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Call for Papers - February 2017 Edition

International Journal of Computer Applications Technology and Research (IJCATR) call for research paper for Volume 6 Issue 2 February 2017 Edition. Submit manuscript to editor@ijcat.com. Last date of manuscript submission is January 31, 2017.

 

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International Journal of Computer Applications Technology and Research (IJCATR)

Volume 2 Issue 3 May-June 2013

Literature Survey on Image Deblurring Techniques

Minu Poulose

10.7753/IJCATR0203.1014

    
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Keywords:Face recognition, blur kernel, Point Spread Function (PSF), Local Phase Quantization, Linear Ternary Patterns,L1 norm

Abstract References BibText Highlights

       Image restoration and recognition has been of great importance nowadays. Face recognition becomes difficult when it comes to blurred and poorly illuminated images and it is here face recognition and restoration come to picture. There have been many methods that were proposed in this regard and in this paper we will examine different methods and technologies discussed so far. The merits and demerits of different methods are discussed in this concern .

  1. Nishiyama, M., Hadid,A.,Takeshima,H., Shotton, J., Kozakaya, T. and Yamaguchi,O. 2011 Facial deblur inference using subspace analysis for recognition of blurred faces, IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 4. Kundur, D. and Hatzinakos, D. Blind image deconvolution revisited.
  2. Hu, H. and Haan, G. 2006 Low cost robust blur estimator Proc. IEEE Int’l Conf. Image Processing, pp. 617 – 620
  3. Yuan, L., Sun, J., Quan, L. and Shum, H.Y. 2007 Image deblurring with blurred/noisy image pairs ACM Trans. Graphics, vol. 26, no. 3, pp. 1
  4. Levin, A. 2006 Blind motion deblurring using image statistics in Proc. Adv. Neural Inform. Process. Syst. Conf pp. 841–848.
  5. Xiaoyang, T. and Bill ,T. 2007 Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions in AMFG 2007, LNCS 4778, pp. 168–182
  6. Ojansivu, V. and Heikkilä, J. 2008 Blur insensitive texture classification using local phase quantization in Proc. 3rd Int. Conf. Image Signal Process., pp. 236–243.
  7. Chen, T., Yin, W., Zhou, X., Comaniciu, D. and Huang, T. 2006 Total variation models for variable lighting face recognition. IEEE TPAMI 28(9), 1519–1524

@article{minu02031014,
title = "Test Driven Development with Continuous Integration: A Literature Review ;,
journal = "International Journal of Computer Applications Technology and Research ",
volume = "2",
number = "3",
pages = "286 - 288",
year = "2013",
author = " Minu Poulose ",
}

In this paper we can incorporate prior knowledge on the type of blur as constraints.
Weight matrix is used in the computation purpose which make it robust to misalignments in the pixels.
Both illumination and blur problems are removed to make the face recognition easier.
Exhibits a stable performance for a wide range of kernel-sizes.