Context adaptive image denoising through modeling of curvelet domain statistics

Linda Tessens, Alexandra Pizurica, Alin Alecu, Adrian Munteanu, Wilfried Philips

Research output: Contribution to journalArticle

26 Citations (Scopus)


In this paper, we perform a statistical analysis of curvelet coefficients, distinguishing between two classes of coefficients: those that contain a significant noise-free component, which we call "signal of interest", and those that do not. By investigating the marginal statistics, we develop a prior model for curvelet coefficients. The analysis of the joint intra- and inter-band statistics
enables us to develop an appropriate local spatial activity indicator for curvelets. Finally, based on our findings, we present a novel denoising method, inspired by a recent wavelet domain method ProbShrink. The new method outperforms its wavelet-based counterpart and produces results that are close to those of state-of-the-art denoisers.
Original languageEnglish
Article number033021
Pages (from-to)1-17
Number of pages17
JournalJournal of Electronic Imaging
Issue number3
Publication statusPublished - Sep 2008


  • Curvelets
  • Image statistics
  • image denoising

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