Пекин, Китайская Народная Республика
Университет Китайской академии наук
Пекин, Китайская Народная Республика
Государственная главная лаборатория космической пого-ды, Академия наук КНР
Пекин, Китайская Народная Республика
A new image enhancement algorithm employing guided filtering is proposed in this work for enhancement of solar images and videos, so that users can easily figure out important fine structures imbedded in the recorded images/movies for solar observation. The proposed algorithm can efficiently remove image noises, including Gaussian and impulse noises. Meanwhile, it can further highlight fibrous structures on/beyond the solar disk. These fibrous structures can clearly demonstrate the progress of solar flare, prominence coronal mass emission, magnetic field, and so on. The experimental results prove that the proposed algorithm gives significant enhancement of visual quality of solar images beyond original input and several classical image en-hancement algorithms, thus facilitating easier determi-nation of interesting solar burst activities from recorded images/movies.
guided filter, Gaussian filter, bilateral filter, edge preserving, image enhancement
1. Bae S., Paris S., Durand F. Two-scale tone management for photographic look. SIGGRAPH’06. Proc. ACM SIGGRAPH 2006. Boston, Massachusetts, 2006.
2. Bo Chen, Jin-Lin Cai, Wen-Sheng Chen, Yan Li. A multiplicative noise removal approach based on partial differential equation model. Mathematical Problems in Engineering. 2012, vol. 2012, ID 242043, 14 p. DOI:https://doi.org/10.1155/2012/242043.
3. Briggs W.L., Henson V.E., McCormick S.F. A Multigrid Tutorial. Second ed. Society for Industrial and Applied Mathematics (SIAM). 2000.
4. Bundy A., Lincoln Wallen. Difference of Gaussians. Catalogue of Artificial Intelligence Tools. Springer Berlin Heidelberg, 1984, p. 30.
5. Chen Xu, Min Li, Xiaoli Sun. An edge-preserving variational method for image decomposition. Chinese J. Electronics. 2013, vol. 22, no. 1, pp. 109-113.
6. Durand F., Dorsey J. Fast Bilateral Filtering for the Display of High-Dynamic-Range Images. SIGGRAPH’02. Proc. 29th Annual Conf. on Computer Graphics and Interactive Technologies, San Antonio, Texas, 2002.
7. Gastal E.S.L., Oliveira M.M. Domain transform for edge-aware image and video processing. ACM Trans. Graphics. 2011, vol. 30, no. 4, pp. 69:1-69:12.
8. Han Yu, et al. Multiplicative noise removal combining a total variation regularizer and a nonconvex regularizer. Intern. J. Computer Math. 2014, vol. 91, no. 10, pp. 2243-2259.
9. He K., Sun J., Tang X. Guided image filtering. IEEE Trans. Pattern Analysis and Machine Intelligence. 2013, vol. 35, no. 6, pp. 1397-1409.
10. Huang T., Yang G., Tang G. A fast two-dimensional median filtering algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing. 1979, vol. 27, no. 1, pp. 13-18.
11. Kim Sang Ho, Jan P. Allebach. Optimal unsharp mask for image sharpening and noise removal. J. Electronic Imaging. 2005, vol. 14, no. 2, pp. 023005-023005.
12. Li Jingna, Li Xia. Symplectic flow for the square root of the negative Laplacian. J. Mathematical Analysis and Applications. 2012, vol. 389, pp. 812-820.
13. Li Xia, Zheng An Yao, Wen Shu Zhou. Existence of positive solutions for a singular p-Laplacian differential equation. Acta Mathematica Sinica. 2008, vol. 24, no. 8, pp. 1331-1344.
14. Lu Jian, et al. An enhanced fractal image denoising algorithm. Chaos, Solitons & Fractals. 2008, vol. 38, no. 4, pp. 1054-1064. DOI:https://doi.org/10.1016/j.chaos.2007.06.048.
15. Saad Y. Iterative Methods for Sparse Linear Systems. Society for Industrial and Applied Mathematics (SIAM), 2003. 528 p.
16. Song Huijuan, Jingxue Yin, Ying Yang. Multiplicity of positive radial solutions for the weighted p-Laplacian in Rn∖{0}. Computers & Mathematics with Applications. 2013, vol. 66, no. 8. pp. 1475-1487.
17. Sun Jiebao, Jing Li, Qiang Liu. Cauchy problem of a nonlocal p-Laplacian evolution equation with nonlocal convection. Nonlinear Analysis: Theory, Methods & Applications. 2014, vol. 95, pp. 691-702.
18. Sun Xiaoli, Min Li, Weiqiang Zhang. An improved image denoising model based on the directed diffusion equation. Computers & Mathematics with Applications. 2011, vol. 61, no. 8, pp. 2177-2181.
19. Tomasi C., Manduchi R. Bilateral filtering for gray and color images. Proc. 1998 IEEE Intern. Conf. on Computer Vision, Bombay, India. 1998, p. 839.
20. Wang Jiefei, et al. A Residual-Based Kernel Regression Method for Image Denoising. Mathematical Problems in Engineering 2016, vol. 2016, ID 5245948, 13 p. DOI: http://dx.doi. org/10.1155/2016/5245948.
