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 …

Recently, the proposed deep multilayer perceptron (MLP) models have stirred up a lot of
interest in the vision community. Historically, the availability of larger datasets combined with …

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 …

Although there have been significant advances in the field of image restoration recently, the
system complexity of the state-of-the-art (SOTA) methods is increasing as well, which may …

Transformers have recently gained significant attention in the computer vision community.
However, the lack of scalability of self-attention mechanisms with respect to image size has …

Recent progress in vision Transformers exhibits great success in various tasks driven by the
new spatial modeling mechanism based on dot-product self-attention. In this paper, we …

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

The aim of this paper is to propose a mechanism to efficiently and explicitly model image
hierarchies in the global, regional, and local range for image restoration. To achieve that, we …

In this paper, we investigate the application of Vehicle-to-Everything (V2X) communication to
improve the perception performance of autonomous vehicles. We present a robust …

Considering the ill-posed nature, contrastive regularization has been developed for single
image dehazing, introducing the information from negative images as a lower bound …