Publication Type
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Article
Title
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Development of robust neighbor embedding based super-resolution scheme
Author(s)
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Deepasikha Mishra, Banshidhar Majhi, Pankaj Kumar Sa, and Ratnakar Dash
Journal
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Neurocomputing
Abstract
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In this paper, we propose a robust neighbor embedding super-resolution (RNESR) scheme to generate a super-resolution (SR) image from a single low-resolution (LR) image. It utilizes histogram matching for selection of best training pair of images. This helps to learn co-occurrence prior to high-resolution (HR) image reconstruction. The global neighborhood size is computed from local neighborhood size, which avoids the over-fitting and under-fitting problem during neighbor embedding. Robust locally linear embedding (RLLE) is used in place of locally linear embedding (LLE) to generate HR image. To validate the scheme, exhaustive simulation has been carried out on standard images. Comparative analysis with respect to different measures like peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and feature similarity index (FSIM) reveals that the RNESR scheme generates high-quality SR image from a LR image as compared to existing schemes.
Keywords
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Graphic methods; Image coding; Image reconstruction; Optical resolving power; Quality control; Signal to noise ratio, Global neighborhood selection; Histogram matching; Locally linear embedding; Robust principal component analysis; Super resolution, Principal component analysis