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An Enhanced Model for Inpainting on Digital Images Using Dynamic Masking

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dc.contributor.author Rana, M. S.
dc.contributor.author Hasan, M. M.
dc.contributor.author Bhuiyan, Touhid
dc.date.accessioned 2018-09-11T05:11:15Z
dc.date.accessioned 2019-05-27T09:59:32Z
dc.date.available 2018-09-11T05:11:15Z
dc.date.available 2019-05-27T09:59:32Z
dc.date.issued 2017-04
dc.identifier.uri http://hdl.handle.net/20.500.11948/3130
dc.description.abstract In the digital world, inpainting is the algorithm used to replace or reconstruct lost, corrupted, or deteriorated parts of image data. Of the various proposed inpainting methods, convolutional methods are the simplest and most efficient. In this paper, an enhanced inpainting model based on convolution theorem is proposed for digital images that preserves the edge and effectively estimates the lost or damaged parts of an image. In the proposed algorithm, a mask image is created dynamically to detect the image area to inpaint where most of the algorithms detect the missing parts of the image manually. Studies confirm the simplicity and effectiveness of our method, which also produces results that are comparable to those produced using other methods. http://doi.org/10.12720/jcm.12.4.248-253 en_US
dc.language.iso en en_US
dc.publisher Semantic Scholar en_US
dc.subject inpainting en_US
dc.subject filtering en_US
dc.subject convolution en_US
dc.subject PSNR en_US
dc.subject Restoration en_US
dc.title An Enhanced Model for Inpainting on Digital Images Using Dynamic Masking en_US
dc.type Article en_US


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