CFP last date
22 April 2024
Reseach Article

Removing Camera Shake using Discrete Cosine Transform

by D. Ramesh Varma, D. Krishna Madhav, K. Rajasekhar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 170 - Number 9
Year of Publication: 2017
Authors: D. Ramesh Varma, D. Krishna Madhav, K. Rajasekhar
10.5120/ijca2017914801

D. Ramesh Varma, D. Krishna Madhav, K. Rajasekhar . Removing Camera Shake using Discrete Cosine Transform. International Journal of Computer Applications. 170, 9 ( Jul 2017), 7-10. DOI=10.5120/ijca2017914801

@article{ 10.5120/ijca2017914801,
author = { D. Ramesh Varma, D. Krishna Madhav, K. Rajasekhar },
title = { Removing Camera Shake using Discrete Cosine Transform },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2017 },
volume = { 170 },
number = { 9 },
month = { Jul },
year = { 2017 },
issn = { 0975-8887 },
pages = { 7-10 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume170/number9/28096-2017914801/ },
doi = { 10.5120/ijca2017914801 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:18:00.608190+05:30
%A D. Ramesh Varma
%A D. Krishna Madhav
%A K. Rajasekhar
%T Removing Camera Shake using Discrete Cosine Transform
%J International Journal of Computer Applications
%@ 0975-8887
%V 170
%N 9
%P 7-10
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Image restoration is one of the burning issues in the field of image processing. Generally, images are corrupted or damaged due to the noise present in the system or due to motion blur while capturing the image. In this paper, a problem of removing blurness in an image which is caused due to camera shake is discussed. The blur Kernel in an image is uneven. Because of this reason, every image in a burst of images is blurred in a different way. In this paper, a new technique is proposed in which burst of images are taken and calculates a weighted average in discrete cosine domain, where the weights depend on their discrete cosine spectrum magnitudes.

References
  1. 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.
  2. J. F.Cai,H.Ji, C.Liu,and Z.Shen,“Blind motion deblurring using multiple images,” J.Comput.Phys., vol. 228, no. 14, pp.5057– 5071,2009.
  3. F. Gavant, L. Alacoque, A. Dupret,and D. David, “A physiological camera shake model for image stabilization systems,” in Proc. IEEE Sensors.
  4. F. Xiao, A.Silverstein, and J. Farrell, “Camera-motion and effective spatial resolution,” in Proc. Int. Congr.Imag. Sci. (ICIS), 2006,pp. 33–36.
  5. Removing Camera Shake via Weighted Fourier Burst Accumulation by Mauricio Delbracio and Guillermo Sapiro.
  6. X..Zhu, F.Šroubek, and P. Milanfar, “Deconvolving PSFs for a better motion deblurring using multiple images” in Proc.IEEE12th Eur.Conf.Comput.Vis. (ECCV), Oct. 2012, pp. 636–647.
  7. M.Delbracio, P.Musé, A.Almansa, and J.-M. Morel, “The non-parametric sub-pixel local point spread function estimation is a well posed problem,” Int. J. Comput. Vis. vol. 96, no. 2, pp. 175–194, 2012.
  8. B. Zitová and J. Flusser, “Image registration methods: A survey,” Image Vis. Comput., vol. 21, no. 11, pp. 977– 1000, 2003.
  9. “A New Weighted Average Filter for Removing Camera Shake” International Journal of Computer Applications (0975 – 8887) Volume 156 – No 9, December 2016
  10. “Image Restoration using 3-Dimensional Discrete Cosine Transform” International Journal of Computer Applications (0975 – 8887) Volume 156 – No 9, December 2016.
Index Terms

Computer Science
Information Sciences

Keywords

Discrete cosine spectrum motion blur camera shake.