Lightweight multi-scale aggregated residual attention networks for image super-resolution

Shurong Pang; Zhe Chen; Fuliang Yin

doi:10.1007/s11042-021-11138-x

ScienceGate Book Chapters

JOURNAL ARTICLE

Lightweight multi-scale aggregated residual attention networks for image super-resolution

Shurong Pang Zhe Chen Fuliang Yin

Year: 2021 Journal: Multimedia Tools and Applications Vol: 81 (4)Pages: 4797-4819 Publisher: Springer Science+Business Media

DOI: 10.1007/s11042-021-11138-x

Get Full-Text PDF Get Analytical Report

Keywords:

Computer science Residual Benchmark (surveying) Convolutional neural network Block (permutation group theory) Scale (ratio) Pattern recognition (psychology) Convolution (computer science) Artificial intelligence Feature (linguistics) Feature extraction Image (mathematics) Exploit Data mining Algorithm Artificial neural network Mathematics

Metrics

Cited By

1.23

FWCI (Field Weighted Citation Impact)

Refs

0.81

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Advanced Image Processing Techniques

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Image Processing Techniques and Applications

Physical Sciences → Engineering → Media Technology

Advanced Vision and Imaging

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Lightweight multi-scale aggregated residual attention networks for image super-resolution

Metrics

Citation History

Topics

Related Documents

Lightweight multi-scale residual networks with attention for image super-resolution

Lightweight Single Image Super-Resolution With Multi-Scale Spatial Attention Networks

Image Super-Resolution Using Aggregated Residual Transformation Networks With Spatial Attention

Mixed multi-scale residual attention networks for single image super-resolution reconstruction

Multi-Scale Residual Attention Network for Image Super-Resolution