research-article

CLG-INet: Coupled Local-Global Interactive Network for Image Restoration

Authors:

Jiapeng WangAuthors Info & Claims

MM '23: Proceedings of the 31st ACM International Conference on Multimedia

Pages 7580 - 7589

https://doi.org/10.1145/3581783.3612251

Published: 27 October 2023 Publication History

Abstract

Image restoration is an ill-posed problem due to the infinite feasible solutions for degraded images. Although CNN-based and Transformer-based approaches have been proven effective in image restoration, there are still two challenges in restoring complex degraded images: 1)local-global information extraction and fusion, and 2)computational cost overhead. To address these challenges, in this paper, we propose a lightweight image restoration network (CLG-INet) based on CNN-Transformer interaction, which can efficiently couple the local and global information. Specifically, our model is hierarchically built with a "sandwich-like" structure of coupling blocks, where each block contains three layers in sequence (CNN-Transformer-CNN). The Transformer layer is designed with two core modules: Dynamic Bi-Projected Attention (DBPA), which performs dual projection with large convolutions across windows to capture long-range dependencies, and Gated Non-linear Feed-Forward Network (GNFF), which reconstructs mixed feature information. In addition, we introduce interactive learning, which fuses local features and global representations in different resolutions to the maximum extent. Extensive experiments demonstrate that CLG-INet significantly boosts performance on various image restoration tasks, such as deraining, deblurring, and denoising.

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Cited By

Wu DGuo CLi RMa Z(2024)Cross-Scale Feature Blending Model for Surface Defect Identification in Machine Tool Elements Resilient to Contaminant InterferenceIEEE Access10.1109/ACCESS.2024.350922512(178022-178037)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3509225

Index Terms

CLG-INet: Coupled Local-Global Interactive Network for Image Restoration
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Reconstruction

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cover image ACM Conferences

MM '23: Proceedings of the 31st ACM International Conference on Multimedia

October 2023

9913 pages

ISBN:9798400701085

DOI:10.1145/3581783

General Chairs:
Abdulmotaleb El Saddik
University of Ottawa, Canada & MBZUAI, UAE
,
Tao Mei
HiDream.ai, China
,
Rita Cucchiara
University of Modena and Reggio Emilia, Italy
,
Program Chairs:
Marco Bertini
University of Florence, Italy
,
Diana Patricia Tobon Vallejo
Unversidad de Medellin, Colombia
,
Pradeep K. Atrey
University at Albany, State University of New York, USA
,
M. Shamim Hossain
M. Shamim Hossain (King Saud University, KSA

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Published: 27 October 2023

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MM '23: The 31st ACM International Conference on Multimedia

October 29 - November 3, 2023

Ottawa ON, Canada

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Cited By

Wu DGuo CLi RMa Z(2024)Cross-Scale Feature Blending Model for Surface Defect Identification in Machine Tool Elements Resilient to Contaminant InterferenceIEEE Access10.1109/ACCESS.2024.350922512(178022-178037)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3509225

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