Real-time impulse noise removal

Real-time impulse noise removal An adaptive interpolation-based impulse noise removal (AIBINR) algorithm is proposed to remove impulse noise from color and gray-scale images in real time. AIBINR works fast and has no need for parameter tuning to remove fixed-valued impulse noise. A GPU application has been developed to demonstrate the speed and inherent parallelization capabilities of the proposed method. Using the high-speed implementation, we have shown that AIBINR can denoise color images fast enough to be used in real-time video denoising, while having comparable denoising performance when compared to the state-of-the-art methods without any modification to its parameters. Keywords Impulse noise removal · Cuda · Real-time denoising · High definition 1 Introduction filter (MDBUTMF) [9], boundary discriminative noise detection (BDND) [29] , improved boundary discriminative Hardware and communication faults, or faulty memory loca- noise detection (IBDND) [16], interpolation-based impulse tions create impulse noise in pictures [36]. Impulse noise noise removal (IBINR) [18] and unbiased weighted mean is a problem for image-processing applications. Moreover, filter (UWMF) [19]. images containing noise may cause performance reduction Ng and Ma proposed a BDND algorithm that performs in computer vision applications such as motion detection efficient noise removal up to 80% [29]. This method uses [5, 11, 12]. Most of these tasks require real-time http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Real-Time Image Processing Springer Journals

Real-time impulse noise removal

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Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer-Verlag GmbH Germany, part of Springer Nature
Subject
Computer Science; Image Processing and Computer Vision; Multimedia Information Systems; Computer Graphics; Pattern Recognition; Signal,Image and Speech Processing
ISSN
1861-8200
eISSN
1861-8219
D.O.I.
10.1007/s11554-018-0791-y
Publisher site
See Article on Publisher Site

Abstract

An adaptive interpolation-based impulse noise removal (AIBINR) algorithm is proposed to remove impulse noise from color and gray-scale images in real time. AIBINR works fast and has no need for parameter tuning to remove fixed-valued impulse noise. A GPU application has been developed to demonstrate the speed and inherent parallelization capabilities of the proposed method. Using the high-speed implementation, we have shown that AIBINR can denoise color images fast enough to be used in real-time video denoising, while having comparable denoising performance when compared to the state-of-the-art methods without any modification to its parameters. Keywords Impulse noise removal · Cuda · Real-time denoising · High definition 1 Introduction filter (MDBUTMF) [9], boundary discriminative noise detection (BDND) [29] , improved boundary discriminative Hardware and communication faults, or faulty memory loca- noise detection (IBDND) [16], interpolation-based impulse tions create impulse noise in pictures [36]. Impulse noise noise removal (IBINR) [18] and unbiased weighted mean is a problem for image-processing applications. Moreover, filter (UWMF) [19]. images containing noise may cause performance reduction Ng and Ma proposed a BDND algorithm that performs in computer vision applications such as motion detection efficient noise removal up to 80% [29]. This method uses [5, 11, 12]. Most of these tasks require real-time

Journal

Journal of Real-Time Image ProcessingSpringer Journals

Published: May 29, 2018

References

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