH. ACM, 1997.
T. Mitsunaga and S. K. Nayar, “Radiometric self calibration,” in CVPR, vol. 1. IEEE, 1999.
R. Szeliski, Computer vision: algorithms and applications. Springer Science & Business Media, 2010.
E. Reinhard, W. Heidrich, P. Debevec, S. Pattanaik, G. Ward, and K. Myszkowski, High dynamic range imaging: acquisition, display, and image-based lighting. Morgan Kaufmann, 2010.
S. Raman and S. Chaudhuri, “Bilateral filter based compositing for variable exposure photography,” in Proceedings of Eurographics, 2009.
F. Durand and J. Dorsey, “Fast bilateral filtering for the display of high-dynamic-range images,” ACM transactions on graphics (TOG), vol. 21, no. 3, pp. 257–266, 2002.
S. Li, X. Kang, and J. Hu, “Image fusion with guided filtering,” Image Processing, IEEE Transactions on, vol. 22, no. 7, pp. 2864–2875, 2013.
S. Paris, P. Kornprobst, J. Tumblin, and F. Durand, “Bilateral filtering: Theory and applications in computer graphics and vision,” 2008.
K. He, J. Sun, and X. Tang, “Guided image filtering,” Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 35, no. 6, pp. 1397–1409, 2013.
J.-H. Rick Chang and Y.-C. Frank Wang, “Propagated image filtering,” in IEEE CVPR, 2015, pp. 10–18.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Science+Business Media Singapore
About this paper
Cite this paper
Patel, D., Sonane, B., Raman, S. (2017). Multi-exposure Image Fusion Using Propagated Image Filtering. In: Raman, B., Kumar, S., Roy, P., Sen, D. (eds) Proceedings of International Conference on Computer Vision and Image Processing. Advances in Intelligent Systems and Computing, vol 459. Springer, Singapore. https://doi.org/10.1007/978-981-10-2104-6_39
Download citation
DOI: https://doi.org/10.1007/978-981-10-2104-6_39
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-2103-9
Online ISBN: 978-981-10-2104-6
eBook Packages: EngineeringEngineering (R0)