Local Adaptive Image Filtering Based on Recursive Dilation Segmentation.

Zhang, J; Chen, C; Chen, K; Ju, M; Zhang, D

Local Adaptive Image Filtering Based on Recursive Dilation Segmentation.

Zhang, J Chen, C Chen, K Ju, M

Zhang, D

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: Sensors (Basel), 2023, 23, (13), pp. 5776
Issue Date:: 2023-06-21

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Field	Value	Language
dc.contributor.author	Zhang, J
dc.contributor.author	Chen, C
dc.contributor.author	Chen, K
dc.contributor.author	Ju, M https://orcid.org/0000-0003-4378-3781
dc.contributor.author	Zhang, D
dc.date.accessioned	2024-05-03T04:31:25Z
dc.date.available	2023-06-16
dc.date.available	2024-05-03T04:31:25Z
dc.date.issued	2023-06-21
dc.identifier.citation	Sensors (Basel), 2023, 23, (13), pp. 5776
dc.identifier.issn	1424-8220
dc.identifier.issn	1424-8220
dc.identifier.uri	http://hdl.handle.net/10453/178614
dc.description.abstract	This paper introduces a simple but effective image filtering method, namely, local adaptive image filtering (LAIF), based on an image segmentation method, i.e., recursive dilation segmentation (RDS). The algorithm is motivated by the observation that for the pixel to be smoothed, only the similar pixels nearby are utilized to obtain the filtering result. Relying on this observation, similar pixels are partitioned by RDS before applying a locally adaptive filter to smooth the image. More specifically, by directly taking the spatial information between adjacent pixels into consideration in a recursive dilation way, RDS is firstly proposed to partition the guided image into several regions, so that the pixels belonging to the same segmentation region share a similar property. Then, guided by the iterative segmented results, the input image can be easily filtered via a local adaptive filtering technique, which smooths each pixel by selectively averaging its local similar pixels. It is worth mentioning that RDS makes full use of multiple integrated information including pixel intensity, hue information, and especially spatial adjacent information, leading to more robust filtering results. In addition, the application of LAIF in the remote sensing field has achieved outstanding results, specifically in areas such as image dehazing, denoising, enhancement, and edge preservation, among others. Experimental results show that the proposed LAIF can be successfully applied to various filtering-based tasks with favorable performance against state-of-the-art methods.
dc.format	Electronic
dc.language	eng
dc.publisher	MDPI
dc.relation.ispartof	Sensors (Basel)
dc.relation.isbasedon	10.3390/s23135776
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0301 Analytical Chemistry, 0502 Environmental Science and Management, 0602 Ecology, 0805 Distributed Computing, 0906 Electrical and Electronic Engineering
dc.subject.classification	Analytical Chemistry
dc.subject.classification	3103 Ecology
dc.subject.classification	4008 Electrical engineering
dc.subject.classification	4009 Electronics, sensors and digital hardware
dc.subject.classification	4104 Environmental management
dc.subject.classification	4606 Distributed computing and systems software
dc.subject.mesh	Algorithms
dc.subject.mesh	Image Processing, Computer-Assisted
dc.subject.mesh	Algorithms
dc.subject.mesh	Image Processing, Computer-Assisted
dc.subject.mesh	Algorithms
dc.subject.mesh	Image Processing, Computer-Assisted
dc.title	Local Adaptive Image Filtering Based on Recursive Dilation Segmentation.
dc.type	Journal Article
utslib.citation.volume	23
utslib.location.activity	Switzerland
utslib.for	0301 Analytical Chemistry
utslib.for	0502 Environmental Science and Management
utslib.for	0602 Ecology
utslib.for	0805 Distributed Computing
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	open_access	*
dc.date.updated	2024-05-03T04:31:15Z
pubs.issue	13
pubs.publication-status	Published online
pubs.volume	23
utslib.citation.issue	13

Abstract:

This paper introduces a simple but effective image filtering method, namely, local adaptive image filtering (LAIF), based on an image segmentation method, i.e., recursive dilation segmentation (RDS). The algorithm is motivated by the observation that for the pixel to be smoothed, only the similar pixels nearby are utilized to obtain the filtering result. Relying on this observation, similar pixels are partitioned by RDS before applying a locally adaptive filter to smooth the image. More specifically, by directly taking the spatial information between adjacent pixels into consideration in a recursive dilation way, RDS is firstly proposed to partition the guided image into several regions, so that the pixels belonging to the same segmentation region share a similar property. Then, guided by the iterative segmented results, the input image can be easily filtered via a local adaptive filtering technique, which smooths each pixel by selectively averaging its local similar pixels. It is worth mentioning that RDS makes full use of multiple integrated information including pixel intensity, hue information, and especially spatial adjacent information, leading to more robust filtering results. In addition, the application of LAIF in the remote sensing field has achieved outstanding results, specifically in areas such as image dehazing, denoising, enhancement, and edge preservation, among others. Experimental results show that the proposed LAIF can be successfully applied to various filtering-based tasks with favorable performance against state-of-the-art methods.

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http://hdl.handle.net/10453/178614