Adaptive noise smoothing algorithm based on nonstationary correlation assumption

Sung Cheol Park, Gi Kang

Research output: Contribution to journalConference article


Various noise smoothing algorithms based on the nonstationary image model have been proposed. In most conventional nonstationary image models, however, the pixels are assumed to be uncorrelated to each other in order not to increase the computational burden too much. As a result, some detailed information is lost in the filtered results. In this paper, we propose a computationally feasible adaptive noise smoothing algorithm which considers the nonstationary correlation characteristics of images. We assume that the image has a nonstationary mean and can be segmented into subimages which have individually different stationary correlations. Taking advantage of the special structure of the covariance matrix that results from the proposed image model, we derive a computationally efficient FFT-based adaptive linear minimum mean square error filter. The justification for the proposed image model is presented and the effectiveness of the proposed algorithm is demonstrated experimentally.

Original languageEnglish
Pages (from-to)355-359
Number of pages5
JournalConference Record of the Asilomar Conference on Signals, Systems and Computers
Publication statusPublished - 2001 Dec 1
Event35th Asilomar Conference on Signals, Systems and Computers - Pacific Grove, CA, United States
Duration: 2001 Nov 42001 Nov 7


All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Computer Networks and Communications

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