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

M. Kivanç Mihçak, Igor Kozintsev, Kannan Ramchandran, Pierre Moulin

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)300-303
Number of pages4
JournalIEEE Signal Processing Letters
Issue number12
StatePublished - Dec 1999

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics


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