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Volume: 28 | Article ID: art00015
Light-Weight Single Image Super-Resolution via Pattern-wise Regression Function
  DOI :  10.2352/ISSN.2470-1173.2016.18.DPMI-028  Published OnlineFebruary 2016

We propose a novel upsampling approach that is suitable for hardware implementation. Compared with past super-resolution (SR) upsampling methods (e.g. example based upsampling), structure of our upsampling approach is very simple. Strategy of our approach is mainly 2 terms; off-line training term and realtime upscaling term. (i)During training term, grouping lowresolution (LR) - high-resolution (HR) patch pairs and determined a linear regression function of each groups. And (ii)during upscaling term, assigning pattern number to each of input LR patches according to the signature using a local binary pattern (LBP), and transforming input LR patches to HR patches by applying the trained regression function based on the LBP in a patch-by-patch fashion. Our evaluation result shows that our method is comparable to other state-of-the-art methods. Furthermore, our approach is compactly implemented on LSI (e.g. FPGAs) or be shorten the processing time on software because of simplicity of the structure.

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Kohei Kuriharaa, Yoshitaka Toyodaa, Shotaro Moriyab, Daisuke Suzukia, Takeo Fujita, Narihiro Matoba, Jay E Thorton, Fatih Poriklid, "Light-Weight Single Image Super-Resolution via Pattern-wise Regression Functionin Proc. IS&T Int’l. Symp. on Electronic Imaging: Digital Photography and Mobile Imaging XII,  2016,

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