Title :
Power Iteration Denoising
Author :
Gomo, Panganai ; Spann, Mike
Author_Institution :
EECE, Univ. of Birmingham, Birmingham, UK
Abstract :
We present a simple method for image denoising called power iteration denoising (PID). PID finds a low dimensional embedding of the image data using a truncated power iteration on a normalized pair-wise similarity matrix generated from the image. This embedding turns out to be an effective denoising algorithm outperforming the widely used non-local means algorithm. We apply this method to the denoising of noisy digital camera images producing visually pleasing results.
Keywords :
cameras; image denoising; denoising algorithm; image data; image denoising; low dimensional embedding; noisy digital camera images; nonlocal means algorithm; normalized pairwise similarity matrix; power iteration denoising; truncated power iteration; Clustering algorithms; Image denoising; Laplace equations; Noise measurement; Noise reduction; PSNR; Pixel; harmonic functions; non-local means; power iteration; semi-supervised machine learning; spectral clustering;
Conference_Titel :
Machine Learning and Applications (ICMLA), 2010 Ninth International Conference on
Conference_Location :
Washington, DC
Print_ISBN :
978-1-4244-9211-4
DOI :
10.1109/ICMLA.2010.131