A Non-Local Algorithm for Image Denoising
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A universal noise removal algorithm with an impulse detector
IEEE Transactions on Image Processing
Accelerating non-local denoising with a patch based dictionary
Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing
A stochastic image denoising algorithm using 3-D block filtering under a non-local means framework
Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing
A general non-local denoising model using multi-kernel-induced measures
Pattern Recognition
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In this paper, improvements to Non-Local Means (NLM) image denoising method is proposed to reduce the computational complexity. In the original NLM algorithm, neighborhood weightages are computed using the window similarity technique. The proposed technique replaces the window similarity by a modified multi-resolution based approach with much fewer comparisons rather than all pixels comparison. This approach also uses the concept of filtering out non-similar neighborhood pixels based on fixed sized window gray mean values. Further, mean values of the variable sized windows in the image are computed efficiently using Summed Image (SI) concept, which requires only 3 additions. The proposed approach is nearly 80 times faster than original Baudes NLM algorithm with close subjective and objective quality measurements.