Scholarlink

<p>Single-image super-resolution reconstruction aims to convert low-resolution images into high-resolution ones. Existing research primarily focuses on deep network architectures to improve image reconstruction quality. Although these methods achieve some performance improvements, they also lead to increases in computational cost and resource consumption. In recent years, with the rise of Transformers, more researchers have begun exploring their potential for image super-resolution reconstruction. While Transformers show great promise in capturing global features and enhancing image processing capabilities, their high computational cost remains a challenge. To address these issues, this paper proposes a Hybrid Lightweight Convolution-Transformer Network for Image Super-Resolution (LCTSR). The model includes two modules: Lightweight Feature Extraction Block (LFEB) and Lightweight Enhanced Transformer Block (LETB), which dynamically adjust feature map sizes while maintaining a lightweight design to extract deep features. Additionally, we introduce a Scale-Aware Attention Mechanism (SAAM) to more effectively integrate information. Experimental results demonstrate that LCTSR outperforms state-of-the-art models in terms of objective evaluation metrics, with a Peak Signal-to-Noise Ratio (PSNR) improvement ranging from 0.1 to 0.5&#xa0;dB. Additionally, our method shows a lower computational cost (FLOPs is 49.8G).</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

LCTSR: A Hybrid Lightweight Convolution-Transformer Network for Image Super-Resolution

  • 期刊论文

  • 发表日期：

    2026年4月10日

查看全文
  • Panpan Zhang,
  • Xin Yang,
  • Chaming Hong

摘要

Single-image super-resolution reconstruction aims to convert low-resolution images into high-resolution ones. Existing research primarily focuses on deep network architectures to improve image reconstruction quality. Although these methods achieve some performance improvements, they also lead to increases in computational cost and resource consumption. In recent years, with the rise of Transformers, more researchers have begun exploring their potential for image super-resolution reconstruction. While Transformers show great promise in capturing global features and enhancing image processing capabilities, their high computational cost remains a challenge. To address these issues, this paper proposes a Hybrid Lightweight Convolution-Transformer Network for Image Super-Resolution (LCTSR). The model includes two modules: Lightweight Feature Extraction Block (LFEB) and Lightweight Enhanced Transformer Block (LETB), which dynamically adjust feature map sizes while maintaining a lightweight design to extract deep features. Additionally, we introduce a Scale-Aware Attention Mechanism (SAAM) to more effectively integrate information. Experimental results demonstrate that LCTSR outperforms state-of-the-art models in terms of objective evaluation metrics, with a Peak Signal-to-Noise Ratio (PSNR) improvement ranging from 0.1 to 0.5 dB. Additionally, our method shows a lower computational cost (FLOPs is 49.8G).