Abstract
Haze Removal Using Dark Channel Prior is one of dehazing methods with good effects, but its disadvantage of high time complexity limits the extent of its applications. In this paper, we present its parallel implementation and optimization based on the GPU, which greatly reduces the execution time. We firstly implement the basic parallel program, and then optimize it to obtain performance acceleration with more than 20 times. We introduce the method of “guide image filter” to Haze Removal Using Dark Channel Prior, instead of “soft matting” method, which largely reduces memory requirements and the computation complexity of the original algorithm. While paralleling and optimizing the algorithm, we improve some parts of the original serial program or basic parallel program according to the characteristics of several steps, and propose a novel method of selecting atmospheric light and a new parallel method of calculating accumulative sum with keeping intermediate results, which reduce the computation complexity of counterpart and increase the parallel degree.
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References
Narasimhan, S.G., Nayar, S.K.: Contrast restoration of weather degraded images. IEEE Trans. Pattern Anal. Mach. Intell. 25(6), 713–724 (2003)
Lv, X.Y., Chen, W.B., Shen, I.F.: Real-Time Dehazing for Image and Video. In: 18th Pacific Conference on Computer Graphics and Applications, pp. 62–69. IEEE Computer Society, Piscataway (2010)
Li, Y.Z.: Uneven Cloud and Fog Removing for Satellite Remote Sensing Image. In: 2011 2nd International Conference on Mechanic Automation and Control Engineering, pp. 5485–5488. IEEE Computer Society, Piscataway (2011)
Liu, Q.L., Zhang, H.Y., Lin, M.S., Wu, Y.D.: Research on Image Dehazing Algorithms based on Physical Model. In: 2011 International Conference on Multimedia Technology, pp. 467–470. IEEE Computer Society, Piscataway (2011)
Xie, B., Guo, F., Cai, Z.X.: Improved Single Image Dehazing Using Dark Channel Prior and Multi-Scale Retinex. In: 2010 International Conference on Intelligent System Design and Engineering Application (ISDEA), pp. 848–851. IEEE Computer Society, Piscataway (2010)
He, K.M., Sun, J., Tang, X.O.: Single image haze removal using dark channel prior. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1956–1963. IEEE Press, IEEE Computer Society, Piscataway (2009)
Levin, A., Lischinski, D., Weiss, Y.: A closed form solution to natural image matting. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 61–68. Institute of Electrical and Electronics Engineers Computer Society (2006)
He, K., Sun, J., Tang, X.: Guided Image Filtering. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part I. LNCS, vol. 6311, pp. 1–14. Springer, Heidelberg (2010)
NVIDIA. NVIDIA CUDA Compute Unified Device Architecture-Programming Guide Version 2.0. (2008)
NVIDIA. NVIDIA CUDATM Developer Guide for NVIDIA Optimus Platforms Version 1.0 (2010)
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Xue, Y., Ren, J., Su, H., Wen, M., Zhang, C. (2013). Parallel Implementation and Optimization of Haze Removal Using Dark Channel Prior Based on CUDA. In: Zhang, Y., Li, K., Xiao, Z. (eds) High Performance Computing. HPC 2012. Communications in Computer and Information Science, vol 207. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41591-3_9
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DOI: https://doi.org/10.1007/978-3-642-41591-3_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41590-6
Online ISBN: 978-3-642-41591-3
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