Improving Denoising Algorithms via a Multi-scale Meta-procedure

  • Harold Christopher Burger
  • Stefan Harmeling
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6835)


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.


Additive White Gaussian Noise Noisy Image Image Denoising High Noise Level Multiscale Approach 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Harold Christopher Burger
    • 1
  • Stefan Harmeling
    • 1
  1. 1.Max Planck Institute for Intelligent SystemsTübingenGermany

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