Scientific article
Open access

Image denoising based on the edge-process model

Published inSignal processing, vol. 85, no. 10, p. 1950-1969
Publication date2005

In this paper a novel stochastic image model in the transform domain is presented and its performance in image denoising application is experimentally validated. The proposed model exploits local subband image statistics and is based on geometrical priors. Contrarily to models based on local correlations, or mixture models, the proposed model performs a partition of the image into non-overlapping regions with distinctive statistics. A close form analytical solution of the image denoising problem for an additive white Gaussian noise is derived and its performance bounds are analyzed. Despite being very simple, the proposed stochastic image model provides a number of advantages in comparison to the existing approaches: (a) simplicity of stochastic image modeling; (b) completeness of the model, taking into account multiresolution, spatially adaptive image behavior, geometrical priors and providing an accurate fit to the global image statistics; (c) very low complexity of the algorithm; (d) tractability of the model and of the obtained results due to the closed-form solution and to the existence of analytical performance bounds; (e) extensibility to different transform domains, such as orthogonal, biorthogonal and overcomplete data representations.

  • Denoising
  • Stochastic modeling
  • ML
  • MAP
  • Variance estimation
  • Wavelets
  • Overcomplete expansions
Citation (ISO format)
VOLOSHYNOVSKYY, Svyatoslav, KOVAL, Oleksiy, PUN, Thierry. Image denoising based on the edge-process model. In: Signal processing, 2005, vol. 85, n° 10, p. 1950–1969. doi: 10.1016/j.sigpro.2005.04.007
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Article (Published version)
ISSN of the journal0165-1684

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