Ntire 2020 challenge on real-world image super-resolution: Methods and results
A Lugmayr, M Danelljan… - Proceedings of the IEEE …, 2020 - openaccess.thecvf.com
This paper reviews the NTIRE 2020 challenge on real world super-resolution. It focuses on
the participating methods and final results. The challenge addresses the real world setting …
the participating methods and final results. The challenge addresses the real world setting …
Deep learning in medical image super resolution: a review
H Yang, Z Wang, X Liu, C Li, J Xin, Z Wang - Applied Intelligence, 2023 - Springer
Super-resolution (SR) reconstruction is a hot topic in medical image processing. SR implies
reconstructing corresponding high-resolution (HR) images from observed low-resolution …
reconstructing corresponding high-resolution (HR) images from observed low-resolution …
COLA-Net: Collaborative attention network for image restoration
Local and non-local attention-based methods have been well studied in various image
restoration tasks while leading to promising performance. However, most of the existing …
restoration tasks while leading to promising performance. However, most of the existing …
PGMAN: An unsupervised generative multiadversarial network for pansharpening
Pansharpening aims at fusing a low-resolution multispectral (MS) image and a high-
resolution (HR) panchromatic (PAN) image acquired by a satellite to generate an HR MS …
resolution (HR) panchromatic (PAN) image acquired by a satellite to generate an HR MS …
Unsupervised cycle-consistent generative adversarial networks for pan sharpening
Deep learning-based pan sharpening has received significant research interest in recent
years. Most of the existing methods fall into the supervised learning framework in which they …
years. Most of the existing methods fall into the supervised learning framework in which they …
Implementation of super resolution in images based on generative Adversarial network
KS Reddy, VP Vijayan, AD Gupta… - … on Smart Structures …, 2022 - ieeexplore.ieee.org
A 3D visualization of a microscopic object is provided by the integral imaging microscopy
system. A generative-adversarial-network (GAN) relied on super resolution (SR) algorithm is …
system. A generative-adversarial-network (GAN) relied on super resolution (SR) algorithm is …
Deep learning-based super-resolution for harmful algal bloom monitoring of inland water
Inland water frequently occurs during harmful algal blooms (HABs), rendering it challenging
to comprehend the spatiotemporal features of algal dynamics. Recently, remote sensing has …
to comprehend the spatiotemporal features of algal dynamics. Recently, remote sensing has …
A comprehensive review of generative adversarial networks: Fundamentals, applications, and challenges
M Megahed, A Mohammed - Wiley Interdisciplinary Reviews …, 2024 - Wiley Online Library
In machine learning, a generative model is responsible for generating new samples of data
in terms of a probabilistic model. Generative adversarial network (GAN) has been widely …
in terms of a probabilistic model. Generative adversarial network (GAN) has been widely …
Direct unsupervised super-resolution using generative adversarial network (DUS-GAN) for real-world data
K Prajapati, V Chudasama, H Patel… - … on Image Processing, 2021 - ieeexplore.ieee.org
The deep learning models for the Single Image Super-Resolution (SISR) task have found
success in recent years. However, one of the prime limitations of existing deep learning …
success in recent years. However, one of the prime limitations of existing deep learning …
Ultrasound speckle reduction using wavelet-based generative adversarial network
The visual quality of ultrasound (US) images is crucial for clinical diagnosis and treatment.
The main source of image quality degradation is the inherent speckle noise generated …
The main source of image quality degradation is the inherent speckle noise generated …