Journal Title : International Journal of Modern Trends in Engineering and Science


Author’s Name : M Santhiya | G Saranya  unnamed

Volume 03 Issue 10 2016

ISSN no:  2348-3121

Page no: 51-55

Abstract – To improve the visual quality of blur images, deblurring techniques are desired, which also play an important role in character recognition and image understanding. The problem of recovering the clear scene text by exploiting the text field characteristics. A series of text-specific multiscale dictionaries (TMD) and a natural scene dictionary is learned for separately modeling the priors on the text and non-text fields. The TMD-based text field reconstruction helps to deal with the different scales of strings in a blurry image effectively. Using functional form kernel method to estimate the kernel. Because the kernel estimation is an important part in deblurring. Mostly the deblurring result depends upon the kernels. Dictionary learning allows more flexible modeling with respect to the text field property, and the combination with the function form kernel method is more appropriate in real situations. Experimental results show that the proposed method achieves the deblurring results with better visual quality than the state-of-the-art methods.   

Keywords— Deblurring; Kernel; Scence Text


  1. C. Yi, X. Yang, and Y. Tian, “Feature representations for scene text character recognition: A comparative study,” in Proc. 12th ICDAR,Aug. 2013, pp. 907–911.
  2. J.-F. Cai, H. Ji, C. Liu, and Z. Shen, “Blind motion deblurring from a single image using sparse approximation,” in Proc. IEEE Conf. CVPR,Jun. 2009, pp. 104–111.
  3. R. Fergus, B. Singh, A. Hertzmann, S. T. Roweis, and W. T. Freeman,“Removing camera shake from a single photograph,” ACM Trans.Graph., vol. 25, no. 3, pp. 787–794, 2006.
  4. Levin, Y. Weiss, F. Durand, and W. T. Freeman, “Understanding and evaluating blind deconvolution algorithms,” in Proc. IEEE Conf. CVPR,Jun. 2009, pp. 1964–1971.
  5. Q. Shan, J. Jia, and A. Agarwala, “High-quality motion deblurring from a single image,” ACM Trans. Graph., vol. 27, no. 3, 2008, Art. ID 73.
  6. L. Xu and J. Jia, “Two-phase kernel estimation for robust motion deblurring,” in Proc. 11th ECCV, 2010, pp. 157–170.
  7. L. Zhong, S. Cho, D. Metaxas, S. Paris, and J. Wang, “Handling noise in single image deblurring using directional filters,” in Proc. IEEE Conf. CVPR, Jun. 2013, pp. 612–619.
  8. L. Xu, S. Zheng, and J. Jia, “Unnatural L0 sparse representation for natural image deblurring,” in Proc. IEEE Conf. CVPR, Jun. 2013,pp. 1107–1114.
  9. H. Madero-Orozco, P. Ruiz, J. Mateos, R. Molina, and K. Katsaggelos, “Image deblurring combining poisson singular integral and total variation prior models,” in Proc. 21st EUSIPCO,Sep. 2013, pp. 1–5.
  10. H. Ji and K. Wang, “A two-stage approach to blind spatially-varying motion deblurring,” in Proc. IEEE Conf. CVPR, Jun. 2012, pp. 73–80.
  11. X. Chen, X. He, J. Yang, and Q. Wu, “An effective document image deblurring algorithm,” in Proc. IEEE Conf. CVPR, Jun. 2011,pp. 369–376.
  12. H. Cho, J. Wang, and S. Lee, “Text image deblurring using text-specific properties,” in Proc. 12th ECCV, 2012, pp. 524–537.
  13. Epshtein, E. Ofek, and Y. Wexler, “Detecting text in natural scenes with stroke width transform,” in Proc. IEEE Conf. CVPR, Jun. 2010,pp. 2963–2970.
  14. Yi and Y. Tian, “Text string detection from natural scenes by structure-based partition and grouping,” IEEE Trans. Image Process.vol. 20, no. 9, pp. 2594–2605, Sep. 2011.
  15. Krishnan, T. Tay, and R. Fergus, “Blind deconvolution using a normalized sparsity measure,” in Proc. IEEE Conf. CVPR, Jun. 2011,pp. 233–240.
  16. J. Jia, “Single image motion deblurring using transparency,” in Proc.IEEE Conf. CVPR, Jun. 2007, pp. 1–8.
  17. J.-F. Cai, H. Ji, C. Liu, and Z. Shen, “Framelet-based blind motion deblurring from a single image,” IEEE Trans. Image Process., vol. 21,no. 2, pp. 562–572, Feb. 2012.
  18. L. Yuan, J. Sun, L. Quan, and H.-Y. Shum, “Image deblurring with blurred/noisy image pairs,” ACM Trans. Graph., vol. 26, no. 3, 2007,Art. ID 1.
  19. M. Elad and M. Aharon, “Image denoising via sparse and redundant representations over learned dictionaries,” IEEE Trans. Image Process.,vol. 15, no. 12, pp. 3736–3745, Dec. 2006.
  20. M. Elad, M. A. T. Figueiredo, and Y. Ma, “On the role of sparse and redundant representations in image processing,” Proc. IEEE, vol. 98,no. 6, pp. 972–982, Jun. 2010.ECCV, 2010, pp. 171–184.