Open Access Open Access  Restricted Access Subscription or Fee Access

Restoring Original Image based on Relation between Neighborhood Pixels

S. Manimurugan, N. Praveen Kumar

Abstract


In this paper, we introduce a restoring the original image that was based on image properties when defining the interaction between pixels in the filtering process. After this, image structure is identified using properties like intensity level and is implemented according to the proposed technique. Using the generation of red points in this technique. In this context, a red points can be generated on to the image to find the similar neighborhood pixels like this different number of points can be generated on to image in order to provide interaction between pixels and identifying similar number of pixels and the weighted probability density function can be calculated for each group of similar pixels that for each window by using this information an original image can be restored. Calculating the average for the obtained weighted probability density function values can gives original restored value

Keywords


Gaussian Noise, Weighted Probability Density Function, Mean, Image Restoration

Full Text:

PDF

References


N. Azzabou, N. Paragios, F. Guichard, and F. Cao, “Variable bandwidth image denoising image-based noise models,” in Proc. IEEE Int. Conf. Computer Vision and Pattern Recognition, 2007, pp. 1–7.

N. Bergman, “Recursive Bayesian Estimation: Navigation and Tracking Applications,” Ph.D. dissertation, Linkoping Univ.Linkoping, Sweden, 1999.

A. Buades, B. Coll, and J.M.Morel, “A non-local algorithm for image de-noising,” in Proc. IEEE Int. Conf. Computer Vision and Pattern Recognition, 2005, pp. 60–65.

M. Mahmoudi and G. Sapiro, “Fast image and video de-noising via nonlocal means of the similar neighbourhoods,” IEEE Signal Process. Lett., vol.12, pp. 839–842, 2005.

S. Mallat, “A theory for multi-resolution signal decomposition: The wavelet representation,” IEEE TransPattern Anal. Mach. Intell., vol11, pp. 674–693,1989.

P. Perona and J. Malik, “Scale space and edge detection using anisotropic diffusion,” IEEE Trans. Pattern Anal. Mach. Intell., vol.12, pp. 629–639, 1990.

J. Polzehl and V. Spokoiny, “Adaptive weights smoothing with applications to image restoration,” J. Roy. Statist. Soc. B, vol. 62, pp. 335–354,2000.

L. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal,” Phys. D, vol. 60, pp. 259–268, 1992.

B. Smolka and K. Wojciechowski, “Random walk approach to image enhancement,” Signal Process., vol. 81, pp. 465–482, 2001.

C. Tomasi and R. Manduchi, “Bilateral filtering for gray and colour images,” in Proc. Int Conf. Computer Vision, 1998, pp. 839–846.

Noura Azzabou, Nikos Paragios, Frédéric Guichard,” Image reconstruction using particle filtere and Multiple hypothesis testing ,” IEEE Transactions On Image Processing, Vol. 19, No. 5, May 2010

L.Vese and S. Osher, “Modeling textures with total variation minimization and oscillating patterns in image processing,” J. Sci. Comput

A. Efros and T. Leung, “Texture synthesis by non-parametric sampling,” in Proc. Int. Conf. Computer Vision, 1999, pp. 1033–1038.

K. Egiazarian, V. Katkovnik, and J. Astola, “Adaptive window size image denoising based on ICI rule,” in Proc. IEEE Int. Conf. Acoustic,Speech and Signal Processing, 2001, pp. 1869–1872.vol. 19, pp. 553–572, 2003.


Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.