Enhancing Multi-Scale Implicit Learning in Image Super-Resolution with Integrated Positional Encoding

Abstract

Is the center position fully capable of representing a pixel? There is nothing wrong to represent pixels with their centers in a discrete image representation, but it makes more sense to consider each pixel as the aggregation of signals from a local area in an image super-resolution (SR) context. Despite the great capability of coordinate-based implicit representation in the field of arbitrary-scale image SR, this area's nature of pixels is not fully considered. To this end, we propose integrated positional encoding (IPE), extending traditional positional encoding by aggregating frequency information over the pixel area. We apply IPE to the state-of-the-art arbitrary-scale image super-resolution method: local implicit image function (LIIF), presenting IPE-LIIF. We show the effectiveness of IPE-LIIF by quantitative and qualitative evaluations, and further demonstrate the generalization ability of IPE to larger image scales and multiple implicit-based methods. Code will be released.

Resolution-Agnostic Remote Sensing Scene Classification With Implicit Neural Representations

“…Inspired by INR, LIIF [32] designs a local implicit image function to achieve continuous image SR, which takes the coordinates and nearby feature representations as input and outputs the RGB value of the corresponding location. Based on LIIF, IPE-LIIF [51] aggregates local frequency information by positional encoding to improve SR performance. UltraSR [33] deeply integrates coordinate encoding with implicit neural representations to improve the accuracy of high-frequency textures.…”