Paper Info
Title
A Robust and Fast Non-Local Means Algorithm for Image Denoising
Abstract
In the paper, we propose a robust and fast image denoising method. The approach integrates both Non-Local means algorithm and Laplacian Pyramid. Given an image to be denoised, we first decompose it into Laplacian pyramid. Exploiting the redundancy property of Laplacian pyramid, we then perform non-local means on every level image of Laplacian pyramid. Essentially, we use the similarity of image features in Laplacian pyramid to act as weight to denoise image. Since the features extracted in Laplacian pyramid are localized in spatial position and scale, they are much more able to describe image, and computing the similarity between them is more reasonable and more robust. Also, based on the efficient Summed Square Image (SSI) scheme and Fast Fourier Transform (FFT), we present an accelerating algorithm to break the bottleneck of non-local means algorithm — similarity computation of compare windows. After speedup, our algorithm is fifty times faster than original non-local means algorithm. Experiments demonstrated the effectiveness of our algorithm.
Year
DOI
Venue
2008
10.1007/s11390-008-9129-8
Journal of Computer Science and Technology
Keywords
Field
DocType
fft,image denoising,laplacian pyramid,non-local means,summed square image,fast fourier transform,non local means,feature extraction,image features
Bottleneck,Pattern recognition,Non-local means,Computer science,Feature (computer vision),Algorithm,Pyramid (image processing),Redundancy (engineering),Fast Fourier transform,Artificial intelligence,Contourlet,Speedup
Journal
Volume
Issue
ISSN
23
2
1860-4749
Citations 
PageRank 
References 
43
1.68
15
Authors
10
Name
Order
Citations
PageRank
Yan-Li Liu1442.06
Jin Wang2616.40
Xi Chen3431.68
Yan-Wen Guo434839.32
Qun-Sheng Peng5887.57
刘艳丽6442.06
王进7442.06
陈曦8442.06
郭延文9442.06
彭群生10452.74