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2021•Unpublished venueRequires access

Single Image Super-Resolution Method Based on Bilinear Interpolation and U-Net Combination

Pavel Lyakhov, Georgii Valuev, Maria Valueva, Dmitrii Kaplun, Aleksandr Sinitca

Open publisher page 4 citations

Abstract

The single image super-resolution issue is studied in this paper. We propose a new method based on a combination of bilinear interpolation and the U-Net neural network to solve this problem. The image is enlarged by bilinear interpolation, then its quality is improved by the neural network. This approach allows improving reconstruction quality by 1.00-6.56% compared with known methods.

About this research paper

What this paper is about

The single image super-resolution issue is studied in this paper. We propose a new method based on a combination of bilinear interpolation and the U-Net neural network to solve this problem. The image is enlarged by bilinear interpolation, then its quality is improved by the neural network. This approach allows improving reconstruction quality by 1.00-6.56% compared with known methods.

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OpenAlex reports 4 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

The single image super-resolution issue is studied in this paper. We propose a new method based on a combination of bilinear interpolation and the U-Net neural network to solve this problem. The image is enlarged by bilinear interpolation, then its quality is improved by the neural network. This approach allows improving reconstruction quality by 1.00-6.56% compared with known methods.

Key concepts: Bilinear interpolation, Interpolation (computer graphics), Stairstep interpolation, Demosaicing, Computer science, Image scaling, Net (polyhedron), Image (mathematics)

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