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LGASR: latent-content guided adversarial sand-dust image reconstruction strategy

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Abstract

The research on sand-dust image enhancement usually follows the developmental dynamics of image haze removal and is transitioning from traditional methods to end-to-end (e2e) learning-based algorithms. However, the more complex degradation of sandstorm images inevitably increases the potential risks in e2e algorithms, leading to unstable model performance. To bridge this gap, we reanalyze the extractor-reconstructor structure and propose a latent-content guided adversarial sand-dust image reconstruction (LGASR) strategy. Specifically, LGASR alternates the training of the backbone network between the learning line and the guiding line, optimizing the extractor to accurately capture the latent content of input images and enabling the reconstructor to reconstruct target images based on the extracted content. Additionally, we designed a module named Desandformer to enhance the model’s ability to extract and utilize latent features. Experimental results on both synthetic and real-world sandstorm images demonstrate the superior performance of LGASR.

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References

  1. Si Y, Xu M, Yang F (2024) Hierarchical contrastive learning and color standardization for single image sand-dust removal. Pattern Anal Appl 27(1):5

    Article  Google Scholar 

  2. Gao G, Lai H, Liu Y, Wang L, Jia Z (2021) Sandstorm image enhancement based on yuv space. Optik 226:165659

    Article  Google Scholar 

  3. Wang B, Wei B, Kang Z, Hu L, Li C (2021) Fast color balance and multi-path fusion for sandstorm image enhancement. Signal Image Video Process 15:637–644

    Article  Google Scholar 

  4. Gao G, Lai H, Wang L, Jia Z (2022) Color balance and sand-dust image enhancement in lab space. Multimed Tools Appl 81(11):15349–15365

    Article  Google Scholar 

  5. Gao G, Lai H, Jia Z, Liu Y, Wang Y (2020) Sand-dust image restoration based on reversing the blue channel prior. IEEE Photonics J 12(2):1–16

    Google Scholar 

  6. Yu S, Zhu H, Wang J, Fu Z, Xue S, Shi H (2016) Single sand-dust image restoration using information loss constraint. J Mod Opt 63(21):2121–2130

    Article  Google Scholar 

  7. Lee H (2022) Sandstorm image enhancement using image-adaptive eigenvalue and brightness-adaptive dark channel network. Symmetry 14(11):2310

    Article  Google Scholar 

  8. Shi F, Jia Z, Lai H, Kasabov NK, Song S, Wang J (2023) Sand-dust image enhancement based on light attenuation and transmission compensation. Multimed Tools Appl 82(5):7055–7077

    Article  Google Scholar 

  9. Gao, Y., Xu, W., Lu, Y.(2023): Let you see in haze and sandstorm: Two-in-one low-visibility enhancement network. IEEE Transact Instrum Meas

  10. Ding B, Chen H, Xu L, Zhang R (2022) Restoration of single sand-dust image based on style transformation and unsupervised adversarial learning. IEEE Access 10:90092–90100

    Article  Google Scholar 

  11. Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, et al.(2021): An image is worth 16x16 words: transformers for image recognition at scale. In: Proceedings of the 9th International Conference on Learning Representations

  12. Khan S, Naseer M, Hayat M, Zamir SW, Khan FS, Shah M (2022) Transformers in vision: a survey. ACM Computing Surveys (CSUR) 54(10s):1–41

    Article  Google Scholar 

  13. Zhai X, Kolesnikov A, Houlsby N, Beyer L(2022): Scaling vision transformers. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 12104–12113

  14. Gatys LA, Ecker AS, Bethge M(2016): Image style transfer using convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 2414–2423

  15. Kingma DP, Welling M.(2014): Auto-encoding variational bayes. In: Proceedings of the 2nd International Conference on Learning Representations.

  16. Fu X, Zhuang P, Huang Y, Liao Y, Zhang X-P, Ding X. : A retinex-based enhancing approach for single underwater image. In: 2014 IEEE International Conference on Image Processing (ICIP), pp 4572–4576 (2014). IEEE

  17. Xu G, Wang X, Xu X (2020) Single image enhancement in sandstorm weather via tensor least square. IEEE/CAA J Autom Sin 7(6):1649–1661

    Article  MathSciNet  Google Scholar 

  18. Al-Ameen Z (2016) Visibility enhancement for images captured in dusty weather via tuned tri-threshold fuzzy intensification operators. Int J Intell Syst Appl 8(8):10

    Google Scholar 

  19. Shi Z, Feng Y, Zhao M, Zhang E, He L (2020) Normalised gamma transformation-based contrast-limited adaptive histogram equalisation with colour correction for sand-dust image enhancement. IET Image Process 14(4):747–756

    Article  Google Scholar 

  20. Hua Z, Qi L, Guan M, Su H, Sun Y (2022) Colour balance and contrast stretching for sand-dust image enhancement. IET Image Process 16(14):3768–3780

    Article  Google Scholar 

  21. Kanti Dhara S, Roy M, Sen D, Kumar Biswas P (2021) Color cast dependent image dehazing via adaptive airlight refinement and non-linear color balancing. IEEE Transact Circuits Syst Video Technol 31(5):2076–2081

    Article  Google Scholar 

  22. Peng Y-T, Cao K, Cosman PC (2018) Generalization of the dark channel prior for single image restoration. IEEE Transact Image Process 27(6):2856–2868

    Article  MathSciNet  Google Scholar 

  23. Shi Z, Feng Y, Zhao M, Zhang E, He L (2019) Let you see in sand dust weather: A method based on halo-reduced dark channel prior dehazing for sand-dust image enhancement. IEEE Access 7:116722–116733

    Article  Google Scholar 

  24. He K, Sun J, Tang X (2011) Single image haze removal using dark channel prior. IEEE Transact Pattern Anal Mach Intell 33(12):2341–2353

    Article  Google Scholar 

  25. He K, Sun J, Tang X (2012) Guided image filtering. IEEE Transact Pattern Anal Mach Intell 35(6):1397–1409

    Article  Google Scholar 

  26. Liu Y, Yan Z, Tan J, Li Y (2023) Multi-purpose oriented single nighttime image haze removal based on unified variational retinex model. IEEE Transact Circuits Syst Video Technol 33(4):1643–1657

    Article  Google Scholar 

  27. Zhang W, Zhuang P, Sun H-H, Li G, Kwong S, Li C (2022) Underwater image enhancement via minimal color loss and locally adaptive contrast enhancement. IEEE Transact Image Process 31:3997–4010

    Article  Google Scholar 

  28. Zhou X, Huang H, He R, Wang Z, Hu J, Tan T(2023): Msra-sr: Image super-resolution transformer with multi-scale shared representation acquisition. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp 12665–12676

  29. Si Y, Yang F, Liu Z (2022) Sand dust image visibility enhancement algorithm via fusion strategy. Sci Rep 12(1):13226

    Article  Google Scholar 

  30. Gao G, Lai H, Jia Z et al (2023) Two-step unsupervised approach for sand-dust image enhancement. Int J Intell Syst 2023:4506331

    Article  Google Scholar 

  31. Liang P, Ding W, Fan L, Wang H, Li Z, Yang F, Wang B, Li C (2022) Multi-scale and multi-patch transformer for sandstorm image enhancement. J Vis Commun Image Represent 89:103662

    Article  Google Scholar 

  32. Shi J, Wei B, Zhou G, Zhang L(2023): Sandformer: Cnn and transformer under gated fusion for sand dust image restoration. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, pp 1–5 . IEEE

  33. Shi W, Caballero J, Huszár F, Totz J, Aitken AP, Bishop R, Rueckert D, Wang Z(2016): Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 1874–1883

  34. Song Y, He Z, Qian H, Du X (2023) Vision transformers for single image dehazing. IEEE Transact Image Process 32:1927–1941

    Article  Google Scholar 

  35. Zamir SW, Arora A, Khan S, Hayat M, Khan FS, Yang M-H(2022): Restormer: efficient transformer for high-resolution image restoration. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 5728–5739

  36. Qin X, Wang Z, Bai Y, Xie X, Jia H(2020): Ffa-net: feature fusion attention network for single image dehazing. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 11908–11915

  37. Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2020) Generative adversarial networks. Commun ACM 63(11):139–144

    Article  MathSciNet  Google Scholar 

  38. Ganin Y, Lempitsky V(2015): Unsupervised domain adaptation by backpropagation. In: Proceedings of International Conference on Machine Learning, pp. 1180–1189

  39. Si Y, Yang F, Guo Y, Zhang W, Yang Y (2022) A comprehensive benchmark analysis for sand dust image reconstruction. J Vis Commun Image Represent 89:103638

    Article  Google Scholar 

  40. Moorthy A, Bovik A (2009) A modular framework for constructing blind universal quality indices. IEEE Signal Process Lett 17:7

    Google Scholar 

  41. Liu L, Liu B, Huang H, Bovik AC (2014) No-reference image quality assessment based on spatial and spectral entropies. Signal Process: Image Commun 29(8):856–863

    Google Scholar 

  42. Liu J, Liu W, Sun J, Zeng T(2021): Rank-one prior: toward real-time scene recovery. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 14802–14810

  43. Liu J, Liu RW, Sun J, Zeng T (2023) Rank-one prior: real-time scene recovery. IEEE Transact Pattern Anal Mach Intell 45(7):8845–8860

    Article  Google Scholar 

  44. Zheng L, Li Y, Zhang K, Luo W (2023) T-net: deep stacked scale-iteration network for image dehazing. IEEE Trans Multimedia 25:6794–6807. https://doi.org/10.1109/TMM.2022.3214780

    Article  Google Scholar 

  45. Wang C-Y, Bochkovskiy A, Liao H-YM (2023): Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 7464–7475

  46. Li B, Ren W, Fu D, Tao D, Feng D, Zeng W, Wang Z (2018) Benchmarking single-image dehazing and beyond. IEEE Transact Image Process 28(1):492–505

    Article  MathSciNet  Google Scholar 

  47. Liao Y, Su Z, Liang X, Qiu B (2018): Hdp-net: Haze density prediction network for nighttime dehazing. In: Proceedings of the Pacific Rim Conference on Multimedia, pp 469–480

  48. Li C, Guo C, Ren W, Cong R, Hou J, Kwong S, Tao D (2019) An underwater image enhancement benchmark dataset and beyond. IEEE Transact Image Process 29:4376–4389

    Article  Google Scholar 

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Acknowledgements

This work is supported by National Key Research and Development Project of China (2019YFB1312102) and Natural Science Foundation of Hebei Province (F2019202364).

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Authors and Affiliations

  1. Jiangsu Province Engineering Research Center of Integrated Circuit Reliability Technology and Testing System, Wuxi University, Wuxi, 214105, China

    Yazhong Si & Chen Li

  2. School of Electronic and Information Engineering, Wuxi University, Wuxi, 214105, China

    Yazhong Si & Chen Li

  3. School of Electronic and Information Engineering, Hebei University of Technology, Tianjin, 300401, China

    Yazhong Si & Fan Yang

Authors
  1. Yazhong Si
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Contributions

Yazhong Si: Conceptualization, Formal analysis, Methodology, Validation, Project administration, Writing—original draft, Writing—review & editing. Chen Li: Visualization, Software, Data curation, Writing—review & editing. Fan Yang: Supervision, Resources, Visualization, Writing—review & editing. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Yazhong Si.

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Si, Y., Li, C. & Yang, F. LGASR: latent-content guided adversarial sand-dust image reconstruction strategy. J Supercomput 81, 168 (2025). https://doi.org/10.1007/s11227-024-06638-0

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