Existing studies have empirically observed that the resolution of the low-frequency region is
easier to enhance than that of the high-frequency one. Although plentiful works have been …

Image super-resolution research recently has been dominated by transformer models which
need higher computational resources than CNNs due to the quadratic complexity of self …

Deep learning techniques have been successfully applied in many areas of computer vision,
including low-level image restoration problems. For image super-resolution, several models …

For image super-resolution (SR) bridging the gap between the performance on synthetic
datasets and real-world degradation scenarios remains a challenge. This work introduces a …

Single image super resolution (SISR) is to reconstruct a high resolution image from a single
low resolution image. The SISR task has been a very attractive research topic over the last …

Deep neural networks have exhibited promising performance in image super-resolution
(SR) by learning a nonlinear mapping function from low-resolution (LR) images to high …

Owe to the powerful generative priors the pre-trained text-to-image (T2I) diffusion models
have become increasingly popular in solving the real-world image super-resolution …

This paper proposes a novel Attention-based Multi-Reference Super-resolution network
(AMRSR) that, given a low-resolution image, learns to adaptively transfer the most similar …

Convolutional neural networks (CNNs) have allowed remarkable advances in single image
super-resolution (SISR) over the last decade. Most SR methods based on CNNs have …

Current CNN-based super-resolution (SR) methods process all locations equally with
computational resources being uniformly assigned in space. However, since missing details …