a comparative study and analysis of image restoration techniques using different images formats

R.Navaneethakrishnan,

Published in International Journal of Advanced Research in Computer Science Engineering and Information Technology

ISSN: 2321-3337          Impact Factor:1.521         Volume:1         Issue:3         Year: 08 January,2014         Pages:131-137

International Journal of Advanced Research in Computer Science Engineering and Information Technology

Abstract

Image Restoration is a field of Image Processing which deals with recovering an original and sharp image from a degraded image using a mathematical degradation and restoration model. This study focuses on restoration of degraded images which have been blurred by known or unknown degradation function. On the basis of knowledge of degradation function image restoration techniques can be divided into two categories: blind and non-blind techniques. Three different image formats viz..jpg(Joint Photographic Experts Group), .png(Portable Network Graphics) and .tif(Tag Index Format) are considered for analyzing the various image restoration techniques like Deconvolution using Lucy Richardson Algorithm (DLR), Deconvolution using Weiner Filter (DWF), Deconvolution using Regularized Filter (DRF) and Blind Image Deconvolution Algorithm (BID).The analysis is done on the basis of various performance metrics like PSNR(Peak Signal to Noise Ratio), MSE(Mean Square Error) , RMSE( Root Mean Square Error).

Kewords

Lucy Richardson Algorithm, Weiner Filter, Regularized Filter, Blind Image Deconvolution, Gaussian Blur, Point Spread Function, PSNR, MSE, RMSE.

Reference

[1] D. Kundur and D. Hatzinakos, “Blind image deconvolution,”IEEE Signal Processing Magazine, pp. 43-64, 1996. [2] D. Kundur and D. Hatzinakos , “A novel blind deconvolution scheme for image restoration using recursive filtering,” IEEE Trans. on Signal Processing, vol. 46, no. 2, pp. 375- 390, 1998. [3] K. H. Yap and L. Guan, “A computational reinforced learning scheme to blind image deconvolution , ” IEEE Transactions on Evolutionary Computation, vol. 6, no. 1, pp. 2-15, 2002. [4] K. H. Yap, L. Guan, and W. Liu, “A recursive soft-decision approach to blind image deconvolution,” IEEE Trans. on Signal Processing, vol. 51,no. 2, pp. 515-526, 2003. [5] Dong-Dong Cao, Ping Guo, “Blind image restoration based on wavelet analysis,” IEEE, Machine Learning and Cybernetics, pp.-4977-4982,2005. [6] Dong-Dong Cao, Ping Guo, “Blind image restoration based on wavelet analysis,” IEEE, Machine Learning and Cybernetics, pp.5977-5982, 2005 . [7] Wang Shoujue; Cao Yu; Huang Yi, “A novel Image Restoration Approach Based on Point Location in High-dimensional Space Geometry”,IEEE, Neural networks and Brain, pp. 301 - 305,2005. [8] Zhijun Zhao; Blahut, R.E, “Blind and nonblind nonnegative impulse response ISI channel demodulation using the Richardson-Lucy Algorithm,” IEEE, Signal Processing and Information Technology, pp.445 – 450,2005. [9]P. Campisi and K. Egiazarian, “Blind image deconvolution theory and applications , CRC Press,2006. [10] Chongliang Zhong; Jinbao Fu; Yalin Ding, “Image motion compensation for a certain aviation camera based on Lucy Richardson Algorithm,” IEEE, Electronics and Optoelectronics ,pp.41-144,2011. [12]Ranipa, K.R.; Joshi, M.V, “A practical app roach for depth and image restoration,” IEEE ,Machine Learning for Signal Processing(MLSP),pp.1-6,2011. [13]Ramya, S.; Mercy Christial, T, “Restoration of Blurred Images using Blind Deconvolution Algorithm,” IEEE, on Emerging Trends in Electrical and Computer Technology(ICETECT), pp.496 - 499,2011. [14]Chong Yi; Shimamura, T, “A blind image deconvolution method based on noise variance estimation and blur type reorganization,” IEEE,Intelligent Signal processing and Communications System,pp.1- 6,2011. [15]Corbalan, L.; Massa, G.O.; Russo, C.;Lanzarini, L.; De Giusti, A., “Image recovery using a new nonlinear adaptive filter based on neural networks,” IEEE ,Information Technology Interfaces , pp. 355 – 360,2006.