This paper reviews the NTIRE 2023 challenge on efficient single-image super-resolution
with a focus on the proposed solutions and results. The aim of this challenge is to devise a …

Transformer-based methods have shown impressive performance in low-level vision tasks,
such as image super-resolution. However, we find that these networks can only utilize a …

This paper provides a comprehensive review of the NTIRE 2024 challenge, focusing on
efficient single-image super-resolution (ESR) solutions and their outcomes. The task of this …

Deep convolutional neural networks can effectively improve the performance of single-
image super-resolution reconstruction. Deep networks tend to achieve better performance …

This report introduces two high-quality datasets Flickr360 and ODV360 for omnidirectional
image and video super-resolution, respectively, and reports the NTIRE 2023 challenge on …

Recently, Transformer architecture has been introduced into image restoration to replace
convolution neural network (CNN) with surprising results. Considering the high …

The attention mechanism plays a pivotal role in designing advanced super-resolution (SR)
networks. In this work, we design an efficient SR network by improving the attention …

Recently, Transformer-based image restoration networks have achieved promising
improvements over convolutional neural networks due to parameter-independent global …

Vision Transformers have shown promising performance in image restoration, which usually
conduct window-or channel-based attention to avoid intensive computations. Although the …

Omnidirectional images (ODIs) have obtained lots of research interest for immersive
experiences. Although ODIs require extremely high resolution to capture details of the entire …