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 …

This paper reviews the Challenge on Blind Enhancement of Compressed Image at NTIRE
2024 which aims at enhancing the quality of JPEG images which are compressed with …

Recent years have seen significant advancements in image restoration, largely attributed to
the development of modern deep neural networks, such as CNNs and Transformers …

We present a novel approach to leverage prior knowledge encapsulated in pre-trained text-
to-image diffusion models for blind super-resolution. Specifically, by employing our time …

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

Image super-resolution (SR) has attracted increasing attention due to its wide applications.
However, current SR methods generally suffer from over-smoothing and artifacts, and most …

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 …

Diffusion model (DM) has achieved SOTA performance by modeling the image synthesis
process into a sequential application of a denoising network. However, different from image …

Recently, transformer-based methods have demonstrated impressive results in various
vision tasks, including image super-resolution (SR), by exploiting the self-attention (SA) for …

Previous works have shown that increasing the window size for Transformer-based image
super-resolution models (eg, SwinIR) can significantly improve the model performance but …