PSR-GAT: Arbitrary point cloud super-resolution using graph attention networks

Zhong, Fan; Bai, Zhengyao

doi:10.1007/s11042-023-16525-0

Multimed Tools Appl

2023

DOI: 10.1007/s11042-023-16525-0

|View full text |Cite

PSR-GAT: Arbitrary point cloud super-resolution using graph attention networks

Fan Zhong,

Zhengyao Bai

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Graph Convolutional Network for Image Restoration: A Survey

Cheng,

Bi,

et al. 2024

Mathematics

View full text Add to dashboard Cite

Image restoration technology is a crucial field in image processing and is extensively utilized across various domains. Recently, with advancements in graph convolutional network (GCN) technology, methods based on GCNs have increasingly been applied to image restoration, yielding impressive results. Despite these advancements, there is a gap in comprehensive research that consolidates various image denoising techniques. In this paper, we conduct a comparative study of image restoration techniques using GCNs. We begin by categorizing GCN methods into three primary application areas: image denoising, image super-resolution, and image deblurring. We then delve into the motivations and principles underlying various deep learning approaches. Subsequently, we provide both quantitative and qualitative comparisons of state-of-the-art methods using public denoising datasets. Finally, we discuss potential challenges and future directions, aiming to pave the way for further advancements in this domain. Our key findings include the identification of superior performance of GCN-based methods in capturing long-range dependencies and improving image quality across different restoration tasks, highlighting their potential for future research and applications.

show abstract

Graph Convolutional Network for Image Restoration: A Survey

Cheng,

Bi,

et al. 2024

Mathematics

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

PSR-GAT: Arbitrary point cloud super-resolution using graph attention networks

Cited by 1 publication

References 47 publications

Graph Convolutional Network for Image Restoration: A Survey

Graph Convolutional Network for Image Restoration: A Survey

Contact Info

Product

Resources

About