Generalized Fuzzy Peer Group for Removal of Mixed Noise from Color Image
In this letter, a novel method has been proposed, which includes formulation of similarity function and a fuzzy-based method for filtering mixed noise. The similarity function is adaptive to local noise level and edge information, and it is used to detect similarity among pixels in a peer group. Bas...
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| Veröffentlicht in: | IEEE signal processing letters 2018-09, Vol.25 (9), p.1330-1334 |
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| container_title | IEEE signal processing letters |
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| creator | Dev, Raghav Verma, Nishchal K. |
| description | In this letter, a novel method has been proposed, which includes formulation of similarity function and a fuzzy-based method for filtering mixed noise. The similarity function is adaptive to local noise level and edge information, and it is used to detect similarity among pixels in a peer group. Based on the peer group, color image corrupted with mixed Gaussian and impulse noise is filtered. The novel method for filtering is an adaptive weighted average of different sized filters. The weights of different sized filters are adaptive to local noise and edge information. The proposed work has been compared with some state-of-the-art techniques. The results show proposed approach is better in preserving edge and color information than others. |
| doi_str_mv | 10.1109/LSP.2018.2852140 |
| format | Article |
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The results show proposed approach is better in preserving edge and color information than others.</description><identifier>ISSN: 1070-9908</identifier><identifier>EISSN: 1558-2361</identifier><identifier>DOI: 10.1109/LSP.2018.2852140</identifier><identifier>CODEN: ISPLEM</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Adaptive filters ; AWGN ; Color ; Color image ; Filtration ; Image color analysis ; Image edge detection ; Microsoft Windows ; mixed noise ; Noise ; Noise level ; peer group ; Similarity ; Similarity measures</subject><ispartof>IEEE signal processing letters, 2018-09, Vol.25 (9), p.1330-1334</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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The results show proposed approach is better in preserving edge and color information than others.</description><subject>Adaptive filters</subject><subject>AWGN</subject><subject>Color</subject><subject>Color image</subject><subject>Filtration</subject><subject>Image color analysis</subject><subject>Image edge detection</subject><subject>Microsoft Windows</subject><subject>mixed noise</subject><subject>Noise</subject><subject>Noise level</subject><subject>peer group</subject><subject>Similarity</subject><subject>Similarity measures</subject><issn>1070-9908</issn><issn>1558-2361</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kE1LAzEQhoMoWKt3wUvA89aZJJvNHqXYD6ha_DiHdTuRLbtNzXaL7a83pcXTvDDPOwMPY7cIA0TIH2bv84EANANhUoEKzlgP09QkQmo8jxkySPIczCW7atslABg0aY9NxrSiUNTVnhZ81O33Oz4nCnwcfLfmzgf-Ro3fFjX3jj9Xv5F68VVL3AXf8KGvIzFtim-6ZheuqFu6Oc0--xw9fQwnyex1PB0-zpJS5LhJlC5BGgIsFqWhUpksM-QUOMpz1CC-0AiBKI02SivSi4xSLFBL7UqJLpN9dn-8uw7-p6N2Y5e-C6v40sZehqgwVZGCI1UG37aBnF2HqinCziLYgy8bfdmDL3vyFSt3x0pFRP-4iRsJWv4BG09kCA</recordid><startdate>20180901</startdate><enddate>20180901</enddate><creator>Dev, Raghav</creator><creator>Verma, Nishchal K.</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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| ispartof | IEEE signal processing letters, 2018-09, Vol.25 (9), p.1330-1334 |
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| language | eng |
| recordid | cdi_crossref_primary_10_1109_LSP_2018_2852140 |
| source | IEEE Electronic Library (IEL) |
| subjects | Adaptive filters AWGN Color Color image Filtration Image color analysis Image edge detection Microsoft Windows mixed noise Noise Noise level peer group Similarity Similarity measures |
| title | Generalized Fuzzy Peer Group for Removal of Mixed Noise from Color Image |
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