Deutsch
 
Hilfe Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT

Freigegeben

Konferenzbeitrag

Improving Denoising Algorithms via a Multi-scale Meta-procedure

MPG-Autoren
/persons/resource/persons83841

Burger,  HC
Max Planck Institute for Intelligent Systems, Max Planck Society;

/persons/resource/persons83954

Harmeling,  S
Max Planck Institute for Intelligent Systems, Max Planck Society;

Volltexte (beschränkter Zugriff)
Für Ihren IP-Bereich sind aktuell keine Volltexte freigegeben.
Volltexte (frei zugänglich)
Es sind keine frei zugänglichen Volltexte in PuRe verfügbar
Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar
Zitation

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


Zitierlink: https://hdl.handle.net/11858/00-001M-0000-0013-BA4C-A
Zusammenfassung
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.