Deep convolutional neural networks have recently made a considerable achievement in the single-image superresolution (SISR) problem. Most CNN architectures for SISR incorporate long or short connections to integrate features, and treat them equally. However, they neglect the discrimination of features, and consequently, achieving relatively poor performance. To address this problem, in this article, we propose a dense discriminative network that is composed of several aggregation modules (AM). Specifically, the AM merges extraction and integration nodes in a tree structure, which can aggregate features progressively in an efficient way. In particular, we compress and rescale the densely connected information in the aggregation node by modeling the interaction between channels, which shares the same insight with the attention mechanism for improving the discriminative ability of network. Extensive experiments conducted on several publicly available datasets have demonstrated the superiority of our model over state-of-the-art in objective metrics and visual impressions.

中文翻译：

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通过密集判别网络的图像超分辨率


深度卷积神经网络最近在单图像超分辨率（SISR）问题上取得了相当大的成就。大多数 SISR 的 CNN 架构都采用长连接或短连接来集成特征，并平等对待它们。然而，他们忽视了特征的区分，因此取得了相对较差的性能。为了解决这个问题，在本文中，我们提出了一个由多个聚合模块（AM）组成的密集判别网络。具体来说，AM将提取和集成节点合并为树结构，可以有效地逐步聚合特征。特别是，我们通过对通道之间的交互进行建模来压缩和重新缩放聚合节点中的密集连接信息，这与提高网络判别能力的注意机制具有相同的见解。对几个公开可用的数据集进行的广泛实验证明了我们的模型在客观指标和视觉印象方面优于最先进的模型。