Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

(1.29MB)
This is an Open Access publication published under CSC-OpenAccess Policy.
Publications from CSC-OpenAccess Library are being accessed from over 74 countries worldwide.
Performance Analysis and Optimization of Nonlinear Image Restoration Techniques in Spatial Domain
Anil L. Wanare, Dilip D. Shah
Pages - 123 - 137     |    Revised - 15-03-2012     |    Published - 16-04-2012
Volume - 6   Issue - 2    |    Publication Date - April 2012  Table of Contents
MORE INFORMATION
KEYWORDS
Nonlinear image restoration, Additive noise, Monochrome image denoising, Median with weight, Correlation distortion metrics
ABSTRACT
Abstract: This paper is concerned with critical performance analysis of spatial nonlinear restoration techniques for continuous tone images from various fields (Medical images, Natural images, and others images).The performance of the nonlinear restoration methods is provided with possible combination of various additive noises and images from diversified fields. Efficiency of nonlinear restoration techniques according to difference distortion and correlation distortion metrics is computed.Tests performed on monochrome images, with various synthetic and real-life degradations, without and with noise, in single frame scenarios, showed good results, both in subjective terms and in terms of the increase of signal to noise ratio(ISNR) measure. The comparison of the present approach with previous individual methods in terms of mean square error, peak signal-to-noise ratio, and normalised absolute error is also provided. In comparisons with other state of art methods, our approach yields better to optimization, and shows to be applicable to a much wider range of noises. We discuss how experimental results are useful to guide to select the effective combination. Promising performance analysed through computer simulation and compared to give critical analysis.
CITED BY (4)  
1 Bannsal, R. (2014). Computationally intelligent watermarking for securing fingerprint images.
2 Bedi, P., Bansal, R., & Sehgal, P. (2013). Using PSO in a spatial domain based image hiding scheme with distortion tolerance. Computers & Electrical Engineering, 39(2), 640-654.
3 Hatwar, S. K., Wanare, A. L., Shah, D. D., & Helonde, J. B. (2013). Performance Analysis and Automatic Selection of Restoration Techniques for Diversified Field Images. Digital Image Processing, 5(9), 422-427.
4 Shah, P. D., & Wanare, A. L. Semi Blind Restoration of Diversified Field Images.
1 Google Scholar 
2 CiteSeerX 
3 refSeek 
4 Scribd 
5 SlideShare 
6 PdfSR 
1 T.Weissman, E.Ordentlich, G.Seroussi, S.Verdi, and M.J.Weinberger, “Universal discrete denoising: known channel,” IEEE trans. Inform. Theory, Jan. 2005.
2 T.Weissman, E.Ordentlich, G.Seroussi, S.Verdi, and M.J.Weinberger, “A discrete universal denoiser and its application to binary image,” in proc. Of ICIP’03, Barcelona, Sept. 2003.
3 A.Buades, B.Coll, and J.M.Morel, “A new of image denoising algorithms, with a new one,” SIAM of multiscale modeling and simulation (MMS)
4 H.Takeda, S.Farsiu, and P.Milanfar, “Kernel regression for image processing and reconstruction,” IEEE Trans. Image Processing., Vol.16, no. 02, pp. 349-366, Feb. 2007.
5 Ignacio Ramirez, M. Weinberger, “The dude frame work for continuous tone image denoising, ” IEEE Trans.2005.
6 R.H.Chan, C.Ho, and M.Nikolova, “salt & pepper noise removal by noising detector and detail persening regulization,” IEEE Transon Image Processing, available at http:/www.math.cunk.edu.hk /impulse.
7 L.Yin, et.al. “Weighted median Filter: Atutorial,” IEEE Trans on circuit and system II, Vol.43, Issue03, pp.157-192, March 1996.
8 A.Buades, B.Cell, and J.Morel, “ A non-local algorithm for image denoising,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, Washington, DC, Oct. 2005, Vol. 02, pp. 60-65.
9 I.Pitas, A.N.Venetsanopoulos, “Nonlinear filters principle and application,” Kluwrer Academic Publishers, Dodresht, 1990.
10 J. Astola and P. Kuosmanen, Fundamental of Nonlinear Filtering, Boca Raton, CRC Press, 1997.
11 A.Asensio, A.Lopez Ariste, “Image Reconstruction with Analytical Point Spread Function,” Astronomy & Astrophysics Manuscript, pp. 1-10, April 20, 2010.
12 Y.E. Ioannidis, "The History of Histograms (Abridged)," Proc. Int'l Conf. Very Large Data Bases, 2003.
13 S. Guha, N. Koudas, and K. Shim, "Approximation and Streaming Algorithms for Histogram Construction Problems," ACM Trans. Database Systems, vol. 31, no. 1, pp. 396-438, 2006.
14 T. Loupos, W. N. McDicken and P. L. Allan, “An adaptative weighted median filter for speckle suppression in medical ultrasonic images,” IEEE Trans. Circuits Syst., vol. 36, Jan. 1989.
15 L. Yin, R. Yang, M. Gabbouj, and Y. Neuvo, “Weighted median filters: A tutorial,” IEEE Trans. Circuits and Syst. II: Analog and Digital Signal Processing, vol. 43, pp. 157-192, Mar. 1996.
16 M.D.grassberg and S.K. nayar, “modeling the space response function of camera,” IEEE trans., PAMI, vol. 26, Oct. 2004.
17 D.Donoho, “ denoising by soft thresholding ,” IEEE, Trans IT, vol. 3, 1995.
18 D.Martin, C. Fowlkes, D. Tal and J. Malik, “ A database of natural images and its applications to evaluating segmentation algorithms and measuring ecological statistics”, proc. IEEE conf. Computer Vision, Volume 2, pp., 416-423, July. 2001.
19 A. stefano, P. white, “ training method for image noise level estimation on wavelet componenet,” JASP, vol. 16, 2004.
20 J. portilla, “ blind denoising through noise covarience estimation using gaussian scale mixture in wavelet domain,” IEEE, conf Image processing, pp1217-1220, 2004.
Mr. Anil L. Wanare
G.H.Raisoni Institute of Engg. & Technology - India
a.wanare@rediffmail.com
Dr. Dilip D. Shah
GHRCEM,PUNE UNIVERSITY - India