Low-complexity image denoising based on statistical modeling of wavelet coefficients

We introduce a simple spatially adaptive statistical model for wavelet image coefficients and apply it to image denoising. Our model is inspired by a recent wavelet image compression algorithm, the estimation-quantization (EQ) coder. We model wavelet image coefficients as zero-mean Gaussian random v...

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Bibliographic Details
Published in:IEEE signal processing letters 1999-12, Vol.6 (12), p.300-303
Main Authors: Kivanc Mihcak, M., Kozintsev, I., Ramchandran, K., Moulin, P.
Format: Article
Language:English
Subjects:
Additive white noise
Algorithms
Approximation
AWGN
Correlation
Gaussian
Gaussian noise
Histograms
Image coding
Image compression
Image denoising
Image processing
Mathematical models
Noise reduction
Random variables
Wavelet
Wavelet coefficients
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Summary:We introduce a simple spatially adaptive statistical model for wavelet image coefficients and apply it to image denoising. Our model is inspired by a recent wavelet image compression algorithm, the estimation-quantization (EQ) coder. We model wavelet image coefficients as zero-mean Gaussian random variables with high local correlation. We assume a marginal prior distribution on wavelet coefficients variances and estimate them using an approximate maximum a posteriori probability rule. Then we apply an approximate minimum mean squared error estimation procedure to restore the noisy wavelet image coefficients. Despite the simplicity of our method, both in its concept and implementation, our denoising results are among the best reported in the literature.
ISSN:1070-9908
1558-2361
DOI:10.1109/97.803428