An adaptive bitonic filtering based edge fusion algorithm for Gaussian denoising

It is broadly recognized that conserving the essential geometrical features of an image is crucial while denoising it. To accomplish this aim, various denoising techniques have been represented in the literature. The technique based on dual way edge fusion can efficiently solve the problem of denois...

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Main Authors: Bhawna Goyal, Anupma Gupta, Ayush Dogra, Deepika Koundal
Format: Article
Language:English
Published: KeAi Communications Co., Ltd. 2022-06-01
Series:International Journal of Cognitive Computing in Engineering
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S2666307422000079
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author Bhawna Goyal
Anupma Gupta
Ayush Dogra
Deepika Koundal
author_facet Bhawna Goyal
Anupma Gupta
Ayush Dogra
Deepika Koundal
author_sort Bhawna Goyal
collection DOAJ
description It is broadly recognized that conserving the essential geometrical features of an image is crucial while denoising it. To accomplish this aim, various denoising techniques have been represented in the literature. The technique based on dual way edge fusion can efficiently solve the problem of denoising. In this paper, an efficient denoising scheme using an innovative method of calculating the image base and details is being proposed. The noisy image is thresholded to remove extra noise by the bitonic filter. Details of the discontinuities is extracted by subtracting the recovered image from the noisy image. Subsequently, details features are subtracted from the noisy image to extract the base information. After that, image features and noise are simultaneously filtered by rolling guidance filter to remove the remaining noise from the features and the significant edge information from the filtered noise. The two images are fused with maximum coefficient value to enhance the information content and visual quality of denoised image. The proposed Dual Way Residue Noise Thresholding (DWEFD) is a combination of various spatial and transform domain commutations performed parallelly. Extensive experimental results and investigations reveal that the proposed methodology is able to recover feature details of an image thereby reducing information loss along with efficient noise removal.
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spelling doaj.art-87a0c460df234b7485a5b4dbb277bb122023-01-08T04:15:00ZengKeAi Communications Co., Ltd.International Journal of Cognitive Computing in Engineering2666-30742022-06-0139097An adaptive bitonic filtering based edge fusion algorithm for Gaussian denoisingBhawna Goyal0Anupma Gupta1Ayush Dogra2Deepika Koundal3Department of Electronics and Communication Engineering, Chandigarh University, Mohali, India; Corresponding author.Department of Electronics and Communication Engineering, Chandigarh University, Mohali, IndiaRonin Institute, Montclair, NJ 07043 USADeptt. of Virtualisation, School of Computer Science, University of Petroleum and Energy Studies, Dehradun, IndiaIt is broadly recognized that conserving the essential geometrical features of an image is crucial while denoising it. To accomplish this aim, various denoising techniques have been represented in the literature. The technique based on dual way edge fusion can efficiently solve the problem of denoising. In this paper, an efficient denoising scheme using an innovative method of calculating the image base and details is being proposed. The noisy image is thresholded to remove extra noise by the bitonic filter. Details of the discontinuities is extracted by subtracting the recovered image from the noisy image. Subsequently, details features are subtracted from the noisy image to extract the base information. After that, image features and noise are simultaneously filtered by rolling guidance filter to remove the remaining noise from the features and the significant edge information from the filtered noise. The two images are fused with maximum coefficient value to enhance the information content and visual quality of denoised image. The proposed Dual Way Residue Noise Thresholding (DWEFD) is a combination of various spatial and transform domain commutations performed parallelly. Extensive experimental results and investigations reveal that the proposed methodology is able to recover feature details of an image thereby reducing information loss along with efficient noise removal.http://www.sciencedirect.com/science/article/pii/S2666307422000079Image denoisingMedical imagingBM3DPeak signal-to-noise ratioGaussian noise
spellingShingle Bhawna Goyal
Anupma Gupta
Ayush Dogra
Deepika Koundal
An adaptive bitonic filtering based edge fusion algorithm for Gaussian denoising
International Journal of Cognitive Computing in Engineering
Image denoising
Medical imaging
BM3D
Peak signal-to-noise ratio
Gaussian noise
title An adaptive bitonic filtering based edge fusion algorithm for Gaussian denoising
title_full An adaptive bitonic filtering based edge fusion algorithm for Gaussian denoising
title_fullStr An adaptive bitonic filtering based edge fusion algorithm for Gaussian denoising
title_full_unstemmed An adaptive bitonic filtering based edge fusion algorithm for Gaussian denoising
title_short An adaptive bitonic filtering based edge fusion algorithm for Gaussian denoising
title_sort adaptive bitonic filtering based edge fusion algorithm for gaussian denoising
topic Image denoising
Medical imaging
BM3D
Peak signal-to-noise ratio
Gaussian noise
url http://www.sciencedirect.com/science/article/pii/S2666307422000079
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