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

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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