CFP last date
22 July 2024
Reseach Article

A New Weighted Average Filter for Removing Camera Shake

by M. V. R. V. Prasad, K. Srinivas, G. Prasanna Kumar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 156 - Number 9
Year of Publication: 2016
Authors: M. V. R. V. Prasad, K. Srinivas, G. Prasanna Kumar

M. V. R. V. Prasad, K. Srinivas, G. Prasanna Kumar . A New Weighted Average Filter for Removing Camera Shake. International Journal of Computer Applications. 156, 9 ( Dec 2016), 23-26. DOI=10.5120/ijca2016912546

@article{ 10.5120/ijca2016912546,
author = { M. V. R. V. Prasad, K. Srinivas, G. Prasanna Kumar },
title = { A New Weighted Average Filter for Removing Camera Shake },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2016 },
volume = { 156 },
number = { 9 },
month = { Dec },
year = { 2016 },
issn = { 0975-8887 },
pages = { 23-26 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2016912546 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-07T00:02:10.017831+05:30
%A M. V. R. V. Prasad
%A K. Srinivas
%A G. Prasanna Kumar
%T A New Weighted Average Filter for Removing Camera Shake
%J International Journal of Computer Applications
%@ 0975-8887
%V 156
%N 9
%P 23-26
%D 2016
%I Foundation of Computer Science (FCS), NY, USA

Image blurring is one of the major problems in the field of digital image processing. Generally, camera shake causes blurring. As a result, uneven blur kernel is present in the image which is random in nature. Therefore, every image in the burst is blurred in a different way. Deblurred image can be obtained using single image or multiple images. A clean sharp image is recovered by fusing the group of images without calculating the blurring kernel. In this paper, a new technique called a new weighted average filter is introduced for removing camera shake using single or multiple images. This technique takes a burst of images and calculates a weighted average in the Discrete Wavelet domain, where the weights of images depend on their Discrete Wavelet Spectrum magnitudes.

  1. B.Carignan, J.-F. Daneault, and C.Duval,“ Quantifying the importance of high frequency components on the amplitude of physiological tremor,” Experim. Brain Res.
  2. F. Gavant, L. Alacoque, A. Dupret,and D. David, “A physiological camera shake model for image stabilization systems,” in Proc. IEEE Sensors.
  3. F. Xiao, A. Silverstein, and J. Farrell, “Camera-motion and effective spatial resolution,” in Proc. Int. Congr. Imag. Sci. (ICIS), 2006,pp. 33–36.
  4. 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.
  5. J. Chen, L. Yuan, C.-K.Tang,and L.Quan, “Robust dual motion deblurring,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR),Jun. 2008, pp. 1–8.
  6. F. Sroubek and P. Milanfar, “Robust multichannel blind deconvolution via fast alternating minimization,” IEEE Trans. Image Process., vol. 21,no. 4, pp. 1687–1700, Apr. 2012.
  7. 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.
  8. 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.
  9. N. M. Law, C. D. Mackay, and J. E. Baldwin, “Lucky imaging: High angular resolution imaging in the visible from the ground,” Astron. Astrophys., vol. 446, no. 2, pp. 739–745, 2006.
  10. V. Garrel, O. Guyon, and P. Baudoz, “A highly efficient lucky imaging algorithm: Image synthesis based on Fourier amplitude selection,” Pub.Astron. Soc. Pacific, vol. 124, no. 918, pp. 861–867, 2012.
  11. D.L.Fried, “Probability of getting a lucky short-exposure image through turbulence,” J. Opt. Soc. Amer., vol. 68, no.12, pp. 1651–1657, 1978.
  12. N. Joshi and M. F. Cohen, “Seeing Mt. Rainier: Lucky imaging for multi-image denoising, sharpening, and haze removal,” in Proc. IEEEInt. Conf. Comput. Photogr. (ICCP), Mar. 2010, pp. 1–8.
  13. 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.
  14. B. Zitová and J. Flusser, “Image registration methods: A survey,” Image Vis. Comput., vol. 21, no. 11, pp. 977–1000, 2003.
  15. Removing Camera Shake via Weighted Fourier Burst Accumulation by Mauricio Delbracio and Guillermo Sapiro.
Index Terms

Computer Science
Information Sciences


Blur burst Discrete Wavelet