This paper reviews the NTIRE 2023 challenge on image super-resolution (x4), focusing on
the proposed solutions and results. The task of image super-resolution (SR) is to generate a …

At present, with the advance of satellite image processing technology, remote sensing
images are becoming more widely used in real scenes. However, due to the limitations of …

Transformer has recently gained considerable popularity in low-level vision tasks, including
image super-resolution (SR). These networks utilize self-attention along different …

Deep learning based approaches has achieved great performance in single image super-
resolution (SISR). However, recent advances in efficient super-resolution focus on reducing …

Recently, a number of CNN based methods have made great progress in single image
super-resolution. However, these existing architectures commonly build massive number of …

Stereo image super-resolution aims at enhancing the quality of super-resolution results by
utilizing the complementary information provided by binocular systems. To obtain …

Abstract We propose Super-resolution Neural Operator (SRNO), a deep operator learning
framework that can resolve high-resolution (HR) images at arbitrary scales from the low …

Self-similarity refers to the image prior widely used in image restoration algorithms that small
but similar patterns tend to occur at different locations and scales. However, recent …

Guided depth super-resolution (GDSR) is an essential topic in multi-modal image
processing, which reconstructs high-resolution (HR) depth maps from low-resolution ones …

ConvNets can compete with transformers in high-level tasks by exploiting larger receptive
fields. To unleash the potential of ConvNet in super-resolution we propose a multi-scale …