Hybrid fuzzy and support vector machine based blur detection technique

  • Authors

    • Sukhamrit Kaur
    • Dr. Vijay Kumar Banga
    2018-09-22
    https://doi.org/10.14419/ijet.v7i4.5.21164
  • Fuzzy Approach, Gaussian Blur, Image Restoration, Out of Focus Blur, Support Vector Machine (SVM).

  • Abstract

    The main objective of our research work is to acquire good quality of an image which is blurred. Therefore, in this research our effort is to propose an advanced algorithm to improve the quality of an image by eliminate the blur in an efficient manner. In this paper, two types of blurred images (i.e., Gaussian blur and out of focus) are used. Deblurring techniques are mostly used to eradicate the blur of an image using different methods & parameters. To reimburse blur different types of methods like algorithm, filtering techniques, fuzzy based approach, support vector machine are used. Blur detection methods are used to eradicate the blur from a blurred section of an image which is caused by the out of focus blur and Gaussian blur.

     

     

  • References

    1. [1] Anju Dahiya, R.B.Dubey,“The Comparative Studing of Deblurring Techniques†Indian Journal of Applied Research ,Vol 5 ,pp.2249- 555X, 2015

      [2] Aizenberg I., Moraga C., “Multilayer Feedforward Neural Network based on multi-Valued Neuron (MLMVN) and a Backpropagation Learning Algorithmâ€. Springer, Berlin Heidelberg New York, Springer pp. 169-183, 2007.

      [3] Joao P.A. Oliveira, Mario A.T.F igueiredo, and Jose M.Bioucas-Dias, “Blind Estimation of Motion Blur Parameters for Image Deconvolution, Fundacao Para a Ciencia Technologic, under project Lisboa, PORTUGAL, pp. 1049-1058.

      [5] S. H. Umale, and A. M. Sahu. "A Review on Various Techniques for Image Debluring", International Journal of Computer Science and Mobile Computing, Vol.3 Issue.4, PP. 263-268, April- 2014.

      [6] X. Lu, X.Li. and L.Mou, ―Semi-Supervised multitask learning for scene recognition,‖ IEEE Trans. Cybernetics, vol. 45, no. 9, pp. 1967-1976, 2015.

      [7] I. M. El-Henawy,A. E. Amin, Kareem Ahmed, Hadeer Adel,“A Comparative Study On Image Deblurring Techniquesâ€, International Journal of Advances in Computer Science and Technology (IJACST), Vol.3 , No.12, Pages : 01-08.

      [8] Roy, A., Singha, J., Devi, S. S., & Laskar, R. H. (2016). Impulse noise removal using SVM classification based fuzzy filter from gray scale images. Signal Processing, 128, 262-273.

      [9] Dong yang Long, Huang, Yanping, Wei Lu, and Wei Sun. "Improved DCT-based detection of copy-move forgery in images." Forensic science international 206, no. 1 (2011): 178-184.

      [10] H. Lee, C. Kim, “Blurred Image Region Detection and Segmentation,†IEEE International Conference on Image Processing, pp. 4427-4431, 2014.

      [11] Lazrus, Anish, Siddhartha Choubey, and G. R. Sinha. "An efficient method of vehicle number plate detection and recognition." International journal of machine intelligence 3.3 (2011): 134-137.

      [12] Sinha, G. R., and Neha Agrawal. "Fuzzy based Image Enhancement Method." IJCA, 2015.

      [13] Yasmin, M., Sharif, M., & Mohsen, S. (2013). Survey paper on diagnosis of breast cancer using image-processing techniques. Research Journal of Recent Sciences ISSN, 2277, 2502.

      [14] Harvinder Kaur Garg, Rishu and Preeti Gupta. "Survey on multi- focus image fusion algorithms." Engineering and Computational Sciences (RAECS), 2014 Recent Advances in. IEEE, 2014.

      [15] Jaffar, M. Arfan, Ayyaz Hussain, and Anwar Majid Mirza. "Fuzzy entropy based optimization of clusters for the segmentation of lungs in CT scanned images." Knowledge and Information Systems 24.1 (2010): 91-111.

      [16] Clampitt, Joseph, Bhuvnesh Jain, and Carles Sánchez. "Clustering and bias measurements of SDSS voids." Monthly Notices of the Royal Astronomical Society 456, no. 4 (2016): 4425-4431.

      [17] Suryan, Sachin. "A Review on Extracting DEBLUR Image Using Fuzzy Logic Approach from Impulse Noise." (2017).

      [18] Suryan, Sachin. "Robust Technique for Extracting Deblur Image Using Fuzzy Logic Approach from Impulse Noise." (2017).

      [19] Dong Yang, Shiyin Qin, “Restoration of Degraded Image with Partial Blurred Regions Based on Blur Detection and Classificationâ€, IEEE 2015.

      [20] Shengyang Dai, Ying Wu, “Removing Partial Blur in a Single Imageâ€, ACM Trans. On Graphics (Proc. SIGGRAPH), 2007.

  • Downloads

  • How to Cite

    Kaur, S., & Vijay Kumar Banga, D. (2018). Hybrid fuzzy and support vector machine based blur detection technique. International Journal of Engineering & Technology, 7(4.5), 591-595. https://doi.org/10.14419/ijet.v7i4.5.21164

    Received date: 2018-10-07

    Accepted date: 2018-10-07

    Published date: 2018-09-22