Browse > Article

Space-Frequency Adaptive Image Restoration Using Vaguelette-Wavelet Decomposition  

Jun, Sin-Young (Dept. of Image Engineering, Graduate School of Advanced Image Science, Multimedia, and Film, Chung-Ang University)
Lee, Eun-Sung (Dept. of Image Engineering, Graduate School of Advanced Image Science, Multimedia, and Film, Chung-Ang University)
Kim, Sang-Jin (Dept. of Image Engineering, Graduate School of Advanced Image Science, Multimedia, and Film, Chung-Ang University)
Paik, Joon-Ki (Dept. of Image Engineering, Graduate School of Advanced Image Science, Multimedia, and Film, Chung-Ang University)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SP / v.46, no.6, 2009 , pp. 112-122 More about this Journal
Abstract
In this paper, we present a novel space-frequency adaptive image restoration approach using vaguelette-wavelet decomposition (VWD). The proposed algorithm classifies a degraded image into flat and edge regions by using spatial information of the wavelet coefficient. For reducing the noise we perform an adaptive wavelet shrinkage process. At edge region candidates, we adopt entropy approach for estimating the noise and remove it by using relative between sub-bands. After shrinking wavelet coefficients process, we restore the degraded image using the VWD. The proposed algorithm can reduce the noise without affecting the sharpness details. Based on the experimental results, the proposed algorithm efficiently proved to be able to restore the degraded image while preserving details.
Keywords
image restoration; wavelet transform; entropy; vaguelette-wavelet decomposition; wavelet shrinkage;
Citations & Related Records
연도 인용수 순위
  • Reference
1 H. Zheng, C. Xiaoqing, and L. Guoming, "Wavelet entropy measure definition and its application for transmission line fault detection and identification," International Conference on Power System Technology 2006, pp. 1 – 6, October 2006
2 D. Donoho, "Nonlinear solution of linear inverse problems by wavelet-vaguelette decomposition," Applied Computational and Harmonic Analysis, vol. 2, pp. 101-26, 1995   DOI   ScienceOn
3 S. Mallat, A Wavelet Tour of Signal Processing, New York: Academic Press, 1998.
4 R. Neelamani, H. Choi, and R. Baraniuk, "ForWaRD: Fourier-wavelet regularized deconvolution for ill-conditioned systems," IEEE Trans. Signal Processing, vol. 52, no. 2, pp. 418-433, February, 2004   DOI   ScienceOn
5 J. Paik, "New application are(839)as of image restoration: a perspective," Proc. Asia-Pacific Conference on Communication, vol. 2, pp. 775 – 778, August, 1993
6 R. Gonzalez and R. Woods, Digital image processing, 2nd ed., Prentice-Hall, 2001
7 M. Banham, N. Galatsanos, H. Gonzalez, and A. Katsaggelos," Multichannel restoration of single channel images using a wavelet-based subband decomposition," IEEE Trans. Image Processing, vol. 3, pp.821-833, November 1994   DOI   ScienceOn
8 A. Jain, Fundamental of Digital Image Processing, Prentice-Hall, 1989
9 I. Daubechies, Ten lectures on wavelets, Society for Industrial and Applied Mathmatics, 1992
10 백준기,"첨단 영상 미디어 서비스와 영상복원 기술," 대한전자공학회, 전자공학회지 제 23권 제6호, pp. 636 – 647, 1996년 6월
11 E. Balster, Y. Zheng, and R. Ewing, "Feature-based wavelet shrinkage algorithm for image denoising," IEEE Trans. Image Processing, vol. 14, no. 12, pp. 2024-2039, December 2005   DOI   ScienceOn
12 F. Abramovich and B. Silverman, "Wavelet decomposition approaches to statistical inverse problems," Biometrika, vol. 85, no. 1, pp. 115-129, 1998   DOI   ScienceOn
13 M. Banham and A. Katsaggelos, "Digital image restoration," IEEE Signal Processing Magazine, vol. 14, no. 2, pp. 24-41, March 1997   DOI   ScienceOn