An Efficient Group Feature Fusion Residual Network for Image Super-Resolution

Pengcheng Lei; Cong Liu

doi:10.1007/978-3-030-69532-3_14

ScienceGate Book Chapters

BOOK-CHAPTER

An Efficient Group Feature Fusion Residual Network for Image Super-Resolution

Pengcheng Lei Cong Liu

Year: 2021 Lecture notes in computer science Pages: 217-233 Publisher: Springer Science+Business Media

DOI: 10.1007/978-3-030-69532-3_14

Get Full-Text PDF Get Analytical Report

Keywords:

Residual Computer science Convolution (computer science) Fuse (electrical) Benchmark (surveying) Convolutional neural network Feature (linguistics) Artificial intelligence Block (permutation group theory) Image (mathematics) Pattern recognition (psychology) Image fusion Group (periodic table) Artificial neural network Algorithm Mathematics Engineering

Metrics

Cited By

0.35

FWCI (Field Weighted Citation Impact)

Refs

0.45

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

An Efficient Group Feature Fusion Residual Network for Image Super-Resolution

Metrics

Citation History

Topics

Related Documents

A Cascaded Feature Fusion Residual Network for Image Super-resolution

Residual Feature Fusion Attention Network For Single Image Super-resolution

Multi-scale feature fusion residual network for Single Image Super-Resolution

Lightweight hierarchical residual feature fusion network for single-image super-resolution

Efficient Attention Fusion Feature Extraction Network for Image Super-Resolution