research-article

Enhancing Real-Time Super Resolution with Partial Convolution and Efficient Variance Attention

Authors:

Zhengfeng YangAuthors Info & Claims

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

Pages 5348 - 5357

https://doi.org/10.1145/3581783.3611729

Published: 27 October 2023 Publication History

Abstract

With the increasing availability of devices that support ultra-high-definition (UHD) images, Single Image Super Resolution (SISR) has emerged as a crucial problem in the field of computer vision. In recent years, CNN-based super resolution approaches have made significant advances, producing high-quality upscaled images. However, these methods can be computationally and memory intensive, making them impractical for real-time applications such as upscaling to UHD images. The performance and reconstruction quality may suffer due to the complexity and diversity of larger image content. Therefore, there is a need to develop efficient super resolution approaches that can meet the demands of processing high-resolution images. In this paper, we propose a simple network named PCEVAnet by constructing the PCEVA block, which leverages Partial Convolution and Efficient Variance Attention. Partial Convolution is employed to streamline the feature extraction process by minimizing memory access. And Efficient Variance Attention (EVA) captures the high-frequency information and long-range dependency via the variance and max pooling. We conduct extensive experiments to demonstrate that our model achieves a better trade-off between performance and actual running time than previous methods.

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Cited By

Lin JTao ZTong XMai XWang HWang BWang YZhao QYu JLin YYan SGao SZhang WCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Suppressing Uncertainties in Degradation Estimation for Blind Super-ResolutionProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681439(6374-6383)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681439
Wang YLi YWang GLiu X(2024)PlainUSR: Chasing Faster ConvNet for Efficient Super-ResolutionComputer Vision – ACCV 202410.1007/978-981-96-0911-6_15(246-264)Online publication date: 8-Dec-2024
https://doi.org/10.1007/978-981-96-0911-6_15

Index Terms

Enhancing Real-Time Super Resolution with Partial Convolution and Efficient Variance Attention
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
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Tao Mei
HiDream.ai, China
,
Rita Cucchiara
University of Modena and Reggio Emilia, Italy
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Program Chairs:
Marco Bertini
University of Florence, Italy
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Diana Patricia Tobon Vallejo
Unversidad de Medellin, Colombia
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Pradeep K. Atrey
University at Albany, State University of New York, USA
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M. Shamim Hossain
M. Shamim Hossain (King Saud University, KSA

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Published: 27 October 2023

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Lin JTao ZTong XMai XWang HWang BWang YZhao QYu JLin YYan SGao SZhang WCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Suppressing Uncertainties in Degradation Estimation for Blind Super-ResolutionProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681439(6374-6383)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681439
Wang YLi YWang GLiu X(2024)PlainUSR: Chasing Faster ConvNet for Efficient Super-ResolutionComputer Vision – ACCV 202410.1007/978-981-96-0911-6_15(246-264)Online publication date: 8-Dec-2024
https://doi.org/10.1007/978-981-96-0911-6_15

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