Perbaikan Visibilitas pada Citra Berkabut Kawah Gunung Berapi Kelud Menggunakan Color Attenuation Prior
DOI:
https://doi.org/10.30865/mib.v5i1.2637Keywords:
Color Attenuation, Dark Channel, Dehazing, Haze, VolcanoAbstract
Mount Kelud is one of the volcanoes that erupted in 2014. To observe the activity, CCTV has been installed in the crater peak of Mount Kelud. The crater of Mount Kelud emits gases coming from the bottom of the crater. The gas makes CCTV surveillance undisturbed so that the resulting image will have noise. By using dehazing, visibility can be improved so that the resulting image can be seen clearly. The method used for this operation is Color Attenuation Prior. In the early stages, there is a dark channel process that works to turn low-intensity pixels dark. The second step is to estimate the atmospheric light from the dark channel image. This process is almost in parallel with the estimation of the depth map. The fourth step is estimating the transmission map, which functions to transmit low-intensity pixels to high-intensity pixels. The last one is the radiance recovery scene. The results show that the foggy image is successful, and it eliminates the fog effect, thus enhancing the visibility of the image. And from questionnaire results, we got 80 % positive results from all respondents. Further research so that it can be applied directly or in real-time.References
O. V. Putra and A. Musthafa, “Dehazing Citra Kabut Gunung Berapi Kelud Dengan Color Attenuation Prior Dan Adaptive Gamma Correction,†Fountain Informatics J., vol. 4, no. 2, p. 69, 2019.
S. Zhao, L. Zhang, S. Huang, Y. Shen, and S. Zhao, “Dehazing Evaluation: Real-World Benchmark Datasets, Criteria, and Baselines,†IEEE Trans. Image Process., vol. 29, no. XX, pp. 6947–6962, 2020.
K. He, J. Sun, and X. Tang, “Single image haze removal using dark channel prior,†IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 12, pp. 2341–2353, 2011.
Q. Zhu, J. Mai, L. Shao, and S. Member, “A Fast Single Image Haze Removal Algorithm Using Color Attenuation Prior,†IEEE Trans. Image Process., vol. 24, no. 11, pp. 3522–3533, 2015.
E. A. Kponou, Z. Wang, and L. Li, “A Comprehensive Study on Fast Image Dehazing Techniques,†Int. J. Comput. Sci. Mob. Comput., vol. 2, pp. 146–152, 2013.
H. Lu, Y. Li, S. Nakashima, and S. Serikawa, “Single Image Dehazing through Improved Atmospheric Light Estimation,†Multimed. Tools Appl., 2015.
O. V. Putra, A. Musthafa, and F. R. Pradhana, “A Hybrid Approach on Single Image Dehazing using Adaptive Gamma Correction,†J. Phys. Conf. Ser., vol. 1381, no. 1, 2019.
R. T. Tan, “Visibility in Bad Weather from a Single Image,†in 2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008, pp. 1–8.
R. Fattal, “Single Image Dehazing,†ACM Trans. Graph., vol. 27, no. 3, pp. 72:1----72:9, Aug. 2008.
K. He, J. Sun, and X. Tang, “Guided Image Filtering,†IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 6, pp. 1397–1409, 2013.
J. H. Kim, W. D. Jang, J. Y. Sim, and C. S. Kim, “Optimized contrast enhancement for real-time image and video dehazing,†J. Vis. Commun. Image Represent., vol. 24, no. 3, pp. 410–425, 2013.
M. Yang, Z. Li, and J. Liu, “Super-pixel Based Single Image Haze Removal,†in 2016 Chinese Control and Decision Conference (CCDC), 2016, pp. 1965–1969.
S. C. Huang, B. H. Chen, and W. J. Wang, “Visibility restoration of single hazy images captured in real-world weather conditions,†IEEE Trans. Circuits Syst. Video Technol., vol. 24, no. 10, pp. 1814–1824, 2014.
A. S. Parihar and G. Gupta, “A Study on Dark Channel Prior based Image Enhancement Techniques,†pp. 1–7, 2020.
G. Meng, Y. Wang, J. Duan, S. Xiang, and C. Pan, “Efficient Image Dehazing with Boundary Constraint and Contextual Regularization,†in 2013 IEEE International Conference on Computer Vision, 2013, pp. 617–624.
J. P. Tarel, N. Hautiere, L. Caraffa, A. Cord, H. Halmaoui, and D. Gruyer, “Vision Enhancement in Homogeneous and Heterogeneous Fog,†IEEE Intell. Transp. Syst. Mag., vol. 4, no. 2, pp. 6–20, 2012.
B. H. Chen, S. C. Huang, and F. C. Cheng, “A high-efficiency and high-speed gain intervention refinement filter for haze removal,†J. Disp. Technol., vol. 12, no. 7, pp. 753–759, 2016.
O. V. Putra, B. Prianto, E. M. Yuniarno, and M. H. Purnomo, “Visibility restoration of lake crater hazy image based on dark channel prior,†in 2016 International Computer Science and Engineering Conference (ICSEC), 2016, pp. 1–6.
C. H. Hsieh, Q. Zhao, and W. C. Cheng, “Single Image Haze Removal Using Weak Dark Channel Prior,†2018 9th Int. Conf. Aware. Sci. Technol. iCAST 2018, no. August, pp. 214–219, 2018.
J.-P. Tarel and N. Hauti, “Fast Visibility Restoration from a Single Color or Gray Level Image,†in 2009 IEEE 12th International Conference on Computer Vision (ICCV), 2009, no. August 2015.
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