Light-weight single image super-resolution via pattern-wise regression function

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.