NTIRE 2023 challenge on efficient super-resolution: Methods and results
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 …
with a focus on the proposed solutions and results. The aim of this challenge is to devise a …
Ntire 2017 challenge on single image super-resolution: Methods and results
This paper reviews the first challenge on single image super-resolution (restoration of rich
details in an low resolution image) with focus on proposed solutions and results. A new …
details in an low resolution image) with focus on proposed solutions and results. A new …
Deep learning for multigrade brain tumor classification in smart healthcare systems: A prospective survey
Brain tumor is one of the most dangerous cancers in people of all ages, and its grade
recognition is a challenging problem for radiologists in health monitoring and automated …
recognition is a challenging problem for radiologists in health monitoring and automated …
Super-resolution reconstruction of turbulent flows with machine learning
We use machine learning to perform super-resolution analysis of grossly under-resolved
turbulent flow field data to reconstruct the high-resolution flow field. Two machine learning …
turbulent flow field data to reconstruct the high-resolution flow field. Two machine learning …
Learning a single convolutional super-resolution network for multiple degradations
Recent years have witnessed the unprecedented success of deep convolutional neural
networks (CNNs) in single image super-resolution (SISR). However, existing CNN-based …
networks (CNNs) in single image super-resolution (SISR). However, existing CNN-based …
Learning data-driven discretizations for partial differential equations
The numerical solution of partial differential equations (PDEs) is challenging because of the
need to resolve spatiotemporal features over wide length-and timescales. Often, it is …
need to resolve spatiotemporal features over wide length-and timescales. Often, it is …
Ntire 2017 challenge on single image super-resolution: Dataset and study
E Agustsson, R Timofte - Proceedings of the IEEE …, 2017 - openaccess.thecvf.com
This paper introduces a novel large dataset for example-based single image super-
resolution and studies the state-of-the-art as emerged from the NTIRE 2017 challenge. The …
resolution and studies the state-of-the-art as emerged from the NTIRE 2017 challenge. The …
Classsr: A general framework to accelerate super-resolution networks by data characteristic
We aim at accelerating super-resolution (SR) networks on large images (2K-8K). The large
images are usually decomposed into small sub-images in practical usages. Based on this …
images are usually decomposed into small sub-images in practical usages. Based on this …
Frame-recurrent video super-resolution
MSM Sajjadi, R Vemulapalli… - Proceedings of the IEEE …, 2018 - openaccess.thecvf.com
Recent advances in video super-resolution have shown that convolutional neural networks
combined with motion compensation are able to merge information from multiple low …
combined with motion compensation are able to merge information from multiple low …
Enhancenet: Single image super-resolution through automated texture synthesis
MSM Sajjadi, B Scholkopf… - Proceedings of the IEEE …, 2017 - openaccess.thecvf.com
Single image super-resolution is the task of inferring a high-resolution image from a single
low-resolution input. Traditionally, the performance of algorithms for this task is measured …
low-resolution input. Traditionally, the performance of algorithms for this task is measured …