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Proceedings Paper

Adaptive filter for noise removal using wavelet transform
Author(s): Chun Hong Yang; G. Su
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Paper Abstract

In this paper, an adaptive noise filter implemented in Wavelet Transform (WT) domain is proposed. This filter smoothes noise while preserving edges as much as possible by taking advantage of different characteristics of signal and noise in WT domain. (1) The shape of signal histograms in WT domain approaches a Gaussian distribution, the mean is zero and the variance increases as scale increases. (2) The white noise in spatial domain remains white in WT domain with variance decreasing proportionally to the scale. (3) Uncorrelated signal and noise in spatial domain remain uncorrelated in WT domain. (4) The signal-to-noise ratio (SNR) increases as scale increases in WT domain. Based on these analyses, we derive a simple form of the 2D Minimum Mean Square Error (MMSE) estimate algorithm in WT domain that is applicable for nonstationary image models. All the nonstationary image statistical parameters needed for the filter can be estimated from the noisy image and no a priori information about the original image is required. A comparison demonstrates that the method in WT domain provides better improvement of SNR and better subjective impression than the same method in spatial domain.

Paper Details

Date Published: 21 April 1995
PDF: 11 pages
Proc. SPIE 2501, Visual Communications and Image Processing '95, (21 April 1995); doi: 10.1117/12.206722
Show Author Affiliations
Chun Hong Yang, National Univ. of Singapore (Singapore)
G. Su, National Univ. of Singapore (Singapore)


Published in SPIE Proceedings Vol. 2501:
Visual Communications and Image Processing '95
Lance T. Wu, Editor(s)

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