Evaluation of histogram image defogging methods based on histogram equalization, dark channel prior, and convolutional neural network

Research Article
Open access

Evaluation of histogram image defogging methods based on histogram equalization, dark channel prior, and convolutional neural network

Maojia Chen 1 , Xiaoqing Li 2 , Yifan Liu 3*
  • 1 Chongqing University of Posts and Telecommunications    
  • 2 Beijing Normal university    
  • 3 Changzhou Institute of Technology    
  • *corresponding author 21030614@czust.edu.cn
Published on 23 October 2023 | https://doi.org/10.54254/2755-2721/17/20230915
ACE Vol.17
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-025-7
ISBN (Online): 978-1-83558-026-4

Abstract

Air pollution condition is getting worse with the advancement of society development, environmental pollution has gradually intensified, and smog frequently occurs in more and more cities. On foggy day, the saturation and contrast of an image could be low, and colors tend to drift and distortion. As a result, seeking a simple and effective image de-fogging technique is important for the subsequent research. In this study, three existing classical de-fogging algorithms are reproduced: histogram equalization, dark channel prior method, and convolutional neural network. The three de-fogging algorithms were compared respectively under the conditions of thin fog, thick fog, high brightness, and low brightness, so as to analyze their advantages and disadvantages. It is concluded that there is no obvious difference among the three algorithms in the de-fogging effect under the conditions of thick fog and high brightness, but relatively speaking, the de-fogging image generated by the dark channel prior is more real. When the fog is thin, the dark channel prior and convolutional neural network work better. Under the condition of low brightness, the histogram equalization has a better de-fogging effect.

Keywords:

defogging, machine learning, histogram equalization, convolutional neural network

Chen,M.;Li,X.;Liu,Y. (2023). Evaluation of histogram image defogging methods based on histogram equalization, dark channel prior, and convolutional neural network. Applied and Computational Engineering,17,65-71.
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References

[1]. Zhou, J. C., Zhang, D. H., & Zhang, W. S. (2020). Classical and state-of-the-art approaches for underwater image defogging: a comprehensive survey. Frontiers of Information Technology & Electronic Engineering, 21(12), 1745-1769.

[2]. Arif, Z. H., Mahmoud, M. A., Abdulkareem, K. H., Mohammed, et, al. (2022). Comprehensive review of machine learning (ML) in image defogging: Taxonomy of concepts, scenes, feature extraction, and classification techniques. IET Image Processing, 16(2), 289-310.

[3]. Wu, H., Qu, Y., Lin, S., Zhou, J., Qiao, R., Zhang, Z., et, al. (2021). Contrastive learning for compact single image dehazing. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 10551-10560.

[4]. Zhu, Z., Wei, H., Hu, G., Li, Y., Qi, G., & Mazur, N. (2020). A novel fast single image dehazing algorithm based on artificial multiexposure image fusion. IEEE Transactions on Instrumentation and Measurement, 70, 1-23.

[5]. Liu, W., Zhou, F., Lu, T., Duan, J., & Qiu, G. (2020). Image defogging quality assessment: Real-world database and method. IEEE Transactions on image processing, 30, 176-190.

[6]. Wang, B., Niu, B., Zhao, P., & Xiong, N. N. (2021). Review of single image defogging. International Journal of Sensor Networks, 35(2), 111-120.

[7]. Gupta, B., Tiwari, M., & Singh Lamba, S. (2019). Visibility improvement and mass segmentation of mammogram images using quantile separated histogram equalisation with local contrast enhancement. CAAI Transactions on Intelligence Technology, 4(2), 73-79.

[8]. Lidong, H., Wei, Z., Jun, W., & Zebin, S. (2015). Combination of contrast limited adaptive histogram equalisation and discrete wavelet transform for image enhancement. IET Image Processing, 9(10), 908-915.

[9]. Kil, T. H., Lee, S. H., & Cho, N. I. (2013). A dehazing algorithm using dark channel prior and contrast enhancement. In 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2484-2487.

[10]. Cai, B., Xu, X., Jia, K., Qing, C., & Tao, D. (2016). Dehazenet: An end-to-end system for single image haze removal. IEEE Transactions on Image Processing, 25(11), 5187-5198.

Cite this article

Chen,M.;Li,X.;Liu,Y. (2023). Evaluation of histogram image defogging methods based on histogram equalization, dark channel prior, and convolutional neural network. Applied and Computational Engineering,17,65-71.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 5th International Conference on Computing and Data Science

ISBN：978-1-83558-025-7(Print) / 978-1-83558-026-4(Online)
Editor：Roman Bauer, Marwan Omar, Alan Wang
Conference website: https://2023.confcds.org/
Conference date: 14 July 2023
Series: Applied and Computational Engineering
Volume number: Vol.17
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Zhou, J. C., Zhang, D. H., & Zhang, W. S. (2020). Classical and state-of-the-art approaches for underwater image defogging: a comprehensive survey. Frontiers of Information Technology & Electronic Engineering, 21(12), 1745-1769.

[2]. Arif, Z. H., Mahmoud, M. A., Abdulkareem, K. H., Mohammed, et, al. (2022). Comprehensive review of machine learning (ML) in image defogging: Taxonomy of concepts, scenes, feature extraction, and classification techniques. IET Image Processing, 16(2), 289-310.

[3]. Wu, H., Qu, Y., Lin, S., Zhou, J., Qiao, R., Zhang, Z., et, al. (2021). Contrastive learning for compact single image dehazing. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 10551-10560.

[4]. Zhu, Z., Wei, H., Hu, G., Li, Y., Qi, G., & Mazur, N. (2020). A novel fast single image dehazing algorithm based on artificial multiexposure image fusion. IEEE Transactions on Instrumentation and Measurement, 70, 1-23.

[5]. Liu, W., Zhou, F., Lu, T., Duan, J., & Qiu, G. (2020). Image defogging quality assessment: Real-world database and method. IEEE Transactions on image processing, 30, 176-190.

[6]. Wang, B., Niu, B., Zhao, P., & Xiong, N. N. (2021). Review of single image defogging. International Journal of Sensor Networks, 35(2), 111-120.

[7]. Gupta, B., Tiwari, M., & Singh Lamba, S. (2019). Visibility improvement and mass segmentation of mammogram images using quantile separated histogram equalisation with local contrast enhancement. CAAI Transactions on Intelligence Technology, 4(2), 73-79.

[8]. Lidong, H., Wei, Z., Jun, W., & Zebin, S. (2015). Combination of contrast limited adaptive histogram equalisation and discrete wavelet transform for image enhancement. IET Image Processing, 9(10), 908-915.

[9]. Kil, T. H., Lee, S. H., & Cho, N. I. (2013). A dehazing algorithm using dark channel prior and contrast enhancement. In 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2484-2487.

[10]. Cai, B., Xu, X., Jia, K., Qing, C., & Tao, D. (2016). Dehazenet: An end-to-end system for single image haze removal. IEEE Transactions on Image Processing, 25(11), 5187-5198.

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