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Improving Denoising Algorithms via a Multi-scale Meta-procedure


Conference Paper


Many state-of-the-art denoising algorithms focus on recovering high-frequency details in noisy images. However, images corrupted by large amounts of noise are also degraded in the lower frequencies. Thus properly handling all frequency bands allows us to better denoise in such regimes. To improve existing denoising algorithms we propose a meta-procedure that applies existing denoising algorithms across different scales and combines the resulting images into a single denoised image. With a comprehensive evaluation we show that the performance of many state-of-the-art denoising algorithms can be improved.

Author(s): Burger, HC. and Harmeling, S.
Book Title: Pattern Recognition
Pages: 206-215
Year: 2011
Month: September
Day: 0
Editors: Mester, R. , M. Felsberg
Publisher: Springer

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

DOI: 10.1007/978-3-642-23123-0_21
Event Name: 33rd DAGM Symposium
Event Place: Frankfurt a.M., Germany

Address: Berlin, Germany
Digital: 0
ISBN: 978-3-642-23123-0

Links: PDF


  title = {Improving Denoising Algorithms via a Multi-scale Meta-procedure },
  author = {Burger, HC. and Harmeling, S.},
  booktitle = {Pattern Recognition},
  pages = {206-215},
  editors = {Mester, R. , M. Felsberg},
  publisher = {Springer},
  address = {Berlin, Germany},
  month = sep,
  year = {2011},
  month_numeric = {9}