Abstract
A fast Graphic Processor Unit (GPU) accelerate algorithm of multi-exposure image fusion with median filter is presented in this paper. The proposed algorithm fuses images in YUV space instead of RGB space compared to traditional image fusion method. Furthermore, in YUV space the brightness components and the chromatism components were weighted fused separately with median filter. At last the filtered images were transferred to RGB and merged to the final fusion image. In the GPU acceleration part, three parallel methods were proposed, including sequence images concurrent execution, adjacent kernels merge, and parallel median filter techniques, to expand the concurrency of the algorithm on the GPU platform. In the experimental results, a 16–21 times speedup was obtained compared to the CPU implementation and up to 60 fps performance was achieved in a 1000 * 1000 * 6 multi-exposure sequence image fusion case. The results in the experiment demonstrate the high efficiency and high availability of our proposed method.
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Ghassemian, H.: A review of remote sensing image fusion methods. Inf. Fusion 32, 75–89 (2016)
El-Gamal, F.E.-Z., Ahmed, M.E., Atwan, A.: Current trends in medical image registration and fusion. Egypt. Inform. J. 17(1), 99–124 (2016)
Xing, C., et al.: Image fusion method based on spatially masked convolutional sparse representation. Image Vis. Comput. 90, 103806 (2019)
Eilertsen, G., Unger, J., Mantiuk, R.K.: Evaluation of tone mapping operators for HDR video. In: High Dynamic Range Video, pp. 185–207. Academic Press (2016)
Endo, Y., Kanamori, Y., Mitani, J.: Deep reverse tone mapping. ACM Trans. Graph. 36(6), 177-1 (2017)
Eilertsen, G., Mantiuk, R.K., Unger, J.: A comparative review of tone‐mapping algorithms for high dynamic range video. Comput. Graph. Forum 36(2), 565–592 (2017)
Du, J., et al.: Union Laplacian pyramid with multiple features for medical image fusion. Neurocomputing 194, 326–339 (2016)
Udhaya Suriya, T.S., Rangarajan, P.: Brain tumour detection using discrete wavelet transform based medical image fusion (2017)
Singh, D., Garg, D., Pannu, H.S.: Efficient landsat image fusion using fuzzy and stationary discrete wavelet transform. Imaging Sci. J. 65(2), 108–114 (2017)
Jiang, Q., et al.: A novel multi-focus image fusion method based on stationary wavelet transform and local features of fuzzy sets. IEEE Access 5, 20286–20302 (2017)
Yu, B., et al.: Hybrid dual-tree complex wavelet transform and support vector machine for digital multi-focus image fusion. Neurocomputing 182, 1–9 (2016)
Ji, X., Zhang, G.: Image fusion method of SAR and infrared image based on Curvelet transform with adaptive weighting. Multimed. Tools Appl. 76(17), 17633–17649 (2015). https://doi.org/10.1007/s11042-015-2879-8
Cai, J., et al.: Fusion of infrared and visible images based on nonsubsampled contourlet transform and sparse K-SVD dictionary learning. Infrared Phys. Technol. 82, 85–95 (2017)
Meng, F., et al.: Image fusion based on object region detection and non-subsampled contourlet transform. Comput. Electr. Eng. 62, 375–383 (2017)
Liu, X., Mei, W., Huiqian, D.: Structure tensor and nonsubsampled shearlet transform based algorithm for CT and MRI image fusion. Neurocomputing 235, 131–139 (2017)
Yin, M., et al.: Medical image fusion with parameter-adaptive pulse coupled neural network in nonsubsampled shearlet transform domain. IEEE Trans. Instr. Meas. 68(1), 49–64 (2018)
Li, S., Kang, X.: Fast multi-exposure image fusion with median filter and recursive filter. IEEE Trans. Consum. Electron. 58(2), 626–632 (2012)
Gastal, E.S.L., Oliveira, M.M.: Domain transform for edge-aware image and video processing. ACM Trans. Graph. 30(4), 69:1–69:11 (2011)
Al-Oraiqat, A.M., Bashkov, E.A., Babkov, V., Titarenko, C.: Fusion of multispectral satellite imagery using a cluster of graphics processing unit. arXiv preprint arXiv:1803.00737 (2018)
Kaehler, A., Bradski, G.: Learning OpenCV 3: Computer Vision in C ++ with the OpenCV Library. O’Reilly Media, Inc., Newton (2016)
Armstrong, D.E.: CUDA GPU Programming Applied to HSI Exploitation. No. LA-UR-17–20565. Los Alamos National Lab. (LANL), Los Alamos, NM, United States (2017)
Reinhard, E., Stark, M., Shirley, P., Ferwerda, J.: Photographic tone reproduction for digital images. In: Proceedings of the ACM SIGGRAPH, pp. 267–276, July 2002
Wang, Z., Bovik, A.: A universal image quality index. IEEE Signal Process. Lett. 9(3), 81–84 (2002)
Qi, G., Chang, L., Luo, Y., et al.: A precise multi-exposure image fusion method based on low-level features. Sensors 20(6), 1597 (2020)
Acknowledgment
This work is supported by National Key Research and Development Program of China (No.2018YFB0204301), the Advanced Research Project of China under grant 31511010202, and the National Natural Science Foundation of China under Grants (No. 61906207)
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Li, S., Yuan, Y., Li, Q., Xie, X. (2020). Graphic Processor Unit Acceleration of Multi-exposure Image Fusion with Median Filter. In: Zeng, J., Jing, W., Song, X., Lu, Z. (eds) Data Science. ICPCSEE 2020. Communications in Computer and Information Science, vol 1257. Springer, Singapore. https://doi.org/10.1007/978-981-15-7981-3_43
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DOI: https://doi.org/10.1007/978-981-15-7981-3_43
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