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Conference Paper

Improving Denoising Algorithms via a Multi-scale Meta-procedure

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons83841

Burger,  HC
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83954

Harmeling,  S
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Burger, H., & Harmeling, S. (2011). Improving Denoising Algorithms via a Multi-scale Meta-procedure. In Pattern Recognition (pp. 206-215). Berlin, Germany: Springer.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-BA4C-A
Abstract
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.