Scholarlink

<p>Image super-resolution aims to enhance low-resolution images by reconstructing high-resolution details. Despite significant advancements, existing methods often struggle with computational efficiency and model complexity. This paper introduces a novel efficient SR method based on a multi-feature aggregation attention mechanism. We propose a cross-feature attention module to refine feature interactions and a multi-feature aggregation attention module to redistribute feature weights. Our approach achieves state-of-the-art performance on benchmark datasets, significantly improving image clarity with fewer parameters. Here, we show a PSNR improvement of up to 0.41dB on the Set5 dataset at a 4x scaling factor, demonstrating the effectiveness of our method in balancing performance and computational efficiency. The source code can be downloaded at <a href="https://github.com/zxycs/MFAASR.">https://github.com/zxycs/MFAASR.</a></p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Multi-feature aggregation attention for efficient image super-resolution

  • 期刊论文

  • 发表日期：

    2025年12月19日

查看全文
  • Xiangyuan Zhu,
  • Wei Zhao,
  • Xuchong Liu,
  • Zheng Wu,
  • Sheng Ren

摘要

Image super-resolution aims to enhance low-resolution images by reconstructing high-resolution details. Despite significant advancements, existing methods often struggle with computational efficiency and model complexity. This paper introduces a novel efficient SR method based on a multi-feature aggregation attention mechanism. We propose a cross-feature attention module to refine feature interactions and a multi-feature aggregation attention module to redistribute feature weights. Our approach achieves state-of-the-art performance on benchmark datasets, significantly improving image clarity with fewer parameters. Here, we show a PSNR improvement of up to 0.41dB on the Set5 dataset at a 4x scaling factor, demonstrating the effectiveness of our method in balancing performance and computational efficiency. The source code can be downloaded at https://github.com/zxycs/MFAASR.