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<p>In recent years, convolutional neural network has been widely used in single image super-resolution. As the depth of the network increases, it is difficult to effectively balance the convergence of training with the improvement of performance. Therefore, in this paper, we propose a multi-branch image super-resolution network based on spatial and channel reconstruction: MB-SCRSR. In MB-SCRSR, we construct a multi branch network to better extract nonlinear features in the feature space. Additionally, the network introduces a fusion attention unit to effectively fuse the extracted features across branches, thereby capturing more relevant information. Finally, MB-SCRSR incorporates spatial and channel reconstruction blocks to reduce redundancy in both spatial and channel dimensions. Experimental results demonstrate that MB-SCRSR outperforms state-of-the-art models in terms of objective evaluation metrics, with a peak signal-to-noise ratio improvement ranging from 0.1 to 0.6&#xa0;dB.</p>

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MB-SCRSR: A Multi-branch Image Super-Resolution Network Based on Spatial and Channel Reconstruction

  • 期刊论文

  • 发表日期：

    2025年3月31日

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  • Panpan Zhang,
  • Xin Yang,
  • Tingyu Xia

摘要

In recent years, convolutional neural network has been widely used in single image super-resolution. As the depth of the network increases, it is difficult to effectively balance the convergence of training with the improvement of performance. Therefore, in this paper, we propose a multi-branch image super-resolution network based on spatial and channel reconstruction: MB-SCRSR. In MB-SCRSR, we construct a multi branch network to better extract nonlinear features in the feature space. Additionally, the network introduces a fusion attention unit to effectively fuse the extracted features across branches, thereby capturing more relevant information. Finally, MB-SCRSR incorporates spatial and channel reconstruction blocks to reduce redundancy in both spatial and channel dimensions. Experimental results demonstrate that MB-SCRSR outperforms state-of-the-art models in terms of objective evaluation metrics, with a peak signal-to-noise ratio improvement ranging from 0.1 to 0.6 dB.