Recently, the performances of deep convolution neural networks (CNNs)-based single-image super-resolution (SISR) have been significantly improved. However, most of the existing CNN-based SISR methods mainly focus on wider or deeper networks and ignore the potential relationship between multi-scale features, leading to the limited representation ability of the reconstructed network. To address this problem, we propose a new multi-scale information polymerization network (MIPN). Specifically, we propose a multi-scale information polymerization block (MIPB), which uses convolution layers of different convolution kernel sizes to extract multi-scale image features, and effectively polymerizate the extracted features together to obtain fine image features. Moreover, we also propose a shallow residual block in MIPB. Compared with the traditional convolution layer, this proposed block can effectively extract image features without increasing the number of parameters. Extensive experiments show that the proposed method performs better than several state-of-the-art methods in quantitative and visual quality indicators.

中文翻译：

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通过多尺度信息聚合网络实现单幅图像超分辨率

最近，基于深度卷积神经网络 (CNN) 的单图像超分辨率 (SISR) 的性能得到了显着提高。然而，现有的大多数基于 CNN 的 SISR 方法主要关注更广或更深的网络，而忽略了多尺度特征之间的潜在关系，导致重构网络的表示能力有限。为了解决这个问题，我们提出了一种新的多尺度信息聚合网络（MIPN）。具体来说，我们提出了一种多尺度信息聚合块（MIPB），它使用不同卷积核大小的卷积层来提取多尺度图像特征，并将提取的特征有效聚合在一起以获得精细的图像特征。此外，我们还在 MIPB 中提出了一个浅残差块。与传统的卷积层相比，这个提出的块可以在不增加参数数量的情况下有效地提取图像特征。大量实验表明，所提出的方法在定量和视觉质量指标方面的性能优于几种最先进的方法。