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image of Enhancing Image Dehazing Efficiency with Dual Colour Space Attentional Deep Networks

Abstract

Aims and Background

Image dehazing is an essential task in computer-vision, aimed at enhancing the clarity and visibility of images degraded by fog and haze. Traditional methods often struggle with handling the complex scattering effects of haze and maintaining the natural colours of the scene.

Objectives and Methodology

To address these challenges, we propose a novel approach termed Dual Colour Space Attentional Deep Network (DCSADN) for efficient image dehazing. Our method leverages the advantages of two-colour spaces: RGB and YCbCr, to boost the network's skill to capture both the luminance and chrominance information, thereby improving the dehazing performance. We employ a multi-stage training strategy to optimize the performance of the DCSADN. This multi-stage approach ensures that the network generalizes well across different types of haze conditions. Extensive experiments demonstrate the superiority of our method over state-of-the-art dehazing techniques.

Results

The results indicate that our approach not only achieves higher quantitative scores in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) but also produces dehazed images with more natural colours and details.

Conclusion

The findings reveal that the dual colour space processing and attentional modules are crucial for the enhanced performance of our method and providing a valuable tool for improving image quality in hazy conditions.

© 2024 Bentham Science Publishers
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2024-11-07
2025-06-16

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References

  1. Haouassi S. Wu D. An efficient attentional image dehazing deep network using two color space (ADMC2-net). Sensors (Basel) 2024 24 2 687 10.3390/s24020687 38276379
    [Google Scholar]
  2. Wang J. Ren J. Li H. Sun Z. Luan Z. Yu Z. Liang C. Monfared Y.E. Xu H. Hua Q. DDGANSE: Dual-discriminator GAN with a squeeze-and-excitation module for infrared and visible image fusion. Photonics 2022 9 3 150 10.3390/photonics9030150
    [Google Scholar]
  3. Zheng C. Zhang J. Hwang J.N. Huang B. Double-branch dehazing network based on self-calibrated attentional convolution. Knowl. Base. Syst. 2022 240 108148 10.1016/j.knosys.2022.108148
    [Google Scholar]
  4. Li S. Yuan Q. Zhang Y. Lv B. Wei F. Image dehazing algorithm based on deep learning coupled local and global features. Appl. Sci. (Basel) 2022 12 17 8552 10.3390/app12178552
    [Google Scholar]
  5. Shen X. Song Y. Li S. Hu X. Underwater image enhancement algorithm for dual color spaces. J. Mar. Sci. Technol. 2024 32 2 5
    [Google Scholar]
  6. Wei Y. Li J. Wei R. Lin Z. A lightweight attention-based network for image dehazing. Signal Image Video Process. 2024 18 10 7271 7284 10.1007/s11760‑024‑03392‑x
    [Google Scholar]
  7. Yang W. Tan R. Wang S. Fang Y. Liu J. Single image deraining: From model-based to data-driven and beyond. IEEE Trans. Pattern Anal. Mach. Intell. 2021 43 11 10.1109/TPAMI.2020.2995190
    [Google Scholar]
  8. Shaker Reddy P.C. Sucharitha Y. A design and challenges in energy optimizing CR-wireless sensor networks. Recent Adv. Comput. Sci. Commun. 2023 16 11 82 92 10.2174/2666255816666221104115024
    [Google Scholar]
  9. Li C. Yuan C. Pan H. Yang Y. Wang Z. Zhou H. Xiong H. Single-image dehazing based on improved bright channel prior and dark channel prior. Electronics (Basel) 2023 12 2 299 10.3390/electronics12020299
    [Google Scholar]
  10. Lu Y. Liao M. Di S. Zhao Y. 2023 Single image dehazing network based on serial feature attention. In: Artificial Neural Networks and Machine Learning – ICANN Springer Cham 123 135 10.1007/978‑3‑031‑44213‑1_11
    [Google Scholar]
  11. Zhang C. Jing H. Wei S. Chen J. Shang X. Chen A. DPAFD-net: A dual-path adaptive fusion dehazing network. J. Vis. Commun. Image Represent. 2024 98 104018 10.1016/j.jvcir.2023.104018
    [Google Scholar]
  12. Suneel S. Balaram A. Amina Begum M. Umapathy K. Reddy P.C.S. Talasila V. Quantum mesh neural network model in precise image diagnosing. Opt. Quantum Electron. 2024 56 4 559 10.1007/s11082‑023‑06245‑y
    [Google Scholar]
  13. Madhavi G.B. Bhavani A.D. Reddy Y.S. Kiran A. Chitra N.T. Reddy P.C.S. 2023 Traffic congestion detection from surveillance videos using deep learning. International Conference on Computer, Electronics & Electrical Engineering & their Applications (IC2E3) Srinagar Garhwal, India 2023 1 5 10.1109/IC2E357697.2023.10262545
    [Google Scholar]
  14. Zong P. Hua Z. Li J. Densely integrated multi-branch attentive net for image dehazing. Ain Shams Eng. J. 2024 15 8 102861 10.1016/j.asej.2024.102861
    [Google Scholar]
  15. Li J. Skinner K. Eustice R. Johnson-Roberson M. WaterGAN: Unsupervised generative network to enable real-time color correction of monocular underwater images. IEEE Robot. Autom. Lett. 2018 3 1 387 394 10.1109/LRA.2017.2730363
    [Google Scholar]
  16. Fernando H. Ayoob M. Poravi G. A survey of single image de-raining in 2020. IOT with Smart Systems Springer Singapore Senjyu T. Mahalle P. Perumal T. Joshi A. 2022 761 771 10.1007/978‑981‑16‑3945‑6_75
    [Google Scholar]
  17. Zhao X. Xu F. Liu Z. TransDehaze: Transformer-enhanced texture attention for end-to-end single image dehaze. Vis. Comput. 2024 1 15 10.1007/s00371‑024‑03458‑4
    [Google Scholar]
  18. Dai Q. Cui T. Zhang M. Zhao X. Hou B. 2023 All-in-one image dehazing based on attention mechanism. Intelligent Robotics and Applications Springer Singapore Yang H. 52 63 10.1007/978‑981‑99‑6486‑4_5
    [Google Scholar]
  19. Lyu Z. Chen Y. Hou Y. MCPNet: Multi-space color correction and features prior fusion for single-image dehazing in non-homogeneous haze scenarios. Pattern Recognit. 2024 150 110290 10.1016/j.patcog.2024.110290
    [Google Scholar]
  20. Shaker Reddy PC. Sucharitha Y. An energy-saving data transmission approach based on migrating virtual machine technology to cloud computing. Recent Adv. Electr. Electron. Eng. 2024 17 9 573 581 10.2174/2352096516666230713163440
    [Google Scholar]
  21. Hu A. Xie Z. Xu Y. Xie M. Wu L. Qiu Q. Unsupervised haze removal for high-resolution optical remote-sensing images based on improved generative adversarial networks. Remote Sens. (Basel) 2020 12 24 4162 10.3390/rs12244162
    [Google Scholar]
  22. Ashwini K. Nenavath H. Jatoth R.K. EPQ-GAN: Evolutionary perceptual quality assessment generative adversarial network for image dehazing. IEEE Trans. Intell. Transp. Syst. 2024 1 15 10.1109/TITS.2024.3394857
    [Google Scholar]
  23. Liu T. Zhou B. Dual-channel and two-stage dehazing network for promoting ship detection in visual perception system. Math. Probl. Eng. 2022 2022 1 1 15 10.1155/2022/8998743
    [Google Scholar]
  24. Wang S. Hou Q. Li J. Liu J. TSID-Net: A two-stage single image dehazing framework with style transfer and contrastive knowledge transfer. Vis. Comput. 2024 1 18 10.1007/s00371‑024‑03511‑2
    [Google Scholar]
  25. Reddy P.C.S. Muller P.S. Koka S.N. Sharma V. Sharma N. Mukherjee S. 2023 Detection of encrypted and malicious network traffic using deep learning. 2023 International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics (AIKIIE) Ballari, India 2006 1 6 10.1109/AIKIIE60097.2023.10390386
    [Google Scholar]
  26. Kajo I. Kas M. Chahi A. Ruichek Y. Subspace-guided GAN for realistic single-image dehazing scenarios. Neural Comput. Appl. 2024 36 27 17023 17044 10.1007/s00521‑024‑09969‑4
    [Google Scholar]
  27. Karavarsamis S. Gkika I. Gkitsas V. Konstantoudakis K. Zarpalas D. A survey of deep learning-based image restoration methods for enhancing situational awareness at disaster sites: the cases of rain, snow and haze. Sensors (Basel) 2022 22 13 4707 10.3390/s22134707 35808203
    [Google Scholar]
  28. Fang J. Wang X. Li Y. Zhang X. Zhang B. Gade M. GLUENet: An efficient network for remote sensing image dehazing with gated linear units and efficient channel attention. Remote Sens. (Basel) 2024 16 8 1450 10.3390/rs16081450
    [Google Scholar]
  29. Song Y. Zhao J. Shang C. A multi-stage feature fusion defogging network based on the attention mechanism. J. Supercomput. 2024 80 4 4577 4599 10.1007/s11227‑023‑05644‑y
    [Google Scholar]
  30. Yi W. Dong L. Liu M. Hui M. Kong L. Zhao Y. Towards compact single image dehazing via task-related contrastive network. Expert Syst. Appl. 2024 235 121130 10.1016/j.eswa.2023.121130
    [Google Scholar]
  31. Kumar G.R. Latha S.B. Shaker Reddy P.C. Sucharitha Y. 2024 Multi-agent Iot-based system for intelligent vehicle traffic management using wireless sensor networks. Recent Adv. Electr. Electron. Eng. 2024 17 8 515 522 10.2174/2352096516666230719114956
    [Google Scholar]
  32. Qiu Z. Gong T. Liang Z. Chen T. Cong R. Bai H. Zhao Y. Perception-oriented UAV image dehazing based on super-pixel scene prior. IEEE Trans. Geosci. Remote Sens. 2024 62 1 19 10.1109/TGRS.2024.3393751
    [Google Scholar]
  33. Wang K. Duan Y. Yang Y. Single image dehazing algorithm based on pyramid mutil-scale transposed convolutional network. Syst. Sci. Control Eng. 2021 9 sup1 150 160 10.1080/21642583.2020.1833780
    [Google Scholar]
  34. Wang C. Pan J. Wang W. Fu G. Liang S. Wang M. Wu X.M. Liu J. Correlation matching transformation transformers for UHD image restoration. Proc. Conf. AAAI Artif. Intell. 2024 38 6 5336 5344 10.1609/aaai.v38i6.28341
    [Google Scholar]
  35. Gurusubramani S. Rosy N.A. Chitra D. Isravel Y.A.D. Reddy P.C.S. 2023 Distributed deep leaning attack detection framework for cyber-attacks in IoT network. 3rd International Conference on Advancement in Electronics & Communication Engineering (AECE) Ghaziabad, India 811 816 10.1109/AECE59614.2023.10428380
    [Google Scholar]
  36. Rekha M.N. Prasad M.L. Mukherjee S. Nikam S.V. Sharma S. Reddy P.C.S. 2024 An automatic error recognition approach for machine translation results based on deep learning. 2nd International Conference on Computer, Communication and Control (IC4) Indore, India 2024 1 8 10.1109/IC457434.2024.10486776
    [Google Scholar]
  37. Jyothi E.V.N. Kranthi M. Sailaja S. Sesadri U. Koka S.N. Reddy P.C.S. An adaptive intrusion detection system in Industrial Internet of Things(IIoT) using deep learning. 1st International Conference on Innovative Sustainable Technologies for Energy, Mechatronics, and Smart Systems (ISTEMS) Dehradun, India 2024 1 6 10.1109/ISTEMS60181.2024.10560245
    [Google Scholar]
  38. Qu Y. Chen Y. Huang J. Xie Y. Enhanced Pix2pix dehazing network. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2019 8152 8160 10.1109/CVPR.2019.00835
    [Google Scholar]
  39. Tayubi I.A. Pawar D.N. Kiran A. Reddy P.C.S. Sharma N. Chitra D. 2024 Facial emotion recognition using a local binary pattern based deep learning. 2nd International Conference on Computer, Communication and Control (IC4) Indore, India 2024 1 7 10.1109/IC457434.2024.10486509
    [Google Scholar]
  40. Tang P.W. Lin C.H. Liu Y. Transformer-driven inverse problem transform for fast blind hyperspectral image dehazing. IEEE Trans. Geosci. Remote Sens. 2024 62 1 14 10.1109/TGRS.2024.3349479
    [Google Scholar]
  41. Gonzalez-Sabbagh S. Robles-Kelly A. Gao S. DGD-cGAN: A dual generator for image dewatering and restoration. Pattern Recognit. 2024 148 110159 10.1016/j.patcog.2023.110159
    [Google Scholar]
  42. An S. Huang X. Cao L. Wang L. A comprehensive survey on image dehazing for different atmospheric scattering models. Multimedia Tools Appl. 2023 83 14 40963 40993 10.1007/s11042‑023‑17292‑8
    [Google Scholar]
  43. Shen Y. Wang Y. Wei M. Chen H. Xie H. Cheng G. Wang F.L. Semi‐MoreGAN: Semi‐supervised generative adversarial network for mixture of rain removal. Comput. Graph. Forum 2022 41 7 443 454 10.1111/cgf.14690
    [Google Scholar]
  44. Sharma N. Kumar V. Singla S.K. Single image defogging using deep learning techniques: Past, present and future. Arch. Comput. Methods Eng. 2021 28 7 4449 4469 10.1007/s11831‑021‑09541‑6
    [Google Scholar]
  45. Hartanto C.A. Rahadianti L. Single image dehazing using deep learning. JOIV. Int. J. Inform. Vis. 2021 5 1 76 82 10.30630/joiv.5.1.431
    [Google Scholar]
  46. Jin Y. Yang W. Tan R.T. 2022 Unsupervised night image enhancement: When layer decomposition meets light-effects suppression. Computer Vision – ECCV 2022 Springer Cham Avidan S. Brostow G. Cissé M. Farinella G.M. Hassner T. 2022 404 421 10.1007/978‑3‑031‑19836‑6_23
    [Google Scholar]
  47. Likhitaa P.S. Anand R. 2021 A comparative analysis of image dehazing using image processing and deep learning techniques. 6th International Conference on Communication and Electronics Systems (ICCES) Coimbatre, India 1611 1616 2021 10.1109/ICCES51350.2021.9489118
    [Google Scholar]
  48. Zheng S. Wang R. Zheng S. Wang L. Liu Z. A learnable full-frequency transformer dual generative adversarial network for underwater image enhancement. Front. Mar. Sci. 2024 11 1321549 10.3389/fmars.2024.1321549
    [Google Scholar]
  49. Xu H. Mu P. Liu Z. Cheng S. Underwater image enhancement via color conversion and white balance-based fusion. Vis. Comput. 2024 1 16 10.1007/s00371‑024‑03421‑3
    [Google Scholar]
  50. Wang R. Zhang Y. Zhang Y. Agent-guided non-local network for underwater image enhancement and super-resolution using multi-color space. J. Mar. Sci. Eng. 2024 12 2 358 10.3390/jmse12020358
    [Google Scholar]
  51. Ren S. He K. Girshick R. Sun J. Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 2017 39 6 1137 1149 10.1109/TPAMI.2016.2577031 27295650
    [Google Scholar]
  52. Zhang H. Sindagi V. Patel V.M. Image de-raining using a conditional generative adversarial network. IEEE Trans. Circ. Syst. Video Tech. 2020 30 11 3943 3956 10.1109/TCSVT.2019.2920407
    [Google Scholar]
/content/journals/swcc/10.2174/0122103279344559241007041602
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Enhancing Image Dehazing Efficiency with Dual Colour Space Attentional Deep Networks
Bentham Science Publishers ; https://doi.org/10.2174/0122103279344559241007041602
/content/journals/swcc/10.2174/0122103279344559241007041602
/content/journals/swcc/10.2174/0122103279344559241007041602
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  • Article Type:     Research Article
Keywords: RGB-YCbCr Integration ; Dual Color Space ; Attention Mechanism ; Deep Learning ; attentional deep networks ; Image Dehazing

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