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Sis Kaldırma Algoritmalarına Genel Bir Bakış

Year 2023, Volume: 6 Issue: 1, 46 - 60, 30.06.2023

Abstract

Sisli ve puslu havalarda çekilen görüntüler gerçekliğini kaybetmektedir. Bu görüntülerden sisin kaldırılmasına, sis kaldırma (dehazing, defogging, fog removal) denilmektedir. Sis kaldırma işleminden sonra elde edilen imgenin içindeki görünürlük artmaktadır. Sis kaldırma işlemi, imgenin yakalandığı zamana (gece, gündüz), imge içerisindeki sisin yoğunluğuna, imge içerisindeki ışık kaynağına vb. etkenlere doğrudan bağlıdır. Literatürde, birçok araştırmacı sis kaldırma problemini çözmek için farklı algoritmalar kullandılar. Bu bildiride, literatürde yaygın olarak kullanılan sis kaldırma algortimaları incelenecektir. Bu incelemeler yapılırken, farklı algortimalardan elde edilen sonuçlar birbirleri ile farklı görüntü kalitesi ölçütleri aracılığı ile karşılaştırılacak ve algoritmaların güçlü ve zayıf yönleri ortaya çıkarılacaktır. İncelemelerde, hem gerçek sis içeren görüntüler, hem de yapay olarak sis eklenmiş görüntüler içeren O-HAZE veri kümesinden örnekler kullanılacaktır

References

  • Narasimhan, Srinivasa G. and Shree K. Nayar. “Chromatic framework for vision in bad weather.” IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 598-605, 2000.
  • Kaiming He, Jian Sun, & Xiaoou Tang. “Single image haze removal using dark channel prior”. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011.
  • Levin, A., Lischinski, D., Weiss, “Y. A closed-form solution to natural image matting”. IEEE Trans. Pattern Anal. Mach. Intell. 30(2), 228–242, 2007.
  • Tripathi, A., Mukhopadhyay, S. “Single image fog removal using anisotropic diffusion”. IET Image Process. 6(7), 966–975, 2012.
  • Gonzalez, R.C., Woods, R.E.: Digital Image Processing, vol. 2. Addison-Wesley, Boston, 1992.
  • He, K., Sun, J., Tang, X. “Guided image filtering”. IEEE Trans. Pattern Anal. Mach. Intell. 35(6), 1397–1409, 2013.
  • Li, Z., Zheng, J. “Edge-preserving decomposition-based single image haze removal”. IEEE Trans. Image Process. 24(12), 5432– 5441, 2015.
  • Salazar-Colores, S., Cabal-Yepez, E., Ramos-Arreguin, J. M., Botella, G., Ledesma-Carrillo, L. M., & Ledesma, S. “A fast image dehazing algorithm using morphological reconstruction”. IEEE Transactions on Image Processing, 28(5), 2357-2366, 2018.
  • C. Ancuti, C. O. Ancuti, C. De Vleeschouwer and A. C. Bovik, "Day and Night-Time Dehazing by Local Airlight Estimation," in IEEE Transactions on Image Processing, vol. 29, pp. 6264-6275, 2020
  • Ngo, D., Lee, G. D., & Kang, B. “Improved color attenuation prior for single-image haze removal”. Applied Sciences, 9(19), 4011. 2019.
  • Q Zhu, Q.; Mai, J.; Shao, L. “A Fast Single Image Haze Removal Algorithm Using Color Attenuation Prior”. IEEE Trans. Image Process, 24, 3522–3533, 2015.
  • Ngo, D., Lee, G. D., & Kang, B., “Haziness degree evaluator: A knowledge-driven approach for haze density estimation”. Sensors, 21(11), 3896, 2021.
  • Chen, L., Tang, C., Xu, M., & Lei, Z., “Enhancement and denoising method for low-quality MRI, CT images via the sequence decomposition Retinex model, and haze removal algorithm”. Medical & Biological Engineering & Computing, 59(11), 2433-2448, 2021.
  • Huang, S.C., Chen, B.H., Wang, W.J. “Visibility restoration of single hazy images captured in real-world weather conditions”. IEEE Trans. Circuits Syst. Video Technol. 24(10), 1814–1824, 2014.
  • Fattal R,. “Dehazing using color-lines”. ACM Trans. on Graph. 34(1), 1-14, 2014.
  • Omer and M. Andwerman. “Color lines: image specific color representation”. IEEE Conference on Computer Vision and Pattern Recognition, 2004.
  • Peng, Y.T., Cao, K., Cosman, P.C. “Generalization of the dark channel prior for single image restoration”. IEEE Trans. Image Process. 27(6), 2856–2868, 2018.
  • D. Berman, T. Treibitz and S. Avidan, "Single Image Dehazing Using Haze-Lines," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 42, no. 3, pp. 720-734, 2020
  • Zhu, Q., Mai, J., Shao, L. “A fast single image haze removal algorithm using color attenuation prior”. IEEE Trans. Image Process. 24(11), 3522–3533, 2015.
  • Cai, B., Xu, X., Jia, K., Qing, C., Tao, D. “Dehazenet: an end-to-end system for single image haze removal”. IEEE Trans. Image Process. 25(11), 5187–5198, 2016.
  • Li, B., Peng, X., Wang, Z., Xu, J., Feng, D. “Aod-net: all-in-one dehazing network”. IEEE International Conference on Computer Vision. 4770–4778, 2017.
  • Li, R., Pan, J., Li, Z., Tang, J. “Single image dehazing via conditional generative adversarial network”. IEEE Conference on Computer Vision and Pattern Recognition, 8202–8211, 2018.
  • Chen, W. T., Ding, J. J., & Kuo, S. Y., ”PMS-net: Robust haze removal based on patch map for single images”. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 11681-11689), 2019.
  • J. Liu, H. Wu, Y. Xie, Y. Qu and L. Ma, "Trident Dehazing Network," 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2020
  • Kuanar, S., Mahapatra, D., Bilas, M., & Rao, K. R, “Multi-path dilated convolution network for haze and glow removal in nighttime images”. The Visual Computer, 38(3), 1121-1134. 2022.
  • Le-Anh Tran, Seokyong Moon, Dong-Chul Park, “A novel encoder-decoder network with guided transmission map for single image dehazing”, Procedia Computer Science, Volume 204, 682-689, 2022.
  • Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros. Image-to-image translation with conditional adversarial networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1125–1134, 2017.
  • Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P. “Image quality assessment: from error visibility to structural similarity”. IEEE Trans. Image Process. 13(4), 600–612, 2004.
  • Sharma G, Wu W, Dalal E.N. “The CIEDE2000 color-diference formula: implementation notes, supplementary test data, and mathematical observations”. Color Res. Appl. 30(1), 21–30, 2004.
  • Mittal A, Moorthy AK, Bovik A.C. “No-reference image quality assessment in the spatial domain”. IEEE Trans Image Process 21(12), 4695–4708, 2012.
  • Ancuti, C.O., Ancuti, C., Timofte, R., De Vleeschouwer, C. “Ohaze: a dehazing benchmark with real hazy and haze-free outdoor images”. IEEE Conference on Computer Vision and Pattern Recognition Workshops, 754–762, 2018.

An Overview of Fog Removal Algorithms

Year 2023, Volume: 6 Issue: 1, 46 - 60, 30.06.2023

Abstract

Images taken in foggy and hazy weather conditions lose their authenticity. Removing the fog from these images is called fog removal (dehazing, defogging, fog removal). Visibility in the image obtained after the fog removal process increases. The fog removal process depends on the time the image was captured (day, night), the density of the fog in the image, the light source in the image, etc. In the literature, many researchers have used different algorithms to solve the fog removal problem. In this paper, fog removal algorithms, which are widely used in the literature, will be examined. While these examinations are being made, the results obtained from different algorithms will be compared with each other through different image quality criteria and the strengths and weaknesses of the algorithms will be revealed. Samples from the O-HAZE dataset will be used in the reviews, which contain both images with real fog and images with artificial fog.

References

  • Narasimhan, Srinivasa G. and Shree K. Nayar. “Chromatic framework for vision in bad weather.” IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 598-605, 2000.
  • Kaiming He, Jian Sun, & Xiaoou Tang. “Single image haze removal using dark channel prior”. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011.
  • Levin, A., Lischinski, D., Weiss, “Y. A closed-form solution to natural image matting”. IEEE Trans. Pattern Anal. Mach. Intell. 30(2), 228–242, 2007.
  • Tripathi, A., Mukhopadhyay, S. “Single image fog removal using anisotropic diffusion”. IET Image Process. 6(7), 966–975, 2012.
  • Gonzalez, R.C., Woods, R.E.: Digital Image Processing, vol. 2. Addison-Wesley, Boston, 1992.
  • He, K., Sun, J., Tang, X. “Guided image filtering”. IEEE Trans. Pattern Anal. Mach. Intell. 35(6), 1397–1409, 2013.
  • Li, Z., Zheng, J. “Edge-preserving decomposition-based single image haze removal”. IEEE Trans. Image Process. 24(12), 5432– 5441, 2015.
  • Salazar-Colores, S., Cabal-Yepez, E., Ramos-Arreguin, J. M., Botella, G., Ledesma-Carrillo, L. M., & Ledesma, S. “A fast image dehazing algorithm using morphological reconstruction”. IEEE Transactions on Image Processing, 28(5), 2357-2366, 2018.
  • C. Ancuti, C. O. Ancuti, C. De Vleeschouwer and A. C. Bovik, "Day and Night-Time Dehazing by Local Airlight Estimation," in IEEE Transactions on Image Processing, vol. 29, pp. 6264-6275, 2020
  • Ngo, D., Lee, G. D., & Kang, B. “Improved color attenuation prior for single-image haze removal”. Applied Sciences, 9(19), 4011. 2019.
  • Q Zhu, Q.; Mai, J.; Shao, L. “A Fast Single Image Haze Removal Algorithm Using Color Attenuation Prior”. IEEE Trans. Image Process, 24, 3522–3533, 2015.
  • Ngo, D., Lee, G. D., & Kang, B., “Haziness degree evaluator: A knowledge-driven approach for haze density estimation”. Sensors, 21(11), 3896, 2021.
  • Chen, L., Tang, C., Xu, M., & Lei, Z., “Enhancement and denoising method for low-quality MRI, CT images via the sequence decomposition Retinex model, and haze removal algorithm”. Medical & Biological Engineering & Computing, 59(11), 2433-2448, 2021.
  • Huang, S.C., Chen, B.H., Wang, W.J. “Visibility restoration of single hazy images captured in real-world weather conditions”. IEEE Trans. Circuits Syst. Video Technol. 24(10), 1814–1824, 2014.
  • Fattal R,. “Dehazing using color-lines”. ACM Trans. on Graph. 34(1), 1-14, 2014.
  • Omer and M. Andwerman. “Color lines: image specific color representation”. IEEE Conference on Computer Vision and Pattern Recognition, 2004.
  • Peng, Y.T., Cao, K., Cosman, P.C. “Generalization of the dark channel prior for single image restoration”. IEEE Trans. Image Process. 27(6), 2856–2868, 2018.
  • D. Berman, T. Treibitz and S. Avidan, "Single Image Dehazing Using Haze-Lines," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 42, no. 3, pp. 720-734, 2020
  • Zhu, Q., Mai, J., Shao, L. “A fast single image haze removal algorithm using color attenuation prior”. IEEE Trans. Image Process. 24(11), 3522–3533, 2015.
  • Cai, B., Xu, X., Jia, K., Qing, C., Tao, D. “Dehazenet: an end-to-end system for single image haze removal”. IEEE Trans. Image Process. 25(11), 5187–5198, 2016.
  • Li, B., Peng, X., Wang, Z., Xu, J., Feng, D. “Aod-net: all-in-one dehazing network”. IEEE International Conference on Computer Vision. 4770–4778, 2017.
  • Li, R., Pan, J., Li, Z., Tang, J. “Single image dehazing via conditional generative adversarial network”. IEEE Conference on Computer Vision and Pattern Recognition, 8202–8211, 2018.
  • Chen, W. T., Ding, J. J., & Kuo, S. Y., ”PMS-net: Robust haze removal based on patch map for single images”. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 11681-11689), 2019.
  • J. Liu, H. Wu, Y. Xie, Y. Qu and L. Ma, "Trident Dehazing Network," 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2020
  • Kuanar, S., Mahapatra, D., Bilas, M., & Rao, K. R, “Multi-path dilated convolution network for haze and glow removal in nighttime images”. The Visual Computer, 38(3), 1121-1134. 2022.
  • Le-Anh Tran, Seokyong Moon, Dong-Chul Park, “A novel encoder-decoder network with guided transmission map for single image dehazing”, Procedia Computer Science, Volume 204, 682-689, 2022.
  • Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros. Image-to-image translation with conditional adversarial networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1125–1134, 2017.
  • Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P. “Image quality assessment: from error visibility to structural similarity”. IEEE Trans. Image Process. 13(4), 600–612, 2004.
  • Sharma G, Wu W, Dalal E.N. “The CIEDE2000 color-diference formula: implementation notes, supplementary test data, and mathematical observations”. Color Res. Appl. 30(1), 21–30, 2004.
  • Mittal A, Moorthy AK, Bovik A.C. “No-reference image quality assessment in the spatial domain”. IEEE Trans Image Process 21(12), 4695–4708, 2012.
  • Ancuti, C.O., Ancuti, C., Timofte, R., De Vleeschouwer, C. “Ohaze: a dehazing benchmark with real hazy and haze-free outdoor images”. IEEE Conference on Computer Vision and Pattern Recognition Workshops, 754–762, 2018.
There are 31 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Articles
Authors

Bahadır Arabalı

Kemal Fidanboylu 0000-0002-7350-0140

Publication Date June 30, 2023
Published in Issue Year 2023 Volume: 6 Issue: 1

Cite

APA Arabalı, B., & Fidanboylu, K. (2023). Sis Kaldırma Algoritmalarına Genel Bir Bakış. Veri Bilimi, 6(1), 46-60.



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