Super-resolution (SR) is an essential class of low-level vision tasks, which aims to improve
the resolution of images or videos in computer vision. In recent years, significant progress …

Abstract The recent work Local Implicit Image Function (LIIF) and subsequent Implicit Neural
Representation (INR) based works have achieved remarkable success in Arbitrary-Scale …

In this work, we present an arbitrary-scale super-resolution (SR) method to enhance the
resolution of scientific data, which often involves complex challenges such as continuity …

The real world exhibits rich structure and detail across many scales of observation. It is
difficult, however, to capture and represent a broad spectrum of scales using ordinary …

Previous work has shown that CNN-based local distribution learning can efficiently
reconstruct high-resolution images, but with limited performance improvement against …

Arbitrary-scale video super-resolution (AVSR) aims to enhance the resolution of video
frames, potentially at various scaling factors, which presents several challenges regarding …

Scale arbitrary super-resolution based on implicit image function gains increasing popularity
since it can better represent the visual world in a continuous manner. However, existing …

In an effort to improve the efficiency and scalability of single-image super-resolution (SISR)
applications, we introduce AnySR, to rebuild existing arbitrary-scale SR methods into any …

Multispectral and Hyperspectral Image Fusion (MHIF) is a practical task that aims to fuse a
high-resolution multispectral image (HR-MSI) and a low-resolution hyperspectral image (LR …

Recently, several arbitrary-scale models have been proposed for single-image super-
resolution. Furthermore, the importance of arbitrary-scale single image super-resolution is …