This paper reviews the NTIRE 2024 challenge on image super-resolution (x4) highlighting
the solutions proposed and the outcomes obtained. The challenge involves generating …

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

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 …

Transformer-based method has demonstrated promising performance in image super-
resolution tasks, due to its long-range and global aggregation capability. However, the …

Abstract Recently, Deep Convolutional Neural Networks have demonstrated high-quality
reconstruction in image super-resolution procedure. In this paper, we propose improved …

Abstract The eighth AI City Challenge highlighted the convergence of computer vision and
artificial intelligence in areas like retail warehouse settings and Intelligent Traffic Systems …

Abstract We introduce SUPIR (Scaling-UP Image Restoration) a groundbreaking image
restoration method that harnesses generative prior and the power of model scaling up …

The pre-trained text-to-image diffusion models have been increasingly employed to tackle
the real-world image super-resolution (Real-ISR) problem due to their powerful generative …

Abstract In recent years Vision Transformer-based approaches for low-level vision tasks
have achieved widespread success. Unlike CNN-based models Transformers are more …

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 …