Fine-grained video super-resolution via spatial-temporal learning and image detail enhancement-Reference-Cited by-同舟云学术

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Fine-grained video super-resolution via spatial-temporal learning and image detail enhancement

Published:2024-05 Issue: Volume:131 Page:107789
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Yeh Chia-Hung^ORCID,Yang Hsin-Fu,Lin Yu-Yang,Huang Wan-Jen^ORCID,Tsai Feng-Hsu,Kang Li-Wei^ORCID

Funder

National Science and Technology Council

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Artificial Intelligence,Control and Systems Engineering

Reference87 articles.

1. Breast tumor classification in ultrasound images using texture analysis and super-resolution methods;Abdel-Nasser;Eng. Appl. Artif. Intell.,2017

2. Segnet: a deep convolutional encoder-decoder architecture for image segmentation;Badrinarayanan;IEEE Trans. Pattern Anal. Mach. Intell.,2017

3. A. A. Baniya, T. -K. Lee, P. W. Eklund and S. Aryal, "Omnidirectional video super-resolution using deep learning." IEEE Trans. Multimed. .

4. Online video super-resolution using information replenishing unidirectional recurrent model;Baniya;Neurocomputing,2023

5. Real-time video super-resolution with spatio-temporal networks and motion compensation;Caballero,2017

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