Welcome to Francis Academic Press

International Journal of Frontiers in Medicine, 2022, 4(4); doi: 10.25236/IJFM.2022.040407.

Image Dehazing and Enhancement Based on Fuzzy Image Modeling

Author(s)

Songning Lai1, Xuan Ren2

Corresponding Author:
Songning Lai
Affiliation(s)

1School of Information Science and Engineering, Shangdong University, No.72 Binhai Road, Jimo District, Qingdao, Shandong, 266000, China

2School of Health Science and Engineering, University of Shanghai for Science and Technology, 516 Jungong Road, Yangpu, Shanghai, 200093, China

Abstract

The presence of haze greatly limits the visibility of images and the acquisition of scene details, so it is particularly important to explore how to efficiently dehazed images. Based on this problem, we delve into the nature of haze imaging, model the blurred image and estimate the airlight and transmission coefficients in it, invert the dehazed image and perform image enhancement processing. In the algorithm we introduce methods to speed up the operation of the algorithm such as KD tree clustering algorithm and interpolation algorithm to reduce the complexity of the algorithm. Finally, we validate our algorithm with the official dataset REISDE and obtain very satisfactory results with an overall complexity of O(n). Therefore, our algorithm can process dehazed images very efficiently and quickly.

Keywords

Dehaze; HE; CLAHE; Airlight; Transmission Coefficient

Cite This Paper

Songning Lai, Xuan Ren. Image Dehazing and Enhancement Based on Fuzzy Image Modeling. International Journal of Frontiers in Medicine (2022), Vol. 4, Issue 4: 34-41. https://doi.org/10.25236/IJFM.2022.040407.

References

[1] Juan Zhang, and Mengtan Guo, Image defogging algorithm based on recurrent generative adversarial network, Shanghai University of Engineering and Technology, Computer Engineering, 2022, pp.280–287.

[2] Yibing Bin, and Peng Li, An image de-misting method, Nanjing University of Science and Technology, Journal of Computer Applications, 2006, pp.154-156.

[3] Yubao Sun, and Liang Xiao, Outdoor image defogging method based on partial differential equations, Nanjing University of Science and Technologym Journal of System Simulation, 2007, pp.3739-3744+3769.

[4] Qiongxiu Zhang, and Zhisheng Gao, Automated image defogging method based on physical model, \Sichuan University, Journal of Instrumentation, 2008, pp.251-255.

[5] Daxing Zhang, Infrared image enhancement algorithm based on adaptive histogram equalization coupled with Laplace transform, Optical Technique, Zhejiang Finance Vocational College, 2021, pp.747-753.

[6] Han Xiao, and Shiyang XiaoHistogram equalization parallel algorithm based on CUDA architecture, Zhengzhou Normal University, Journal of Guilin University of Technology, 2021, pp.654-663.

[7] W. E. K. Middleton. Vision through the atmosphere. Toronto: University of Toronto Press, 1952.

[8] S. G. Narasimhan and S. K. Nayar. Chromatic framework for vision in bad weather. In Proc. IEEE CVPR, 2000.

[9] Y. Y. Schechner, S. G. Narasimhan, and S. K. Nayar. Instant dehazing of images using polarization. In Proc. IEEE CVPR, 2001.

[10] Dana Berman, Tali Treibitz, Shai Avidan, Air-light Estumation using Haze-lines, IEEE ICCP, 2017.

[11] Fattal. Single image dehazing. ACM Trans. Graph., 27(3): 72, 2008.

[12] K.He, J.Sun, and X.Tang. Single image remowal using dark channel prior. In Proc. IEEE CVPR, 2009.

[13] R.Tan. Visibility in bad weather from a single image. In Proc. IEEE ICCP, 2008.

[14] Dana Berman, Tali Treibitz, Non-Local Image Dehazing, IEEE CVPR, 2016.

[15] Fattal. Dehazing using color-lines, ACM Trans.Graph. 34(1): 13, 2014.

[16] K.Nishino, L.Kratz, and S.Lombardi. Bayesian defogging. Int, Journal of Computer Vision, 98(3): 263-278, 2012.

[17] Zhiqun Liu, and Wanting Yang, Research comparison of several image enhancement algorithms, Anhui Vocational and Technical Co m llege, Journal of Hefei Normal University, 2010, pp.60-63.

[18] Guanqun Huo, Jinbo Lu, Shengxiang Luo, Image stitching research based on CLAHE and improved ZNCC, Southwest Petroleum University, Advances in Lasers and Optoelectronics, 2022.

[19] Dingwen Xue, Jianzhong Li, Optimization of k-means clustering algorithm based on KD tree, Harbin Institute of Technology, Intelligent Computers and Applications, 2021, pp.194-197.