Research Article | OPEN ACCESS
Strategical Report on Removal of Blurring in an Original Image Using Non Linear Median Filter Technique
1G. Sasikala and 2K. Siddappanaidu
1Department of ECE, Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology, Vel Tech Dr. RR & Dr. SR Technical University
2School of Electrical Engineering, Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology, Avadi, Chennai-62, Tamil Nadu, India
Research Journal of Applied Sciences, Engineering and Technology 2014 18:1994-2000
Received: September 03, 2014 | Accepted: October 12, 2014 | Published: November 15, 2014
Abstract
In real world application, the original signal, image, motion pictures or any another transform the removing of blur is a still challenging issue for the researchers. There have been several published algorithm, techniques and new methodologies. But each approach has its own assumptions, advantages and limitations. This study explores a technique of how image enhancement and denoising are useful in motion recording and storing for various applications such as digital still camera, video mail camera, video conferencing camera, surveillance camera, web camera, wireless camera, toy camera and digital video recorder.
Keywords:
Gaussian noise, impulse noise, MATLAB software, spatial linear and non linear filter,
References
-
Afonso, M., J. Bioucas-Dias and M. Figueiredo, 2010. Fast image recovery using variable splitting and constrained optimization. IEEE T. Image Process., 19(9): 2345-2356.
CrossRef PMid:20378469
-
Bioucas-Dias, J. and M. Igueiredo, 2010. Multiplicate noise removal using variable splitting and constrained optimization. IEEE T. Image Process., 19(7): 1720-1730.
CrossRef PMid:20215071
-
Dong, W., L. Zhang, G. Shi and X. Wu, 2011. Image deblurring and super resolution by adaptive sparse domain selection and adaptive regularization. IEEE T. Image Process., 20(7): 1838-1857.
CrossRef PMid:21278019
-
Gonzalez, R.C. and R.E. Woods, 2012. Digital Image Processing. Pearson Education Inc., NY.
-
Joshi, N., C.K. Zitnick, R. Szeliski and D. Kriegman, 2009. Image deblurring and denoising using color prior. Proceeding of the International Conference on Computer Vision Pattern Recognition, pp: 1550-1557.
CrossRef
-
Katkovnik, V., A. Foi, K. Egiazarian and J. Astola, 2010. From local kernel to nonlocal multiple model image denoising. Int. J. Comput. Vision, 86(1): 1-32.
CrossRef
-
Yan, M., 2013. Restoration of images corrupted by impulse noise and mixed Gaussian impulse noise using blind in painting. Siam J. Imag. Sci., 6(3): 1227-1245.
CrossRef
-
Zhang, L., W. Dong, D. Zhang and G. Shi, 2010. Two stage image donoising by principal component analysis and local pixel grouping. Pattern Recogn., 43(4): 1531-1549.
CrossRef
-
Zho, W., L. Zhang, C. Song and D. Zhang, 2013. Texture enhanced image denoising via gradient histogram preservation. Proceeding of the International Conference on Computer Vision Pattern Recognition (CVPR, 2013), pp: 1203-1210.
-
Zitnick, C.L. and D Parikh, 2012. The role of image understanding in contour detection. Proceeding of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR, 2012), pp: 622-629.
CrossRef
Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Copyright
The authors have no competing interests.
|
|
|
ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
|
Information |
|
|
|
Sales & Services |
|
|
|