research-article

Image Super-Resolution via Lightweight Attention-Directed Feature Aggregation Network

Authors:

Yuying LiangAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications and Applications, Volume 19, Issue 2

Article No.: 60, Pages 1 - 23

https://doi.org/10.1145/3546076

Published: 06 February 2023 Publication History

Abstract

The advent of convolutional neural networks (CNNs) has brought substantial progress in image super-resolution (SR) reconstruction. However, most SR methods pursue deep architectures to boost performance, and the resulting large model sizes are impractical for real-world applications. Furthermore, they insufficiently explore the internal structural information of image features, disadvantaging the restoration of fine texture details. To solve these challenges, we propose a lightweight architecture based on a CNN named attention-directed feature aggregation network (AFAN), consisting of chained stacking multi-aware attention modules (MAAMs) and a simple channel attention module (SCAM), for image SR. Specifically, in each MAAM, we construct a space-aware attention block (SAAB) and a dimension-aware attention block (DAAB) that individually yield unique three-dimensional modulation coefficients to adaptively recalibrate structural information from an asymmetric convolution residual block (ACRB). The synergistic strategy captures multiple content features that are both space-aware and dimension-aware to preserve more fine-grained details. In addition, to further enhance the accuracy and robustness of the network, SCAM is embedded in the last MAAM to highlight channels with high activated values at low computational load. Comprehensive experiments verify that our proposed network attains high qualitative accuracy while employing fewer parameters and moderate computational requirements, exceeding most state-of-the-art lightweight approaches.

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Index Terms

Image Super-Resolution via Lightweight Attention-Directed Feature Aggregation Network
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Reconstruction

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Published In

cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 19, Issue 2

March 2023

540 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3572860

Editor:
Abdulmotaleb El Saddik
Mohamed Bin Zayed University of Artificial Intelligence, UAE and University of Ottawa, Canada

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Publication History

Published: 06 February 2023

Online AM: 30 June 2022

Accepted: 23 June 2022

Revised: 08 June 2022

Received: 21 January 2022

Published in TOMM Volume 19, Issue 2

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National Natural Science Foundation of China
Science and Technology Project of Jiangxi Provincial Education Department
Nanchang Key Laboratory Construction Project

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https://dl.acm.org/doi/10.1155/2024/3255233
Zhang DZhu WLiao XQi FYang GDing X(2024)Spatiotemporal Inconsistency Learning and Interactive Fusion for Deepfake Video DetectionACM Transactions on Multimedia Computing, Communications, and Applications10.1145/3664654Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3664654
Zhang PLiu MSong XCao DGao ZNie L(2024)Universal Relocalizer for Weakly Supervised Referring Expression GroundingACM Transactions on Multimedia Computing, Communications, and Applications10.1145/365604520:7(1-23)Online publication date: 16-May-2024
https://dl.acm.org/doi/10.1145/3656045
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