Deblurring of Blurred Image By Measuring White Space

Devika Sahu*, Sanjivani Shantaya**
* M.Tech Scholar, Department of Computer Science and Engineering, RITEE, Raipur, India.
** Assistant Professor and Head, Department of Computer Science and Engineering, RITEE, Raipur, India.
Periodicity:July - September'2017
DOI : https://doi.org/10.26634/jip.4.3.13925

Abstract

One of the major problems in the field of photography is a blur. A blur in the image is obtained by the disturbance in the setting of the camera or due to the motion of the things to be captured and noise added to the image. This artifact becomes very crucial nowadays in the field of photography. There are various works already been done by the researchers and a lot of work is still in progress. But, the restoring of the image in its original state are still a big problem. In this paper, the authors propose a method, in which the blur can be removed by using whiteness measurement of the image captured or stored.

Keywords

Defocus Image Deblurring, Blind Image, Non-Blind Image, Spatially Variant Deblurring, Image Deconvolution/ Deblurring, Image Restoration

How to Cite this Article?

Sahu,D. and Shantaiya,S. (2017). Deblurring of Blurred Image By Measuring White Space. i-manager’s Journal on Image Processing, 4(3), 28-31. https://doi.org/10.26634/jip.4.3.13925

References

[1]. Bae, S., & Durand, F. (2007). Defocus magnification. In Computer Graphics Forum (Vol. 26, No. 3, pp. 571-579). Blackwell Publishing Ltd.
[2]. Chan, S. H., & Nguyen, T. Q. (2011). Single image spatially variant out-of-focus blur removal. In Image th Processing (ICIP), 2011 18 IEEE International Conference on (pp. 677-680). IEEE.
[3]. Cheong, H., Chae, E., Lee, E., Jo, G., & Paik, J. (2015). Fast image restoration for spatially varying defocus blur of imaging sensor. Sensors, 15(1), 880-898.
[4]. Dabov, K., Foi, A., Katkovnik, V., & Egiazarian, K. (2008). Image restoration by sparse 3D transform-domain collaborative filtering. In Image Processing: Algorithms and Systems VI (Vol. 6812, p. 681207). International Society for Optics and Photonics.
[5]. Levin, A., Fergus, R., Durand, F., & Freeman, W. T. (2007). Image and depth from a conventional camera with a coded aperture. ACM Transactions on Graphics (TOG), 26(3), 70.
[6]. Mehra, R. (1971). On-line identification of linear dynamic systems with applications to Kalman filtering. IEEE Transactions on Automatic Control, 16(1), 12-21.
[7]. Oliveira, J. P., Figueiredo, M. A., & Bioucas-Dias, J. M. (2014). Parametric blur estimation for blind restoration of natural images: Linear motion and out-of-focus. IEEE Transactions on Image Processing, 23(1), 466-477.
[8]. Shen, C. T., Hwang, W. L., & Pei, S. C. (2012). Spatiallyvar ying out-of-focus image deblurring with L1-2 optimization and a guided blur map. In Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on (pp. 1069-1072). IEEE.
[9]. Tang, C., Hou, C., & Song, Z. (2013). Defocus map estimation from a single image via spectrum contrast. Optics Letters, 38(10), 1706-1708.
[10]. Whyte, O., Sivic, J., Zisserman, A., & Ponce, J. (2012). Non-uniform deblurring for shaken images. International Journal of Computer Vision, 98(2), 168-186.
[11]. Zhou, C., Lin, S., & Nayar, S. K. (2011). Coded aperture pairs for depth from defocus and defocus deblurring. International Journal of Computer Vision, 93(1), 53-72.

Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
USD EUR INR USD-ROW
Pdf 35 35 200 20
Online 35 35 200 15
Pdf & Online 35 35 400 25

If you have access to this article please login to view the article or kindly login to purchase the article
Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.