Recently, numerous methods based on convolutional neural networks (CNNs) have been proposed to attain satisfactory performance in single image super-resolution (SISR). Meanwhile, diverse lightweight CNN-based networks have been proposed to achieve applicability in real-time scenarios. However, the receptive fields in lightweight networks are limited because they do not make good use of multi-scale information. In this paper, we propose a lightweight multi-scale feature integration network (MFIN) to address the above issue. Specifically, to expand the receptive fields for global features, MFIN is constructed by cascading the multi-scale feature integration blocks (MFIBs) in a serial manner. Each MFIB contains a multi-scale feature extraction module (MFEM) and a feature integration unit (FIU). To enlarge the receptive fields at a granular level, the features in MFEM are cascaded in a parallel manner. To capture the full-image dependencies, FIU incorporates the dense and pixel-wise correlations from the outputs of MFEM efficiently. The conducted experiments demonstrate that our method outperforms state-of-the-art methods in quantitative and qualitative evaluation. Notably, the experimental results on running time state that our method can achieve real-time performance.

最近，已经提出了许多基于卷积神经网络 (CNN) 的方法来在单图像超分辨率 (SISR) 中获得令人满意的性能。同时，已经提出了多种基于 CNN 的轻量级网络以实现在实时场景中的适用性。然而，轻量级网络中的感受野是有限的，因为它们没有很好地利用多尺度信息。在本文中，我们提出了一个轻量级的多尺度特征集成网络（MFIN）来解决上述问题。具体来说，为了扩展全局特征的感受野，MFIN 是通过以串行方式级联多尺度特征集成块 (MFIB) 来构建的。每个 MFIB 包含一个多尺度特征提取模块 (MFEM) 和一个特征集成单元 (FIU)。为了在粒度级别扩大感受野，MFEM 中的特征以并行方式级联。为了捕获完整图像的依赖关系，FIU 有效地结合了来自 MFEM 输出的密集和像素相关性。进行的实验表明，我们的方法在定量和定性评估方面优于最先进的方法。值得注意的是，运行时间的实验结果表明我们的方法可以实现实时性能。