Defocused image restoration using Wiener and inverse filter in context of security application
Many times, the use of image processing techniques becomes a vital tool in investigation processes of security applications. This paper elaborates an attempt made in image processing domain which is needed in investigation processes of security applications to achieve deblurring of images using Wien...
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description | Many times, the use of image processing techniques becomes a vital tool in investigation processes of security applications. This paper elaborates an attempt made in image processing domain which is needed in investigation processes of security applications to achieve deblurring of images using Wiener and Inverse filters and the comparative study on the basis of image parameters. For accurate investigation and observations, improved quality of the defocused images can be obtained using the filtering process through which blur or noise can be removed. Such blur or noise gets induced in the captured images due to shaking of security camera while continuously moving to capture the area under surveillance, improper maintenance of such camera system or unlearned lenses of the camera. The improper illumination of the area under surveillance or the fast movement of the object itself also creates sometimes a blur in the captured images. In this work, two filters namely Wiener filter and Inverse filter are used to remove the noise and to restore the images after its quality improvement. Further to understand the quality improvement of the images, an analysis of restored images is done on the basis of image parameters. Finally, the obtained results are reported in tabulated form along with the graphs of original and restored image parameters. |
doi_str_mv | 10.1063/5.0106020 |
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Further to understand the quality improvement of the images, an analysis of restored images is done on the basis of image parameters. Finally, the obtained results are reported in tabulated form along with the graphs of original and restored image parameters.</description><subject>Cameras</subject><subject>Comparative studies</subject><subject>Image processing</subject><subject>Image quality</subject><subject>Image restoration</subject><subject>Parameters</subject><subject>Quality improvement</subject><subject>Security</subject><subject>Shaking</subject><subject>Surveillance</subject><subject>Wiener filtering</subject><issn>0094-243X</issn><issn>1551-7616</issn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2022</creationdate><recordtype>conference_proceeding</recordtype><recordid>eNp9kE1LAzEYhIMoWKsH_0HAm7A12Wyym6NUq0LBi6InQ5qPklKTNckW---NbcGbp4GXh5l3BoBLjCYYMXJDJ6goqtERGGFKcdUyzI7BCCHeVHVD3k_BWUorhGrett0IfNwZG9SQjIbuUy4NjCblEGV2wcMhOb-Eb854E6H0BfEbE5OB1q1zOTkPVfDZfGcYLExGDdHlLZR9v3ZqZ3EOTqxcJ3Nx0DF4nd2_TB-r-fPD0_R2XvWYdbnCrGkQpY3lzUJqghki3HDEtMaK0IVtldQWM7zodOnApUWEIN1qZbC2XHVkDK72vn0MX0OpIFZhiL5EirptOspbQnihrvdUUi7v_hN9LLXjVmAkfvcTVBz2-w_ehPgHil5b8gPXi3IJ</recordid><startdate>20221209</startdate><enddate>20221209</enddate><creator>Goilkar, Suhasini S.</creator><creator>Yadav, Dinkar M.</creator><general>American Institute of Physics</general><scope>8FD</scope><scope>H8D</scope><scope>L7M</scope></search><sort><creationdate>20221209</creationdate><title>Defocused image restoration using Wiener and inverse filter in context of security application</title><author>Goilkar, Suhasini S. ; Yadav, Dinkar M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p168t-16440554f94bad316039e906dd1c35bf7cadf161b8d2439af0330d7dce1df9c83</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Cameras</topic><topic>Comparative studies</topic><topic>Image processing</topic><topic>Image quality</topic><topic>Image restoration</topic><topic>Parameters</topic><topic>Quality improvement</topic><topic>Security</topic><topic>Shaking</topic><topic>Surveillance</topic><topic>Wiener filtering</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Goilkar, Suhasini S.</creatorcontrib><creatorcontrib>Yadav, Dinkar M.</creatorcontrib><collection>Technology Research Database</collection><collection>Aerospace Database</collection><collection>Advanced Technologies Database with Aerospace</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Goilkar, Suhasini S.</au><au>Yadav, Dinkar M.</au><au>Kaur, Sukhdeep</au><au>Bhatia, Vinay</au><au>Tandon, Bhawna</au><au>Singla, Parveen</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Defocused image restoration using Wiener and inverse filter in context of security application</atitle><btitle>AIP conference proceedings</btitle><date>2022-12-09</date><risdate>2022</risdate><volume>2576</volume><issue>1</issue><issn>0094-243X</issn><eissn>1551-7616</eissn><coden>APCPCS</coden><abstract>Many times, the use of image processing techniques becomes a vital tool in investigation processes of security applications. This paper elaborates an attempt made in image processing domain which is needed in investigation processes of security applications to achieve deblurring of images using Wiener and Inverse filters and the comparative study on the basis of image parameters. For accurate investigation and observations, improved quality of the defocused images can be obtained using the filtering process through which blur or noise can be removed. Such blur or noise gets induced in the captured images due to shaking of security camera while continuously moving to capture the area under surveillance, improper maintenance of such camera system or unlearned lenses of the camera. The improper illumination of the area under surveillance or the fast movement of the object itself also creates sometimes a blur in the captured images. In this work, two filters namely Wiener filter and Inverse filter are used to remove the noise and to restore the images after its quality improvement. 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subjects | Cameras Comparative studies Image processing Image quality Image restoration Parameters Quality improvement Security Shaking Surveillance Wiener filtering |
title | Defocused image restoration using Wiener and inverse filter in context of security application |
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