Fast non-local means with size-adaptive search window

In this study, the authors present a fast non-local means (NLM) image denoising algorithm with size-adaptive search window. On the basis of the edge gradient and direction of the noisy image, the proposed fast scheme divides all the pixels into significant edge, moderate edge or non-edge region, and...

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Main Authors: Hancheng Yu, Min Tan
Format: Article
Language:English
Published: Wiley 2016-03-01
Series:The Journal of Engineering
Subjects:
Online Access:http://digital-library.theiet.org/content/journals/10.1049/joe.2015.0190
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author Hancheng Yu
Min Tan
author_facet Hancheng Yu
Min Tan
author_sort Hancheng Yu
collection DOAJ
description In this study, the authors present a fast non-local means (NLM) image denoising algorithm with size-adaptive search window. On the basis of the edge gradient and direction of the noisy image, the proposed fast scheme divides all the pixels into significant edge, moderate edge or non-edge region, and the size of the search window for most pixels which belong to non-edge region can be reduced. The proposed fast scheme also adopts different strategies to pre-select similar patches in the search window for efficient NLM denoising. Experimental results show that compared with the standard NLM method, the proposed fast scheme achieves a substantial reduction in computational cost and improvement in the denoising performance, both in terms of visual quality and numerical results.
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spelling doaj.art-264f9ba029de4741a176f9ddd8fc54cc2022-12-21T19:38:37ZengWileyThe Journal of Engineering2051-33052016-03-0110.1049/joe.2015.0190JOE.2015.0190Fast non-local means with size-adaptive search windowHancheng Yu0Min Tan1Nanjing University of Aeronautics and AstronauticsNanjing University of Aeronautics and AstronauticsIn this study, the authors present a fast non-local means (NLM) image denoising algorithm with size-adaptive search window. On the basis of the edge gradient and direction of the noisy image, the proposed fast scheme divides all the pixels into significant edge, moderate edge or non-edge region, and the size of the search window for most pixels which belong to non-edge region can be reduced. The proposed fast scheme also adopts different strategies to pre-select similar patches in the search window for efficient NLM denoising. Experimental results show that compared with the standard NLM method, the proposed fast scheme achieves a substantial reduction in computational cost and improvement in the denoising performance, both in terms of visual quality and numerical results.http://digital-library.theiet.org/content/journals/10.1049/joe.2015.0190image denoisingedge detectionvisual qualitycomputational costnonedge regionnoisy imageedge gradientNLM image denoising algorithmsize-adaptive search windowfast nonlocal means
spellingShingle Hancheng Yu
Min Tan
Fast non-local means with size-adaptive search window
The Journal of Engineering
image denoising
edge detection
visual quality
computational cost
nonedge region
noisy image
edge gradient
NLM image denoising algorithm
size-adaptive search window
fast nonlocal means
title Fast non-local means with size-adaptive search window
title_full Fast non-local means with size-adaptive search window
title_fullStr Fast non-local means with size-adaptive search window
title_full_unstemmed Fast non-local means with size-adaptive search window
title_short Fast non-local means with size-adaptive search window
title_sort fast non local means with size adaptive search window
topic image denoising
edge detection
visual quality
computational cost
nonedge region
noisy image
edge gradient
NLM image denoising algorithm
size-adaptive search window
fast nonlocal means
url http://digital-library.theiet.org/content/journals/10.1049/joe.2015.0190
work_keys_str_mv AT hanchengyu fastnonlocalmeanswithsizeadaptivesearchwindow
AT mintan fastnonlocalmeanswithsizeadaptivesearchwindow