With the development of deep learning (DL), convolutional neural networks (CNNs) have shown great reconstruction performance in single image super-resolution (SISR). However, some methods blindly deepen the networks to purchase the performance, which neglect to make full use of the multi-scale information of different receptive fields and ignore the efficiency in practice. In this paper, a lightweight SISR network with multi-scale information fusion blocks (MIFB) is proposed to fully extract information via a multiple ranges of receptive fields. The features are refined in a coarse-to-fine manner within each block. Group convolutional layers are employed in each block to reduce the number of parameters and operations. Results of extensive experiments on the benchmarks show that our method achieves better performance than the state-of-the-arts with comparable parameters and multiply–accumulate (MAC) operations.

中文翻译：

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LMSN：用于单图像超分辨率的轻量级多尺度网络

随着深度学习 (DL) 的发展，卷积神经网络 (CNN) 在单幅图像超分辨率 (SISR) 中表现出出色的重建性能。然而，一些方法一味地深化网络以购买性能，忽视了对不同感受野的多尺度信息的充分利用，在实践中忽视了效率。在本文中，提出了一种具有多尺度信息融合块（MIFB）的轻量级 SISR 网络，以通过多个感受野范围充分提取信息。在每个块内以从粗到细的方式细化特征。在每个块中使用组卷积层以减少参数和操作的数量。