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dc.contributor.authorBalure C.S.
dc.contributor.authorRamesh Kini M.
dc.date.accessioned2021-05-05T10:30:07Z-
dc.date.available2021-05-05T10:30:07Z-
dc.date.issued2021
dc.identifier.citationInternational Journal of Computational Vision and Robotics Vol. 11 , 1 , p. 109 - 125en_US
dc.identifier.urihttps://doi.org/10.1504/IJCVR.2021.111878
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/16291-
dc.description.abstractLike optical images, depth images are also gaining popularity because of its use in many applications like robot navigation, augmented reality, 3DTV and more. The commercially available depth cameras generate depth images which suffer from low spatial resolution, corrupted with noise, and missing regions. Such images need to be super-resolved, denoised and inpainted before using them to have better accuracy. Super-resolution (SR) techniques can be used to produce a high-resolution output. Since SR is an ill-posed inverse problem, a good initial estimate is always a good regulariser to find the optimal solution. We propose an initial estimate as part of our SR pipeline, esp. ×8, which will helps in quick convergence and accurate output. We propose a cascade approach by combining residual interpolation (RI) method with anisotropic total generalised variation (ATGV) method, both uses HR guidance image. The improvements are shown qualitative and quantitative with different levels of noise. © 2021 Inderscience Publishers. All rights reserved.en_US
dc.titleGuidance-based improved depth upsampling with better initial estimateen_US
dc.typeArticleen_US
Appears in Collections:1. Journal Articles

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