Please use this identifier to cite or link to this item: https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/15666
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dc.contributor.authorNeole, Bhumika-
dc.contributor.authorPinjarkar, Latika-
dc.contributor.authorPatni, Jagdish Chandra-
dc.contributor.authorVidyabhanu, Anusha-
dc.contributor.authorMalpani, Anshu-
dc.contributor.authorDudhe, Ojas-
dc.date.accessioned2024-05-29T08:53:00Z-
dc.date.available2024-05-29T08:53:00Z-
dc.date.issued2024-
dc.identifier.citationVol. 5, No. 6; pp. 298-307en_US
dc.identifier.issn2633-352X-
dc.identifier.urihttp://dx.doi.org/10.61707/y562qn51-
dc.identifier.urihttp://gnanaganga.inflibnet.ac.in:8080/jspui/handle/123456789/15666-
dc.description.abstractBetter results can be produced by the Hybridization of the Wavelet-based image denoising technique and sparse representation of edges. A novel method for spatial domain edge identification that produces a denoised image that has been tainted by additive white Gaussian noise without sacrificing the image's detail information. By combining bivariate shrinkage and local profile edge detection, a denoised image is produced. In this paper, the hybridization method is proposed by modifying the existing Wavelet Transform for image denoising leading to an increase in the PSNR and SSIM as compared to that given by existing Wavelet denoising techniques, maintaining the visual quality of an image. To modify the wavelet coefficients Bivariate Wavelet Shrinkage is used. The quality assessment is evaluated in terms of SSIM value and PSNR value. © 2024, Transnational Press London Ltd. All rights reserved.en_US
dc.language.isoenen_US
dc.publisherInternational Journal of Religionen_US
dc.publisherTransnational Press London Ltden_US
dc.subjectBivariate Wavelet Shrinkageen_US
dc.subjectDiscrete Wavelet Transformen_US
dc.subjectPsnren_US
dc.subjectSpatial Domainen_US
dc.subjectSsimen_US
dc.titleDenoising of Digital Images Using Spatial Domain Edge Detection Approachen_US
dc.typeArticleen_US
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