ISSN 0021-3454 (print version)
ISSN 2500-0381 (online version)
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vol 67 / April, 2024 Article

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DOI 10.17586/0021-3454-2021-64-10-811-820
UDC 681.772.7

METHODS FOR IMPROVING IMAGE QUALITY FOR VIDEO SURVEILLANCE SYSTEM OPERATION IN CONDITIONS OF INSUFFICIENT ILLUMINATION

V. A. Kovalevskiy
ITMO University, Saint Petersburg, 197101, Russian Federation,; PhD Student

V. V. Volkhonskiy
ITMO University, Saint Petersburg, 197101, Russian Federation; Professor of Practice


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Abstract. Preparing low quality video images for payload extraction is considered as a challenging task in the field of imaging and computer graphics. The most frequently used methods of image quality improvement are reviewed, such as full or partial exposure change, as well as algorithms for spatial and frequency filtering. The selected algorithms and methods are analyzed with the account for peculiarities of neural network operation with a finite video sequence. Based on the study, the main parameters and criteria of video images were identified to ensure the correct operation of the neural network.
Keywords: image quality improvement method, image filtering methods, exposure time change, histogram equalization, RETINEX

References:
  1. Volkhonsky V.V. Sistemy televizionnogo nablyudeniya: osnovy proyektirovaniya i primeneniya (Television Surveillance Systems: Design and Application Fundamentals), Moscow, 2020, 392 р. (in Russ.)
  2. Hanspal R.K., Sahoo K. International Journal of Science and Research (IJSR), 2017, no. 5(6), pp. 2467–2471.
  3. Kim Y.T. IEEE transactions on Consumer Electronics, 1997, no. 1(43), pp. 1–8.
  4. Kumar M., Rana A. International Journal of Engineering and Computer Science, 2016, no. 6(5), pp. 16977–16979.
  5. Sengee N., Choi H.K. IEEE Transactions on Consumer Electronics, 2008, no. 3(54), pp. 1329–1337.
  6. Azzari L., Foi A. IEEE signal processing letters, 2016, no. 8(23), pp. 1086–1090.
  7. Caballero J. et al. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, рр. 4778–4787.
  8. Celik T., Tjahjadi T. IEEE Transactions on Image Processing, 2011, no. 12(20), pp. 3431–3441.
  9. Chen Z.Y. et al. IEEE transactions on image processing, 2006, no. 8(15), pp. 2290–2302.
  10. Dong X. et al. 2011 IEEE International Conference on Multimedia and Expo, IEEE, 2011, рр. 1–6.
  11. Everingham M. et al. International journal of computer vision, 2010, no. 2(88), pp. 303–338.
  12. Fu X. et al. Signal Processing, 2016, vol. 129, рр. 82–96.
  13. Fu X. et al. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, рр. 2782–2790.
  14. Guo X., Li Y., Ling H. IEEE Transactions on image processing, 2016, no. 2(26), pp. 982–993.
  15. Ibrahim H., Kong N.S.P. IEEE Transactions on Consumer Electronics, 2007, no. 4(53), pp. 1752–1758.
  16. Isola P. et al. Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, рр. 1125–1134.
  17. Kim M. et al. IEEE Transactions on Consumer Electronics, 2015, no. 1(61), pp. 72–80.
  18. Ko S. et al. IEEE Transactions on Industrial Electronics. 2017, no. (), pp.64. №. 8. С. 6392-6401.
  19. Ledig C. et al. Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, рр. 4681–4690.
  20. Lee C. H. et al. 2013 International Conference on Signal-Image Technology & Internet-Based Systems, IEEE, 2013, рр. 43–50.
  21. Lee C., Lee C., Kim C.S. IEEE transactions on image processing, 2013, no. 12(22), pp. 5372–5384.
  22. Lore K. G., Akintayo A., Sarkar S. Pattern Recognition, 2017, vol. 61, рр. 650–662.
  23. Nakai K., Hoshi Y., Taguchi A. 2013 International Symposium on Intelligent Signal Processing and Communication Systems, IEEE, 2013, рр. 445–449.
  24. Pang J., Zhang S., Bai W. 2017 IEEE Symposium on Computers and Communications (ISCC), IEEE, 2017, рр. 1366–1371.
  25. Tao L. et al. 2017 IEEE International Conference on Image Processing (ICIP), IEEE, 2017, рр. 3215–3219.
  26. Tao L. et al. 2017 IEEE Visual Communications and Image Processing (VCIP), IEEE, 2017, рр. 1–4.
  27. Wang Z. et al. IEEE transactions on image processing, 2004, no. 4(13), pp. 600–612.
  28. Yeganeh H., Wang Z. IEEE Transactions on Image Processing, 2012, no. 2(22), pp. 657–667.
  29. Ying Z., Li G., Gao W. A bio-inspired multi-exposure fusion framework for low-light image enhancement, arXiv preprint arXiv:1711.00591, 2017.
  30. Ying Z. et al. International Conference on Computer Analysis of Images and Patterns, Springer, Cham, 2017, рр. 36–46.
  31. Ying Z. et al. Proceedings of the IEEE International Conference on Computer Vision Workshops, 2017, рр. 3015–3022.
  32. Dabov K. et al. Image Processing: Algorithms and Systems, Neural Networks, and Machine Learning, International Society for Optics and Photonics, 2006, vol. 6064, рр. 606414.
  33. Sakaridis C. et al. Proceedings of the European Conference on Computer Vision (ECCV), 2018, рр. 687–704.
  34. Dai D., Van Gool L. 2018 21st International Conference on Intelligent Transportation Systems (ITSC), IEEE, 2018, рр. 3819–3824.
  35. Gharbi M. et al. ACM Transactions on Graphics (TOG), 2017, no. 4(36), pp. 1–12.
  36. Guo S. et al. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, рр. 1712–1722.
  37. Iizuka S., Simo-Serra E., Ishikawa H. ACM Transactions on Graphics (ToG), 2016, no. 4(35), pp. 1–11.
  38. Kingma D.P., Ba J. Adam: A method for stochastic optimization, arXiv preprint arXiv:1412.6980, 2014.
  39. Lv F. et al. BMVC, 2018, рр. 220.
  40. Yang F., Wu J. 2010 International Conference on Computer Design and Applications, IEEE, 2010, vol. 1, рр. V1-346–V1-349.
  41. Lyu G. et al. 2014 12th International Conference on Signal Processing (ICSP), IEEE, 2014, рр. 1022–1025.
  42. Wang D., Niu X., Dou Y. Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics (SPAC), IEEE, 2014, рр. 235–240.
  43. Gonzalez R.C., Woods R.E. Digital Image Processing, Prentice Hall, 2008
  44. Kruegle H. ССTV Surveillance. Analog and Digital Video Practices and Technology, Elsevier Butterworth Heinemann, 2007, 656 р.
  45. Pizer S.M. et al. Computer vision, graphics, and image processing, 1987, no. 3(39), pp. 355–368.
  46. Land E.H., McCann J.J. JOSA, 1971, no. 1(61), pp. 1–11.
  47. Haykin S. Neural Networks a Comprehensive Foundation, McMaster University, 2001.
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