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Improving Denoising Algorithms via a Multi-scale Meta-procedure
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.
@inproceedings{BurgerH2011, title = {Improving Denoising Algorithms via a Multi-scale Meta-procedure }, booktitle = {Pattern Recognition}, 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.}, pages = {206-215}, editors = {Mester, R. , M. Felsberg}, publisher = {Springer}, address = {Berlin, Germany}, month = sep, year = {2011}, slug = {burgerh2011}, author = {Burger, HC. and Harmeling, S.}, month_numeric = {9} }