Multi-Objective Threshold Optimized Image De-Noising Algorithm for High Density Mixed Impulse Noise.
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| Title: | Multi-Objective Threshold Optimized Image De-Noising Algorithm for High Density Mixed Impulse Noise. |
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| Authors: | V, Suresh Babu1 (AUTHOR) sureshvece@gmail.com, R, Vijaykumar V2 (AUTHOR), K, Mohaideen Abdul Kadhar3 (AUTHOR), R, Sudhakar4 (AUTHOR) |
| Source: | Journal of Intelligent & Fuzzy Systems. Oct2025, Vol. 49 Issue 4, p1071-1087. 17p. |
| Subjects: | Multi-objective optimization, Burst noise, Signal-to-noise ratio, Evolutionary algorithms, Art techniques |
| Abstract: | This paper proposes a novel Multi-Objective Optimization based Fuzzy Switching Median Filter (MOOFASMF) to remove high density Random Valued Impulse Noise (RVIN), "Salt & Pepper" Impulse Noise (SPIN) and Mixed Impulse Noise (MIN). In this work, multi-objective optimization technique is used to find out the fuzzy switching median filter threshold values for accurate detection of corrupted pixels. The proposed multi-objective framework uses Decomposition based Multi Objective Evolutionary Algorithm (MOEA/D) to obtain optimized fuzzy switching median filter drives the threshold values with the objectives Mean Square Error (MSE) and inverse of Structural Similarity Index Metrics (SSIM) as optimization objectives. Even though the MSE and SSIM are not closely related parameters, the optimized threshold value gives better results in terms of both PSNR and SSIM. The advantages of the proposed framework are that it works effectively on RVIN, SPIN, and MIN-affected images. The effectiveness of the proposed framework is outstanding for high-density RVIN, SPIN, and MIN, which makes it more advantageous over other existing methods. Experimental results in terms of visual and quantitative metrics such as Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Structural Similarity Index Metrics (SSIM), and Edge Preservation Index (EPI) clearly demonstrates the better performance of the proposed algorithm over the state of art techniques. The proposed framework performed 6.02% and 32.11% better than the best existing methods in terms of PSNR and SSIM for the mixture of 40% SPIN & 50% RVIN affected image. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Intelligent & Fuzzy Systems is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 188155926 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Multi-Objective Threshold Optimized Image De-Noising Algorithm for High Density Mixed Impulse Noise. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22V%2C+Suresh+Babu%22">V, Suresh Babu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> sureshvece@gmail.com</i><br /><searchLink fieldCode="AR" term="%22R%2C+Vijaykumar+V%22">R, Vijaykumar V</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22K%2C+Mohaideen+Abdul+Kadhar%22">K, Mohaideen Abdul Kadhar</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22R%2C+Sudhakar%22">R, Sudhakar</searchLink><relatesTo>4</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Intelligent+%26+Fuzzy+Systems%22">Journal of Intelligent & Fuzzy Systems</searchLink>. Oct2025, Vol. 49 Issue 4, p1071-1087. 17p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Multi-objective+optimization%22">Multi-objective optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Burst+noise%22">Burst noise</searchLink><br /><searchLink fieldCode="DE" term="%22Signal-to-noise+ratio%22">Signal-to-noise ratio</searchLink><br /><searchLink fieldCode="DE" term="%22Evolutionary+algorithms%22">Evolutionary algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Art+techniques%22">Art techniques</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This paper proposes a novel Multi-Objective Optimization based Fuzzy Switching Median Filter (MOOFASMF) to remove high density Random Valued Impulse Noise (RVIN), "Salt & Pepper" Impulse Noise (SPIN) and Mixed Impulse Noise (MIN). In this work, multi-objective optimization technique is used to find out the fuzzy switching median filter threshold values for accurate detection of corrupted pixels. The proposed multi-objective framework uses Decomposition based Multi Objective Evolutionary Algorithm (MOEA/D) to obtain optimized fuzzy switching median filter drives the threshold values with the objectives Mean Square Error (MSE) and inverse of Structural Similarity Index Metrics (SSIM) as optimization objectives. Even though the MSE and SSIM are not closely related parameters, the optimized threshold value gives better results in terms of both PSNR and SSIM. The advantages of the proposed framework are that it works effectively on RVIN, SPIN, and MIN-affected images. The effectiveness of the proposed framework is outstanding for high-density RVIN, SPIN, and MIN, which makes it more advantageous over other existing methods. Experimental results in terms of visual and quantitative metrics such as Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Structural Similarity Index Metrics (SSIM), and Edge Preservation Index (EPI) clearly demonstrates the better performance of the proposed algorithm over the state of art techniques. The proposed framework performed 6.02% and 32.11% better than the best existing methods in terms of PSNR and SSIM for the mixture of 40% SPIN & 50% RVIN affected image. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Intelligent & Fuzzy Systems is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1177/18758967251353036 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 17 StartPage: 1071 Subjects: – SubjectFull: Multi-objective optimization Type: general – SubjectFull: Burst noise Type: general – SubjectFull: Signal-to-noise ratio Type: general – SubjectFull: Evolutionary algorithms Type: general – SubjectFull: Art techniques Type: general Titles: – TitleFull: Multi-Objective Threshold Optimized Image De-Noising Algorithm for High Density Mixed Impulse Noise. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: V, Suresh Babu – PersonEntity: Name: NameFull: R, Vijaykumar V – PersonEntity: Name: NameFull: K, Mohaideen Abdul Kadhar – PersonEntity: Name: NameFull: R, Sudhakar IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: Oct2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 10641246 Numbering: – Type: volume Value: 49 – Type: issue Value: 4 Titles: – TitleFull: Journal of Intelligent & Fuzzy Systems Type: main |
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