Abstract
Multi-exposure image fusion (MEF) is a convenient way to get high dynamic range (HDR) images. However, when the input sequence with a large difference in exposure time, the existing MEF algorithms cannot well reconstruct relative contrast, resulting in loss of detail in underexposed or overexposed region. In order to get the details from the images as much as possible, an enhanced multi-scale weight assignment strategy is proposed in this paper. First, the input image guided by itself is decomposed into the first level with base layer (BL) and detail layer (DL) using guided filtering. Then, this BL continues to be decomposed into the second level like the first level. With the going of decomposition, the image can be decomposed into several DLs and 1 BL. Afterward, the image information contained in BLs and DLs is extracted by using the exposure weights and the global gradient weights to reconstruct the fused image, respectively. Finally, 39 sets of two-exposure image sequences are selected and compared with nine representative algorithms. Experimental results show that the proposed algorithm has good visual performance in subjective evaluation and state-of-the-art performance in objective evaluation.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Data Availability
The datasets analyzed during the current study are available in the [MEFB] repository, [https://github.com/xingchenzhang/MEFB]. All data included in this study are available upon request by contact with the corresponding author.
References
Karr, B.A., Debattista, K., Chalmers, A.G.: Optical effects on HDR calibration via a multiple exposure noise-based workflow. Vis. Comput. 37, 895–910 (2021)
Frédo, D., Julie, D.: Fast bilateral filtering for the display of high-dynamic-range images. ACM Trans. Graph. 21, 257–266 (2002)
Shen, R., Cheng, I., Shi, J., Basu, A.: Generalized random walks for fusion of multi-exposure images. IEEE Trans. Image Process. 20(12), 3634–3646 (2011)
Han, D., Li, L., Guo, X., Ma, J.: Multi-exposure image fusion via deep perceptual enhancement. Inf. Fus. 79, 248–262 (2021)
Ma, K., Wang, Z.: Multi-exposure image fusion: a patch-wise approach. In: 2015 IEEE International Conference on Image Processing(ICIP), pp. 1717–1721 (2015)
Li, H., Ma, K., Yong, H., Zhang, L.: Fast multi-scale structural patch decomposition for multi-exposure image fusion. IEEE Trans. Image Process. 29, 5805–5816 (2020)
Li, S., Kang, X., Hu, J.: Image fusion with guided filtering. IEEE Trans. Image Process. 22(7), 2864–2875 (2013)
Ma, K., Li, H., Yong, H., Wang, Z., Meng, D., Zhang, L.: Robust multi-exposure image fusion: a structural patch decomposition approach. IEEE Trans. Image Process. 26(5), 2519–2532 (2017)
Ma, K., Duanmu, Z., Yeganeh, H., Wang, Z.: Multi-exposure image fusion by optimizing a structural similarity index. IEEE Trans. Comput. Imaging 4(1), 60–72 (2018)
Kou, F., Li, Z., Wen, C.: Edge-preserving smoothing pyramid based multi-scale exposure fusion. J. Vis. Commun. Image Represent. 53, 235–244 (2018)
Paul, S., Sevcenco, I.S., Agathoklis, P.: Multi-exposure and multi-focus image fusion in gradient domain. J. Circuits Syst. Comput. 25(10), 1650123 (2016)
Malik, M., Gilani, S., Anwaar, H.: Wavelet Based Exposure Fusion. Lecture Notes in Engineering & Computer Science. 1, 2170 (2008)
Yang, Y., Cao, W., Wu, S., Li, Z.: Multi-scale fusion of two large-exposure-ratio images. IEEE Signal Process. Lett. 25(12), 1885–1889 (2018)
Wang, Q., Chen, W., Wu, X., Li, Z.: Detail-enhanced multi-scale exposure fusion in yuv color space. IEEE Trans. Circuits Syst. Video Technol. 26(3), 1243–1252 (2019)
Qiu, X., Li, M., Zhang, L., Yuan, X.: Guided filter-based multi-focus image fusion through focus region detection. Signal Process. Image Commun. 72, 35–46 (2019)
Gan, W., Wu, X., Wu, W., Yang, X., Chao, R., He, X., Liu, K.: Infrared and visible image fusion with the use of multi-scale edge-preserving decomposition and guided image filter. Infrared Phys. Technol. 72, 37–51 (2015)
Bavirisetti, D.P., Xiao, G., Zhao, J., Dhuli, R., Liu, G.: Multi-scale guided image and video fusion: a fast and efficient approach. Circuits Syst. Signal Process. 38(12), 5576–5605 (2019)
Lee, S.-h., Park, J.S, Cho, N.I.: A multi-exposure image fusion based on the adaptive weights reflecting the relative pixel intensity and global gradient. In: 2018 25th IEEE International Conference on Image Processing (ICIP), pp. 1737–1741 (2018)
Zhang, X.: Benchmarking and comparing multi-exposure image fusion algorithms. Inf. Fus. 74, 111–131 (2021)
Liu, Y., Wang, Z.: Dense sift for ghost-free multi-exposure fusion. J. Vis. Commun. Image Represent. 31, 208–224 (2015)
Burt, P.J., Kolczynski, R.J.: Enhanced image capture through fusion. In: Fourth International Conference on Computer Vision, pp. 173–182 (1993)
Mertens, T., Kautz, J., Reeth, F.: Exposure fusion. In: 15th Pacific Conference on Computer Graphics and Applications, pp. 382–390 (2007)
Qi, Y., Yu, M., Jiang, H., Jiang, G.: Multi-exposure image fusion based on tensor decomposition and convolution sparse representation. Opto-Electron. Eng. 46(1) (2019)
Karakaya, D., Oguzhan, U., Mehmet, T.: PAS-MEF: Multi-exposure image fusion based on principal component analysis, adaptive well-exposedness and saliency map. arXiv:2105.11809 (2021)
Liu, S., Zhang, Y.: Detail-preserving underexposed image enhancement via optimal weighted multi-exposure fusion. IEEE Trans. Consum. Electron. 65(3), 303–311 (2019)
Cai, J., Gu, S., Zhang, L.: Learning a deep single image contrast enhancer from multi-exposure images. IEEE Trans. Image Process. 27(4), 2049–2062 (2018)
Wang, C., He, C., Xu, M.: Fast exposure fusion of detail enhancement for brightest and darkest regions. Vis. Comput. 37, 1233–1243 (2021)
Shen, J., Zhao, Y., He, Y.: Detail-preserving exposure fusion using subband architecture. Vis. Comput. 28(5), 463–473 (2012)
Prabhakar, K.R., Srikar, V.S., Babu, R.V.: Deepfuse: A deep unsupervised approach for exposure fusion with extreme exposure image pairs. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 4724–4732 (2017)
Xu, H., Ma, J., Jiang, J., Guo, X., Ling, H.: U2fusion: a unified unsupervised image fusion network. IEEE Trans. Pattern Anal. Mach. Intell. 44(1), 502–518 (2022)
Huang, S.-W., Peng, Y.-T., Chen, T.-H., Yang, Y.-C.: Two-exposure image fusion based on cross attention fusion. In: 2021 55th Asilomar Conference on Signals, pp. 867–872 (2021)
Zhao, H., Zheng, J., Shang, X., Zhong, W.: Coarse-to-fine multi-scale attention-guided network for multi-exposure image fusion. Vis. Comput. 06, 1–14 (2023)
He, K., Sun, J., Tang, X.: Guided image filtering. IEEE Trans. Pattern Anal. Mach. Intell. 35(6), 1397–1409 (2013)
Li, H., Ma, K., Yong, H., Zhang, L.: Fast multi-scale structural patch decomposition for multi-exposure image fusion. IEEE Trans. Image Process. 29, 5805–5816 (2020)
Wang, Q., Chen, W., Wu, X., Li, Z.: Detail-enhanced multi-scale exposure fusion in yuv color space. IEEE Trans. Circuits Syst. Video Technol. 30(8), 2418–2429 (2020)
Liu, X.: Perceptual multi-exposure fusion. arXiv:2210.09604 (2022)
Ma, K., Duanmu, Z., Yeganeh, H., Wang, Z.: Multi-exposure image fusion by optimizing a structural similarity index. IEEE Trans. Comput. Imaging 4(1), 60–72 (2017)
Xydeas, C.S., Petrović, V.: Objective image fusion performance measure. Electron. Lett. 36(4), 308–309 (2000)
Acknowledgements
This work was supported by Fundamental Research Funds for the Central Universities (No. 3072022QBZ0803), the China Postdoctoral Science Foundation (No. 2018M631911), and the Heilongjiang Postdoctoral Foundation, China (No. LBH-Z18055).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflicts of interest
All authors declare that there are no conflict of interests.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Li, Y., Yang, Z., Qi, J. et al. An enhanced multi-scale weight assignment strategy of two-exposure fusion. Vis Comput 40, 8603–8614 (2024). https://doi.org/10.1007/s00371-023-03258-2
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00371-023-03258-2