Image Filter Selection, Denoising and Enhancement Based on Statistical Attributes of Pixel Array

  • Vihar KuramaEmail author
  • T. Sridevi
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 766)


The choice of image filters in computer vision has a significant effect on the image reconstruction and feature extraction. Currently, the most filters are used to enhance images for human consumptions, programmed operations and to reduce the noise, frequency levels in the image. Though it is hard to select an optimal set of filters for a given series of images, in this work, we propose to choose the best assortment of different filters for a given image as the input. By generating the pixel array of the input image, we compute all the image attributes such as RGB colour mean, variance, mean squared error and signal-to-noise ratio values of the input image and then compare with the same, once the filter is applied. We verify the effectiveness of the filters by conducting an empirical evaluation with best-discovered traits.


Image filters Convolution kernel Mean filter Median filter SNR 


  1. 1.
    He, K., Sun, J., Tang, X.: Guided image filtering. IEEE Trans. Pattern Anal. Mach. Intell. 35(6), 1397–1409 (2013). Scholar
  2. 2.
    Tierney, S., Gao, J., Guo, Y.: Affinity pansharpening and image fusion. In: 2014 International Conference on Digital lmage Computing: Techniques and Applications (DlCTA), pp. 1–8 (2014)Google Scholar
  3. 3.
    Bougleux, S., Elmoataz, A.: Image smoothing and segmentation by graph regularization. In: Bebis G., Boyle R., Koracin D., Parvin B. (eds.) Advances in Visual Computing. ISVC, Lecture Notes in Computer Science, vol. 3804. Springer, Berlin, Heidelberg (2005)CrossRefGoogle Scholar
  4. 4.
    Dravida, S., Woods, J., Shen, W.: A comparison of image filtering algorithms. In: IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP ’84) (1984)Google Scholar
  5. 5.
    Thivakaran, T.K., Chandrasekaran, R.M.: Nonlinear filter based image denoising using AMF approach. Int. J. Comput. Sci. Inf. Secur. 7(2) (2010)Google Scholar
  6. 6.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Prentice Hall, Upper Saddle River (2008)Google Scholar
  7. 7.
    Clementel, E., Vandenberghe, S., Karp, J.S., Surti, S.: Comparison of image signal-to-noise ratio and noise equivalent counts in time-of-flight PET. In: IEEE Nuclear Science Symposium and Medical Imaging Conference. KnoxvilleGoogle Scholar
  8. 8.
    Hardie, R.C., Barner, K.E., Sarhan, A.: Selection filters for signal restoration. In: Proceedings of the IEEE 1994 National Aerospace and Electronics Conference (NAECON 1994), vol. 2, pp. 827–834 (1994)Google Scholar
  9. 9.
    Eslahi, N., Mahdavinataj, H., Aghagolzadeh, A.: Mixed Gaussian-impulse noise removal from highly corrupted images via adaptive local and nonlocal statistical priors. In: 2015 9th Iranian Conference on Machine Vision and Image Processing (MVIP), pp. 70–75 (2015). ISSN 2166-6784Google Scholar
  10. 10.
    Song, D.-B., Zhang, J.-W., Zhou, J.: Case study for graph signal denoising by graph structure similarity. In: 2017 2nd International Conference on Image Vision and Computing (ICIVC), pp. 847–851 (2017)Google Scholar

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© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Chaitanya Bharathi Institute of TechnologyHyderabadIndia

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