Abstract
The dark channel prior dehazing algorithm can clear the foggy images to different degrees. However, the algorithm still has some deficiencies, such as the halo phenomenon at the edge of the image in the area of sudden change in depth of field; inaccurate estimation of transmittance, resulting in color shift in the image; inaccurate estimation of atmospheric light value, resulting in darker images after defogging, etc. Therefore, it is necessary to improve the dark channel prior algorithm. This paper improves the dehazing algorithm on the basis of in-depth study of the dark channel prior algorithm, and proposes a new dehazing algorithm based on the atmospheric scattering model. The simulation results show that the improved algorithm in this paper can effectively suppress the halo and color distortion in the abrupt depth of field area, and the obtained defogged image has rich detail information, clearer image, and moderate brightness. The algorithm has improved in objective parameters such as average gradient, structural similarity, peak signal-to-noise ratio and information entropy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Electronic Industry Press, Beijing (2011)
Bai, Z., Wang, B.: Air Particulate Matter Pollution and Prevention. Chemical Industry Press, Beijing (2011)
Wang, D., Zhang, T.: Review and analysis of image dehazing algorithms. J. Graph. 154(06), 861–870 (2020)
Tarel, J.P., Hautière, N.: Fast visibility restoration from a single color or gray level image. In: IEEE International Conference on Computer Vision. IEEE (2010)
Fattal, R.: Single image dehazing. ACM Trans. Graph. 27(3), 1–9 (2008)
Tan, R.T.: Visibility in bad weather from a single image. In: 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Anchorage, Alaska, USA. IEEE (2008)
He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2341–2353 (2011)
Harald, K.: Theorieder horizontalen sichtweite: Kontrast und Sichtweite. Keim & Nemnich (1925)
Mccartney, E.J.: Scattering phenomena. Optics of the atmosphere: scattering by molecules and particles. Science 196, 1084–1085 (1977)
Nayar, S.K., Narasimhan, S.G.: Vision in bad weather. In: Proceedings of the Seventh IEEE International Conference on Computer Vision. IEEE (2002)
Shen, Y., Wang, Q.: Sky region detection in a single image for autonomous ground robot navigation. Int. J. Adv. Rob. Syst. 10(4), 1 (2013)
Li, X., Zhou, L.: Open-set voiceprint recognition adaptive threshold calculation method based on Otsu algorithm and deep learning. J. Jilin Univ. (Nat. Sci. Ed.) 59(04), 909–914 (2021)
Li, X., Niu, H., Zhong, H.: Image defogging method based on mean standard deviation and weighted transmittance. J. Railway Sci. Eng. 17(11), 2938–2945 (2020)
Zhao, X., Huang, F.: Image enhancement based on dual-channel prior and illumination map-guided filtering. Adv. Lasers Optoelectron. 58(8), 45–54 (2021)
Wang, P., Zhang, Y., Bao, F., et al.: Optimal dehazing method based on foggy image degradation model. Chin. J. Image Graph. 23(4), 12 (2018)
Cai, B., Xu, X., Jia, K., et al.: DehazeNet: an end-to-end system for single image haze removal. IEEE Trans. Image Process. 25(11), 5187–5198 (2016)
Ren, W., Liu, S., Zhang, H., Pan, J., Cao, X., Yang, M.-H.: Single image dehazing via multi-scale convolutional neural networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 154–169. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46475-6_10
Rashid, H., Zafar, N., Iqbal, M.J., et al.: Single image dehazing using CNN. Procedia Comput. Sci. 147, 124–130 (2019)
He, Z., Patel, V.M.: Densely connected pyramid dehazing network. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE (2018)
Acknowledgement
This work was supported by the National Nature Science Foundation of China (62076249), Key Research and Development Plan of Shandong Province (2020CXGC010701, 2020LYS11), Natural Science Foundation of Shandong Province (ZR2020MF154).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Pan, X., Wang, H., Liu, Y., Zhao, Z., Huang, F. (2023). Research on Improved Image Dehazing Algorithm Based on Dark Channel Prior. In: Pan, L., Zhao, D., Li, L., Lin, J. (eds) Bio-Inspired Computing: Theories and Applications. BIC-TA 2022. Communications in Computer and Information Science, vol 1801. Springer, Singapore. https://doi.org/10.1007/978-981-99-1549-1_31
Download citation
DOI: https://doi.org/10.1007/978-981-99-1549-1_31
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-1548-4
Online ISBN: 978-981-99-1549-1
eBook Packages: Computer ScienceComputer Science (R0)