@article{01GWVMD8F4WW4T6MRZSNB99JZD,
  abstract     = {{Recently, Convolutional Neural Networks (CNNs) have achieved great success in Single Image Super-Resolution (SISR). In particular, the recursive networks are now widely used. However, existing recursion-based SISR networks can only make use of multi-scale features in a layer-wise manner. In this paper, a Deep Recursive Multi-Scale Feature Fusion Network (DRMSFFN) is proposed to address this issue. Specifically, we propose a Recursive Multi-Scale Feature Fusion Block (RMSFFB) to make full use of multi-scale features. Besides, a Progressive Feature Fusion (PFF) technique is proposed to take advantage of the hierarchical features from the RMSFFB in a global manner. At the reconstruction stage, we use a deconvolutional layer to upscale the feature maps to the desired size. Extensive experimental results on benchmark datasets demonstrate the superiority of the proposed DRMSFFN in comparison with the state-of-the-art methods in both quantitative and qualitative evaluations.}},
  articleno    = {{103730}},
  author       = {{Liu, Feiqiang and  Yang, Xiaomin and De Baets, Bernard}},
  issn         = {{1047-3203}},
  journal      = {{JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION}},
  keywords     = {{INTERPOLATION,Single Image Super-Resolution (SISR),Recursive networks,Multi-scale,features,Progressive feature fusion}},
  language     = {{eng}},
  pages        = {{10}},
  title        = {{A deep recursive multi-scale feature fusion network for image super-resolution?}},
  url          = {{http://doi.org/10.1016/j.jvcir.2022.103730}},
  volume       = {{90}},
  year         = {{2023}},
}

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