Please use this identifier to cite or link to this item: https://idr.l1.nitk.ac.in/jspui/handle/123456789/16370
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dc.contributor.authorMhala N.C.
dc.contributor.authorPais A.R.
dc.date.accessioned2021-05-05T10:30:18Z-
dc.date.available2021-05-05T10:30:18Z-
dc.date.issued2020
dc.identifier.citationVisual Computer , Vol. , , p. -en_US
dc.identifier.urihttps://doi.org/10.1007/s00371-020-01972-9
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/16370-
dc.description.abstractNowadays, underwater images are being used to identify various important resources like objects, minerals, and valuable metals. Due to the wide availability of the Internet, we can transmit underwater images over a network. As underwater images contain important information, there is a need to transmit them securely over a network. Visual secret sharing (VSS) scheme is a cryptographic technique, which is used to transmit visual information over insecure networks. Recently proposed randomized VSS (RVSS) scheme recovers secret image (SI) with a self-similarity index (SSIM) of 60–80%. But, RVSS is suitable for general images, whereas underwater images are more complex than general images. In this paper, we propose a VSS scheme using super-resolution for sharing underwater images. Additionally, we have removed blocking artifacts from the reconstructed SI using convolution neural network (CNN)-based architecture. The proposed CNN-based architecture uses a residue image as a cue to improve the visual quality of the SI. The experimental results show that the proposed VSS scheme can reconstruct SI with almost 86–99% SSIM. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.en_US
dc.titleA secure visual secret sharing (VSS) scheme with CNN-based image enhancement for underwater imagesen_US
dc.typeArticleen_US
Appears in Collections:1. Journal Articles

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