Farmland Image Dehazing Method Based on Wavelet Precise Integration and Dark Channel Prior

被引：0
|
作者：
Gao R. [1 ]
Mei S. [1 ]
Li L. [1 ]
Wang A. [1 ]
机构：
[1] College of Information and Electrical Engineering, China Agricultural University, Beijing
来源：
Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery | 2019年 / 50卷
关键词：
Dark channel prior; Dehazing; Farmland image; Precise integration; Shannon-Cosine wavelet;
D O I：
10.6041/j.issn.1000-1298.2019.S0.026
中图分类号：
学科分类号：
摘要：
Images collection of farmland is one of the important components of modern agricultural informatization. From the images, information such as the growth and distribution of crops and pests in the field can be monitored. Foggy weather is a special natural weather phenomenon. When the image of farmland is collected, fog is often caused, resulting in blurred and faded images. Aiming at this problem, based on the dark channel prior, a Shannon-Cosine wavelet precise integration method for farmland images dehazing was proposed. Aiming at the problems of the block effects in the transmission image and the loss of image texture after restoration, the transmission image was refined by the proposed algorithm. According to the characteristics of the transmission image, the nonlinear partial differential equation model was used to smooth and preserve the edge of images. The multi-scale Shannon-Cosine wavelet was used to discretize the equations. In this process, Shannon-Cosine wavelet can adaptively select feature points and identify the image texture to highlight the image texture features. This process reduced the size of the equations and the amount of computation. Then the precise integration method was used to solve the equations, and this method also effectively improved the calculation accuracy. The proposed algorithm also improved the atmospheric value A and improved the operation speed. The experimental results showed that the transmission image obtained by the algorithm had clear boundaries and was locally smooth. The recovered image had better definition and richer texture than the original algorithm. Compared with the original dark channel prior algorithm, the proposed algorithm increased the ratio of newly visible edges by 30.36%, the contrast by 40.72%, and the standard deviation by 28.21%. The proposed algorithm had better dehazing results. © 2019, Chinese Society of Agricultural Machinery. All right reserved.
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页码：167 / 174
页数：7
相关论文
共 24 条
  • [1] Yue X., Wang L., Lan Y., Et al., Algorithm of defogging UAV's aerial images based on DCP and OCE, Transactions of the Chinese Society for Agricultural Machinery, 47, pp. 419-425, (2016)
  • [2] Tang Z.X., Image dehazing based on haziness analysis, International Journal of Automation and Computing, 11, 1, pp. 78-86, (2014)
  • [3] Wu D., Zhu Q., The latest research progress of image dehazing, Acta Automatica Sinica, 41, 2, pp. 221-239, (2015)
  • [4] Jiang D., Wang S., Zeng Y., Et al., Agricultural robot visual de-hazing method based on image segmentation map, Transactions of the Chinese Society for Agricultural Machinery, 47, 11, pp. 25-31, (2016)
  • [5] Tan R.T., Visibility in bad weather from a single image, Proceedings of IEEE CVPR, pp. 1-8, (2008)
  • [6] Fattal R., Single image dehazing, ACM Transactions on Graphics, 27, 3, pp. 1-9, (2008)
  • [7] Tarel J.P., Hautiere N., Fast visibility restoration from a single color or gray level image, Proceedings of IEEE the 12th International Conference on Computer Vision, pp. 2201-2208, (2009)
  • [8] He K.M., Sun J., Tang X.O., Single image haze removal using dark channel prior, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1956-1963, (2009)
  • [9] Levin A., Lischinski D., Weiss Y., A closed-form solution to natural image matting, IEEE Transactions on Pattern Analysis and Machine Intelligence, 30, 2, pp. 228-242, (2008)
  • [10] Wei Y., Zhang Y., Mei S., Et al., Image dehazing method based on dark channel prior and interval interpolation wavelet transform, Transactions of the CSAE, 33, pp. 281-287, (2017)
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