Paper Info
Title
Adaptive Non-Local Means For Multiview Image Denoising: Searching For The Right Patches Via A Statistical Approach
Abstract
We present an adaptive non-local means (NLM) denoising method for a sequence of images captured by a multiview imaging system, where direct extensions of existing single image NLM methods are incapable of producing good results. Our proposed method consists of three major components: (1) a robust joint-view distance metric to measure the similarity of patches; (2) an adaptive procedure derived from statistical properties of the estimates to determine the optimal number of patches to be used; (3) a new NLM algorithm to denoise using only a set of similar patches. Experimental results show that the proposed method is robust to disparity estimation error, out-performs existing algorithms in multiview settings, and performs competitively in video settings.
Year
Venue
Keywords
2013
2013 20TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP 2013)
Non-local means, adaptive filtering, multiview denoising, patch-based denoising
Field
DocType
ISSN
Noise reduction,Computer vision,Pattern recognition,Computer science,Non-local means,Image matching,Metric (mathematics),Image capture,Artificial intelligence,Image denoising,Video denoising,Statistical analysis
Conference
1522-4880
Citations 
PageRank 
References 
8
0.51
16
Authors
4
Name
Order
Citations
PageRank
Enming Luo1846.63
Stanley H. Chan240330.95
Shengjun Pan3937.68
Truong Q. Nguyen41402136.69