Noise Level Based Denoising Technique Utilizing Patch- Based Noise Level Estimator for Low-Light Condition Surveillance Image

  • Authors

    • Suhaila Sari
    • Wong Zhi Lin
    • Hazli Roslan
    • Nik Shahidah Afifi Mohd Taujuddin
    • Chua King Lee
    • Siti Zarina Mohd Muji
    2018-11-30
    https://doi.org/10.14419/ijet.v7i4.30.28173
  • Denoising, Low-Light Condition, Mean Absolute Error, Poisson noise, Surveillance image acquisition.

  • Abstract

    Digital Image Processing is a method to obtain image or to take out useful details or feature from image. The noise will cause the results of error in the image acquisition process. Generation of higher noise levels in the low light condition environment will often result in oversmoothed edges and textures during the denoising process because of lower signal levels in the image. Thus, this study goal is to improve denoising techniques for Poisson noise removal in low light condition for surveillance images. The Patch-Based Noise Level Estimator is designed to estimate the noise level of noisy image. The noisy image then fed to either OTSU WIE-WATH Filter or OTSU KU-WIE-WATH Filter automatically based on the noise level of image. The OTSU WIE-WATH Filter is used for low and medium Poisson noise removal while OTSU KU-WIE-WATH Filter is used mainly for high Poisson noise removal. The proposed denoising technique performances are analyzed with other existing denoising techniques in terms of Mean Absolute Error (MAE), computational time and visual effect inspection. The results verified that proposed technique is effective  in removing different level Poisson noise in low light condition surveillance images.

     

     

  • References

    1. [1] Gonzalez RC & Woods RE, Digital Image Processing, Pearson, (2008).

      [2] Wueller D, “Low Light Performance of Digital Still Cameras,†SPIE :Multimedia Content and Mobile Devices, Vol. 8667, (2013), https://spie.org/Publications/Proceedings/Paper/10.1117 /12.2003080

      [3] Kaur S, “Noise types and various removal techniques,†International Journal of Advanced Research in Electronics and Communication Engineering Vol. 4, No. 2,( 2015) pp. 226-230, available online: http://ijarece.org/wp-content/uploads/2015/02/ IJARECE-VOL-4-ISSUE-2-226-230.pdf, last visit:13.08.2018

      [4] Sari S, Chia YY, Mohd MNH, Taujuddin NSAM, Ibrahim N, and Roslan H, “OKWW Filter for Poisson Noise Removal in Low-Light Condition Digital Image,†IEEE International Conference on Signal and Image Processing Applications, (2017) pp. 335-340, https://ieeexplore.ieee.org/document/8120632/

      [5] Immerkaer J, “Fast Noise Variance Estimation,†Computer Vision and Image Understanding, Vol. 64, No. 2, (1996), pp. 300-302, available online: https://www.sciencedirect.com/ science/article/pii/S1077314296900600, last visit: 13.08.2018

      [6] Shin DH, Park RH, Yang S & Jung JH, “Block-based Noise Estimation using Adaptive Gaussian Filtering,†IEEE Transactions on Consumer Electronics, Vol. 51, No. 1, (2005), pp. 218-226, available online: https://ieeexplore.ieee.org/document/ 1405723/, last visit: 13.08.2018

      [7] Liu X, Tanaka M, & Okutomi M, “Single-image Noise Level Estimation for Blind Denoising,†IEEE Transactions on Image Processing, Vol. 22, No. 12, (2013), pp. 5226-5237, available online: https://ieeexplore.ieee.org/document/6607209/, last visit: 13.08.2018

      [8] Tomasi C & Manduchi R, “Bilateral Filtering for Gray and Color Images,†IEEE International Conference of Computer Vision, (1998), pp. 839-846, https://ieeexplore.ieee.org/ document/ 710815/

      [9] Foi A, Trimeche M, Katkovnik V & Egiazarian K, “Practical Poissonian- Gaussian Noise Modeling and Fitting for Single-Image Raw-Data,†IEEE Transactions on Image Processing, Vol. 17, No. 10,( 2008), pp.1737-1754, available online: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=4623175, last visit: 13.08.2018

      [10]Roy S, Sinha N & Sen AK, “A New Hybrid Image Denoising Method,†International Journal of Information Technology and Knowledge Management, Vol. 2, No. 2, (2010), pp. 491-497, http://csjournals.com/IJITKM/PDF%203-1/63.pdf

      [11]Zhang X, Shen P, Luo L, Zhang L & Song J, “Enhancement and Noise Reduction of Very Low Light Level Images,†IEEE International Conference on Pattern Recognition, (2012) pp. 2034-2037, https://ieeexplore.ieee.org/stamp/stamp.jsp?arnum ber=6460559

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  • How to Cite

    Sari, S., Zhi Lin, W., Roslan, H., Shahidah Afifi Mohd Taujuddin, N., King Lee, C., & Zarina Mohd Muji, S. (2018). Noise Level Based Denoising Technique Utilizing Patch- Based Noise Level Estimator for Low-Light Condition Surveillance Image. International Journal of Engineering & Technology, 7(4.30), 544-549. https://doi.org/10.14419/ijet.v7i4.30.28173

    Received date: 2019-03-03

    Accepted date: 2019-03-03

    Published date: 2018-11-30