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A real-time quality improvement method based on an adaptive dynamic screened Poisson equation for surveillance video in sand-dust weather

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Abstract

The videos captured by the surveillance equipment in sand-dust weather will have poor visibility, color distortion, and low contrast, and the performance of surveillance systems is seriously interfered. To solve this problem, a real-time quality improvement method based on an adaptive dynamic screened Poisson equation for surveillance video in sand-dust weather is proposed in the paper. In the proposed method, the surveillance video collected in sand-dust weather is first divided into several segments using a keyframe extraction method, and then each segment of the surveillance video is processed according to the frame difference strategy to obtain the enhanced surveillance video. There are three steps in processing surveillance video segments, one is to extract the background frame using a multi-frame averaging method, the second is to enhance the background frame using an automatically improving frame quality method based on screened Poisson equation, and the third is to use the enhanced background image and the frame difference information to obtain the enhancement result of the frame image to be processed. Through qualitative and quantitative comprehensive experiments on sand-dust images and videos, the experimental results are compared with the existing related methods, the results of processing sand-dust images using our improving frame quality method have the best visual effect and the highest total scores in quantitative analysis. The results of the frame difference strategy show an average 11.36\(\times\) speed up as compared with the framewise quality improvement method and realize the goal of real-time processing surveillance video.

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References

  1. Shen, W., et al.: An image enhancement algorithm of video surveillance scene based on deep learning. IET Image Process. 16, 681–690 (2022). https://doi.org/10.1049/ipr2.12286

    Article  Google Scholar 

  2. Tsakanikas, V., Dagiuklas T.: Video surveillance systems-current status and future trends[J]. Computers & Electrical Engineering. 70, 736–753 (2018)

    Article  Google Scholar 

  3. Agrawal, S.C., Agarwal, R.: A novel contrast and saturation prior for image dehazing. Vis. Comput. (2022). https://doi.org/10.1007/s00371-022-02694-w

    Article  Google Scholar 

  4. Cai, B.L., Xu, X.M., Jia, K., et al.: Dehazenet: an end-to-end system for single image haze removal. IEEE Trans. Image Process. 25(11), 5187–5198 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  5. Yang, J., Jiang, B., Lv, Z., et al.: A real-time image dehazing method considering dark channel and statistics features. J. Real Time Image Proc. 13, 479–490 (2017). https://doi.org/10.1007/s11554-017-0671-x

    Article  Google Scholar 

  6. 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)

    Article  Google Scholar 

  7. Yang, Y., Zhang, C., Liu, L., et al.: Visibility restoration of single image captured in dust and haze weather conditions. Multidim. Syst. Sign Process. 31, 619–633 (2020). https://doi.org/10.1007/s11045-019-00678-z

    Article  Google Scholar 

  8. Li, M., Liu, J., Yang, W., et al.: Structure-revealing lowlight image enhancement via robust retinex model. IEEE Trans. Image Process. 27(99), 1 (2018)

    Google Scholar 

  9. He, J., Zhang, V., Yang, R., Zhu, K.: Convex optimization for fast image dehazing. IEEE Int. Conf. Image Process. (ICIP), pp. 2246-2250, (2016). https://doi.org/10.1109/ICIP.2016.7532758

    Article  Google Scholar 

  10. Salazar-Colores, S., Cabal-Yepez, E., Ramos Arreguin, J.M., Botella, G., Ledesma-Carrillo, L.M., Ledesma, S.: A fast image dehazing algorithm using morphological reconstruction. IEEE Trans. Image Process. 28(5), 2357–2366 (2019). https://doi.org/10.1109/TIP.2018.2885490

    Article  MathSciNet  Google Scholar 

  11. Vazquez-Corral, J., Galdran, A., Cyriac, P., et al.: A fast image dehazing method that does not introduce color artifacts. J. Real Time Image Proc. 17, 607–622 (2020). https://doi.org/10.1007/s11554-018-0816-6

    Article  Google Scholar 

  12. Lee, H.: Efficient sandstorm image color correction using rank-based singular value recombination. Symmetry 14(8), 1501 (2022). https://doi.org/10.3390/sym14081501

    Article  Google Scholar 

  13. Xu, G., Wang, X., Xu, X.: Single image enhancement in sandstorm weather via tensor least square. IEEE/CAA J. Autom. Sin. 7(6), 1649–1661 (2020). https://doi.org/10.1109/JAS.2020.1003423

    Article  Google Scholar 

  14. Hua, Z., Qi, L., Guan, M., Su, H., Sun, Y.: Colour balance and contrast stretching for sand-dust image enhancement. IET Image Process. 16, 3768–3780 (2022). https://doi.org/10.1049/ipr2.12592

    Article  Google Scholar 

  15. Zhang, Z., He, H.: A customized low-rank prior model for structured cartoon-texture image decomposition. Signal Process. Image Commun. 96(8), 116308 (2021)

    Article  Google Scholar 

  16. Shi, Z., Feng, Y., Zhao, M., Zhang, E., He, L.: 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 (2019). https://doi.org/10.1109/ACCESS.2019.2936444

    Article  Google Scholar 

  17. Gao, G., Lai, H., Jia, Z., Liu, Y., Wang, Y.: Sand-dust image restoration based on reversing the blue channel prior. IEEE Photonics J. 12(2), 3900216 (2020). https://doi.org/10.1109/JPHOT.2020.2975833

    Article  Google Scholar 

  18. Wang, B., Wei, B., Kang, Z., et al.: Fast color balance and multi-path fusion for sandstorm image enhancement. SIViP 15, 637–644 (2021). https://doi.org/10.1007/s11760-020-01786-1

    Article  Google Scholar 

  19. Fu, X., Yue, H., Zeng, D., Zhang, X.P., Ding, X.: A fusionbased enhancing approach for single sandstorm image. IEEE 16th International Workshop on Multimedia Signal Processing (2014)

  20. Cheng, Y., Jia, Z., Lai, H., Yang, J., Kasabov, N.K.: Blue channel and fusion for sandstorm image enhancement. IEEE Access 8, 66931–66940 (2020). https://doi.org/10.1109/ACCESS.2020.2985869

    Article  Google Scholar 

  21. Ding, B., Chen, H., Xu, L., Zhang, R.: Restoration of single sand-dust image based on style transformation and unsupervised adversarial learning. IEEE Access 10, 90092–90100 (2022). https://doi.org/10.1109/ACCESS.2022.3200163

    Article  Google Scholar 

  22. Liang, P., Dong, P., Wang, F., et al.: Learning to remove sandstorm for image enhancement. Vis. Comput. (2022). https://doi.org/10.1007/s00371-022-02448-8

    Article  Google Scholar 

  23. Si, Y., Yang, F., Liu, Z.: Sand dust image visibility enhancement algorithm via fusion strategy. Sci. Rep. 12, 13226 (2022). https://doi.org/10.1038/s41598-022-17530-3

    Article  Google Scholar 

  24. Si, Y., Yang, F., Guo, Y., et al.: A comprehensive benchmark analysis for sand dust image reconstruction[J]. J. Vis. Commun. Image Represent. 89, 103638 (2022)

  25. Su, W., Zhang, Y., Peng, D., et al.: Fast automatic white balancing method by color histogram stretching. 2011 4th International Congress on Image and Signal Processing. IEEE (2011)

  26. Morel, J.M., Petro, A.B., Sbert, C.: Screened Poisson equation for image contrast enhancement. Image Process. On Line 4, 16–29 (2014)

    Article  Google Scholar 

  27. Shlens, J.: A tutorial on principal component analysis (2014). arXiv:1404.1100

  28. Ni, D., Jia, Z., Yang, J., et al.: A fast sand-dust video quality improvement method based on adaptive dynamic guided filtering and interframe detection strategy[J]. J. Real-Time Image Proc. 19(6), 1181–1197 (2022)

    Article  Google Scholar 

  29. Moore, B.E., Gao, C., Nadakuditi, R.R.: Panoramic robust PCA for foreground-background separation on noisy, free-motion camera video. IEEE Trans. Comput. Imaging 5(2), 195–211 (2019). https://doi.org/10.1109/TCI.2019.2891389

    Article  Google Scholar 

  30. Liu, Q., Li, X.: Efficient low-rank matrix factorization based on l1,\(\epsilon\)-norm for online background subtraction. IEEE Trans. Circuits Syst. Video Technol. 32(7), 4900–4904 (2022). https://doi.org/10.1109/TCSVT.2021.3129503

    Article  Google Scholar 

  31. Zhu, Z., Wei, H., Hu, G., et al.: A novel fast single image dehazing algorithm based on artificial multiexposure image fusion. IEEE Trans. Instrum. Meas. 70(99), 1 (2020)

    Article  Google Scholar 

  32. Mittal, A., Soundararajan, R., Bovik, A.C.: Making a ‘completelyblind’ image quality analyzer. IEEE Signal Process 20, 209–212 (2013)

    Article  Google Scholar 

  33. Halmaoui, H., Cord, A., Hautière, N.: Contrast restoration of road images taken in foggy weather. IEEE International Conference on Computer Vision Workshops, ICCV 2011 Workshops, Barcelona, Spain, November 6–13, 2011. IEEE (2011)

  34. Hautiere, N., Tarel, J.P., Aubert, D., Dumont, E.: Blind contrast enhancement assessment by gradient rationing at visible edges. Image Anal. Stereol. 27(2), 87–95 (2011)

    Article  MATH  Google Scholar 

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Authors and Affiliations

  1. School of Information Science and Engineering, Xinjiang University, Ürümqi, 830046, China

    Dongdong Ni & Zhenhong Jia

  2. Key Laboratory of Signal Detection and Processing, Xinjiang University, Ürümqi, 830046, China

    Dongdong Ni & Zhenhong Jia

  3. Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, 200400, China

    Jie Yang

  4. Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland, 1020, New Zealand

    Nikola Kasabov

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  1. Dongdong Ni
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  2. Zhenhong Jia
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ND wrote the main manuscript text. All authors reviewed the manuscript.

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Correspondence to Zhenhong Jia.

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This work was supported by the National Science Foundation of China under Grant 62261053, the International Science and Technology Cooperation Project of the Ministry of Education of the People’s Republic of China under Grant 2016-2196, and the Excellent Doctoral Research Innovation Program of Xinjiang University under Grant XJU2022BS067.

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Ni, D., Jia, Z., Yang, J. et al. A real-time quality improvement method based on an adaptive dynamic screened Poisson equation for surveillance video in sand-dust weather. J Real-Time Image Proc 20, 105 (2023). https://doi.org/10.1007/s11554-023-01361-0

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