  Implicit neural representation (INR) is a popular approach for
arbitrary-scale image super-resolution (SR), as a key component of INR,
position encoding improves its representation ability. Motivated by position
encoding, we propose orthogonal position encoding (OPE) - an extension of
position encoding - and an OPE-Upscale module to replace the INR-based
upsampling module for arbitrary-scale image super-resolution. Same as INR, our
OPE-Upscale Module takes 2D coordinates and latent code as inputs; however it
does not require training parameters. This parameter-free feature allows the
OPE-Upscale Module to directly perform linear combination operations to
reconstruct an image in a continuous manner, achieving an arbitrary-scale image
reconstruction. As a concise SR framework, our method has high computing
efficiency and consumes less memory comparing to the state-of-the-art (SOTA),
which has been confirmed by extensive experiments and evaluations. In addition,
our method has comparable results with SOTA in arbitrary scale image
super-resolution. Last but not the least, we show that OPE corresponds to a set
of orthogonal basis, justifying our design principle.
