NTIRE 2022 challenge on learning the super-resolution space
This paper reviews the NTIRE 2022 challenge on learning the super-Resolution space. This
challenge aims to raise awareness that the super-resolution problem is ill-posed. Since …
challenge aims to raise awareness that the super-resolution problem is ill-posed. Since …
NTIRE 2021 learning the super-resolution space challenge
A Lugmayr, M Danelljan… - Proceedings of the IEEE …, 2021 - openaccess.thecvf.com
This paper reviews the NTIRE 2021 challenge on learning the super-Resolution space. It
focuses on the participating methods and final results. The challenge addresses the problem …
focuses on the participating methods and final results. The challenge addresses the problem …
Scaling up to excellence: Practicing model scaling for photo-realistic image restoration in the wild
Abstract We introduce SUPIR (Scaling-UP Image Restoration) a groundbreaking image
restoration method that harnesses generative prior and the power of model scaling up …
restoration method that harnesses generative prior and the power of model scaling up …
Learning dynamic generative attention for single image super-resolution
R Chen, Y Zhang - IEEE Transactions on Circuits and Systems …, 2022 - ieeexplore.ieee.org
Attention mechanisms have achieved great success for image super-resolution as they can
effectively improve the feature representation ability. However, most attention-based …
effectively improve the feature representation ability. However, most attention-based …
Text-guided Explorable Image Super-resolution
KV Gandikota, P Chandramouli - Proceedings of the IEEE …, 2024 - openaccess.thecvf.com
In this paper we introduce the problem of zero-shot text-guided exploration of the solutions
to open-domain image super-resolution. Our goal is to allow users to explore diverse …
to open-domain image super-resolution. Our goal is to allow users to explore diverse …
Training Generative Image Super-Resolution Models by Wavelet-Domain Losses Enables Better Control of Artifacts
Super-resolution (SR) is an ill-posed inverse problem where the size of the set of feasible
solutions that are consistent with a given low-resolution image is very large. Many …
solutions that are consistent with a given low-resolution image is very large. Many …
Reasons for the superiority of stochastic estimators over deterministic ones: Robustness, consistency and perceptual quality
Stochastic restoration algorithms allow to explore the space of solutions that correspond to
the degraded input. In this paper we reveal additional fundamental advantages of stochastic …
the degraded input. In this paper we reveal additional fundamental advantages of stochastic …
Invertible monotone operators for normalizing flows
Normalizing flows model probability distributions by learning invertible transformations that
transfer a simple distribution into complex distributions. Since the architecture of ResNet …
transfer a simple distribution into complex distributions. Since the architecture of ResNet …
DiSR-NeRF: Diffusion-Guided View-Consistent Super-Resolution NeRF
Abstract We present DiSR-NeRF a diffusion-guided framework for view-consistent super-
resolution (SR) NeRF. Unlike prior works we circumvent the requirement for high-resolution …
resolution (SR) NeRF. Unlike prior works we circumvent the requirement for high-resolution …
Asymmetric dual-direction quasi-recursive network for single hyperspectral image super-resolution
H Wang, C Wang, Y Yuan - … on Circuits and Systems for Video …, 2023 - ieeexplore.ieee.org
Single hyperspectral image super-resolution aims to reconstruct a high-resolution
hyperspectral image from a low-resolution one, which does not use any auxiliary images …
hyperspectral image from a low-resolution one, which does not use any auxiliary images …