Paper Info
Title
Center pixel weight estimation for non-local means filtering using local James-Stein estimator with bounded self-weights
Abstract
Assigning appropriate center pixel weights (CPW) in nonlocal means (NLM) filter is an important issue to affect the quality of filtered images. Using local James-Stein (LJS) type CPW yielded superior peak signal-to-noise ratio (PSNR) over using other existing methods of determining the contribution of center pixels in NLM. However, the original LJS CPW method assumed no upper bound for self-weights implicitly, it may yield excessively large self-weights and visual artifacts. We propose a method, called bounded self-weights LJS (BLJS), to incorporate bounded self-weights into LJS such that this new estimator is dominating partially. Our proposed method was evaluated using a patient MR image with 3 levels of additive Gaussian noise. For fixed local area, the proposed BLJS yielded lower variances than the original LJS for almost all bias levels. BLJS also achieved PSNR comparable to or better than LJS. Visual image quality assessment showed that BLJS produced less localized visual artifacts than LJS.
Year
DOI
Venue
2016
10.1109/ISBI.2016.7493216
2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI)
Keywords
Field
DocType
James-Stein estimator,non-local means,center pixel weight,image filtering
Computer vision,James–Stein estimator,Visual artifact,Pattern recognition,Upper and lower bounds,Non-local means,Image quality,Pixel,Artificial intelligence,Gaussian noise,Mathematics,Estimator
Conference
ISSN
Citations 
PageRank 
1945-7928
1
0.35
References 
Authors
4
2
Name
Order
Citations
PageRank
Mink Phuong Nguyen110.35
Se Young Chun27218.18