Journal of Applied Science and Engineering

Published by Tamkang University Press

1.30

Impact Factor

2.10

CiteScore

Civil Engineering

Chu Zhao1, Hang Li1This email address is being protected from spambots. You need JavaScript enabled to view it., and Man Jiang2This email address is being protected from spambots. You need JavaScript enabled to view it.

1College of Artificial Intelligence, Shenyang Normal University, Shenyang 110034 China

2Liaoning Vocational Technical College of Modern Service, Shenyang, 110164, China

 

Received: June 3, 2025
Accepted: October 1, 2025
Publication Date: October 24, 2025

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.6180/jase.202606_29(6).0007  


In order to solve the shortcomings of the current image dehazing algorithm, which has poor recovery effect and general timeliness, a novel image dehazing algorithm combining the layer priors and encoding-decoding network is proposed. Firstly, the haze image is divided into background layer and haze layer, and the time gradient of background layer and horizontal gradient of haze layer and the pre-trained Gaussian mixture model corresponding to each layer are used as the prior conditions to construct the model function. Then, a channel attention module is added at the end of the encoder and the beginning of the decoder to assign different weights to the haze related feature maps extracted by the encoder and calculate the transmittance accurately. Thirdly, using the proposed fuzzy partition entropy graph cutting algorithm, the transmittance is divided into close-range, mid-range and far-range under different scene light coverage. The experimental results show that the new algorithm has a good dehazing effect on both synthetic and real fog maps compared with other dehazing methods.


Keywords: Image dehazing; Gaussian mixture model; Layer priors; Encoding-decoding network; Fuzzy partition entropy graph cutting algorithm


  1. [1] G. Dimas, D. E. Diamantis, P. Kalozoumis, and D. K. Iakovidis, (2020) “Uncertainty-aware visual perception system for outdoor navigation of the visually challenged" Sensors 20(8): 2385. DOI: 10.3390/s20082385.
  2. [2] E. Zambrano-Serrano, S. Bekiros, M. A. Platas-Garza, C. Posadas-Castillo, P. Agarwal, H. Jahanshahi, and A. A. Aly, (2021) “On chaos and projective synchroniza tion of a fractional difference map with no equilibria using a fuzzy-based state feedback control" Physica A: Statistical Mechanics and its Applications 578: 126100. DOI: 10.1016/j.physa.2021.126100.
  3. [3] M.Bataineh, M. Alaroud, S. Al-Omari, and P. Agar wal, (2021) “Series representations for uncertain fractional IVPs in the fuzzy conformable fractional sense" Entropy 23(12): 1646. DOI: 10.3390/e23121646.
  4. [4] L. Teng, Y. Qiao, and S. Yin, (2024) “Underwater im age denoising based on curved wave filtering and two dimensional variational mode decomposition" Computer Science and Information Systems 21(4): 1765–1781. DOI: 10.2298/CSIS240314057T.
  5. [5] T.Rasham,M.S.Shabbir,P.Agarwal,andS.Momani, (2022) “On a pair of fuzzy dominated mappings on closed ball in the multiplicative metric space with applications" Fuzzy sets and systems 437: 81–96. DOI: 10.1016/j.fss.2021.09.002.
  6. [6] M. Ju, D. Zhang, and X. Wang, (2017) “Single image dehazing via an improved atmospheric scattering model" The Visual Computer 33(12): 1613–1625. DOI: 10.1007/s00371-016-1305-1.
  7. [7] B. Cai, X. Xu, K. Jia, C. Qing, and D. Tao, (2016) “Dehazenet: An end-to-end system for single image haze removal" IEEE transactions on image processing 25(11): 5187–5198. DOI: 10.1109/TIP.2016.2598681.
  8. [8] H.ZhangandV.M.Patel. “Densely connected pyramid dehazing network”. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2018, 3194–3203. DOI: 10.1109/CVPR.2018.00337.
  9. [9] S. Yin, L. Wang, and L. Teng, (2024) “Threshold seg mentation based on information fusion for object shadow detection in remote sensing images" Computer Science and Information Systems 21(4): 1221–1241. DOI: 10.2298/CSIS231230023Y.
  10. [10] L. Yin, L. Wang, W. Huang, S. Liu, B. Yang, and W. Zheng, (2021) “Spatiotemporal analysis of haze in Beijing based on the multi-convolution model" Atmosphere 12(11): 1408. DOI: 10.3390/atmos12111408.
  11. [11] Y. Gao, Q. Li, and J. Li, (2020) “Single image dehazing via a dual-fusion method" Image and Vision Computing 94: 103868. DOI: 10.1016/j.imavis.2019.103868.
  12. [12] I. Yoon, S. Jeong, J. Jeong, D. Seo, and J. Paik, (2015) “Wavelength-adaptive dehazing using histogram merging based classification for UAV images" Sensors 15(3): 6633–6651. DOI: 10.3390/s150306633.
  13. [13] S. An, X. Huang, L. Cao, and L. Wang, (2024) “A com prehensive survey on image dehazing for different atmospheric scattering models" Multimedia Tools and Ap plications 83(14): 40963–40993. DOI: 10.1007/s11042 023-17292-8.
  14. [14] Y. Wang, F. Fu, F. Lai, W. Xu, J. Shi, and J. Wang, (2019) “Haze removal algorithm based on single-images with chromatic properties" Signal Processing: Image Communication 72: 80–91. DOI: 10.1016/j.image.2018.12.010.
  15. [15] H. Fu, W. Liu, H. Chen, and Z. Wang, (2020) “An anisotropic Gaussian filtering model for image de-hazing" Ieee Access 8: 175140–175149. DOI: 10.1109/ACCESS.2020.3026185.
  16. [16] M. Shams, N. Kausar, P. Agarwal, andS.Jain. “Fuzzy fractional Caputo-type numerical scheme for solving fuzzy nonlinear equations”. In: Fractional Differential Equations. Elsevier, 2024, 167–175. DOI: 10.1016/B978-0-44-315423-2.00016-3.
  17. [17] L. Shafi, S. Jain, P. Agarwal, P. Iqbal, and A. R. Sheer gojri, (2024) “An improved fuzzy time series forecasting model based on hesitant fuzzy sets" Journal of fuzzy extension and applications 5(2): 173–189. DOI: 10.22105/jfea.2024.432442.1357.
  18. [18] S. Yin, H. Li, A. A. Laghari, T. R. Gadekallu, G. A. Sampedro, and A. Almadhor, (2024) “An anomaly detection model based on deep auto-encoder and capsule graph convolution via sparrow search algorithm in 6G In ternet of Everything" IEEE Internet of Things Journal 11(18): 29402–29411. DOI: 10.1109/JIOT.2024.3353337.
  19. [19] A. R. Abbasi, (2020) “Probabilistic load flow based on holomorphic embedding, kernel density estimator and saddle point approximation including correlated uncertainty variables" Electric Power Systems Research 183: 106178. DOI: 10.1016/j.epsr.2019.106178.
  20. [20] A.R.AbbasiandA.R.Seifi,(2013) “Anewcoordinated approach to state estimation in integrated power systems" International Journal of Electrical Power & Energy Systems 45(1): 152–158. DOI: 10.1016/j.ijepes.2012.08.058.
  21. [21] D. Adla, G. Reddy, P. Nayak, and G. Karuna, (2023) “A full-resolution convolutional network with a dynamic graph cut algorithm for skin cancer classification and detection" Healthcare Analytics 3: DOI: 10.1016/j.health.2023.100154.
  22. [22] B. S. Krishna, A. S. Anandhi, G. Sharmila, D. Ramya, L. Balaji, and B. Pasupulati. “A Method for Im age De-Hazing for Vision Based Applications”. In: 2024 International Conference on Computational Intelligence for Green and Sustainable Technologies (ICCIGST). IEEE. 2024, 1–6. DOI: 10.1109/ICCIGST60741.2024.10717509.
  23. [23] J. S. Bhadoria and D. Agarwal. “Enhanced Image De-Hazing Using Novel Hybrid Filtering and Perceptual Fog Density Improvement”. In: 2023 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS). IEEE. 2023, 562–567. DOI: 10.1109/ICCCIS60361.2023.10425763.
  24. [24] J. A. H. Shaikh, S. M. Mukane, and S. N. Randive, (2025) “Lightweight progressive recurrent network for video de-hazing in adverse weather conditions" The Visual Computer 41(7): 4661–4672. DOI: 10.1007/s00371-024-03683-x.
  25. [25] E. Radha, D. Suresha, and E. Ramesh. “Design and Implementation of Image De-hazing Using Histogram Equalization”. In: Mobile Radio Communications and 5G Networks: Proceedings of Third MRCN 2022. Springer, 2023, 343–351. DOI: 10.1007/978-981-19-7982-8_28.
  26. [26] M. T. Ahmed, S. H. Bappy, J. K. Tuly, and T. Bil lah, (2023) “An Effective Single Image Dehazing Method to Enhance Image Visibility" Journal of Information Technology 12(1): 1–12. DOI: 10.6035/14028.2017.178642.
  27. [27] A.-R. Abbasi, R. Khoramini, B. Dehghan, M. Abbasi, and E. Karimi, (2015) “A new intelligent method for optimal allocation of D-STATCOM with uncertainty" Journal of Intelligent & Fuzzy Systems 29(5): 1881 1888. DOI: 10.3233/ifs-151666.
  28. [28] A. Kavousi-Fard, R. Khorram-Nia, M. Rostami, and A. Abbasi, (2015) “An smart stochastic approach to model plug-in hybrid electric vehicles charging effect in the optimal operation of micro-grids" Journal of Intelligent &Fuzzy Systems 28(2): 835–842. DOI: 10.3233/ifs-141365.

Latest Articles

Spatio-Temporal Heterogeneous learning for Traffic Flow Forecasting in Smart Transportation
Spatio-Temporal Heterogeneous learning for Traffic Flow Forecasting in Smart TransportationVolume 29, Issue 6 (In press)

Read more: ...

Pill Counting Method for Strip Plate Slots Based on YOLOv12
Pill Counting Method for Strip Plate Slots Based on YOLOv12Volume 29, Issue 6 (In press)

Read more: ...

Multifactor Reliability Analysis of a Cylindrical Structure Integrated with Wind-Solar Power and Air Condensation Water Production Device
Multifactor Reliability Analysis of a Cylindrical Structure Integrated with Wind-Solar Power and Air Condensation Water Production DeviceVolume 29, Issue 6 (In press)

Read more: ...

On Fuzzy Fréchet Contractions Governed by Rational Expressions and Related ApplicationsVolume 29, Issue 6 (In press)

Read more: ...

Layer Priors and Encoding-decoding Network for Image Dehazing
Layer Priors and Encoding-decoding Network for Image DehazingVolume 29, Issue 6 (In press)

Read more: ...

Optimization-Driven Cost Estimation in House Construction: A Predictive Modeling Approach Using Fruit Fly Optimization Algorithm
Optimization-Driven Cost Estimation in House Construction: A Predictive Modeling Approach Using Fruit Fly Optimization AlgorithmVolume 29, Issue 6 (In press)

Read more: ...

Advancing Traffic Flow Prediction: A Comprehensive Approach to Addressing Urban Congestion and Dynamic Transport Challenges
Advancing Traffic Flow Prediction: A Comprehensive Approach to Addressing Urban Congestion and Dynamic Transport ChallengesVolume 29, Issue 6 (In press)

Read more: ...

The fracture energy assessment of the stone matrix asphalt with recycled concrete aggregate from semi-circular bending test
The fracture energy assessment of the stone matrix asphalt with recycled concrete aggregate from semi-circular bending testVolume 29, Issue 6 (In press)

Read more: ...

Optimizing Water Quality Prediction by Comparing Machine Learning Models in Different States of Alkaline, Acidic, and Neutral
Optimizing Water Quality Prediction by Comparing Machine Learning Models in Different States of Alkaline, Acidic, and Neutral Volume 29, Issue 6 (In press)

Read more: ...

    



 

2.1
2023CiteScore
 
 
69th percentile
Powered by  Scopus

SCImago Journal & Country Rank

You may also like these articles below...

Most Popular Articles

Research Categories

Enter your name and email below to receive latest published articles in Journal of Applied Science and Engineering.