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所属单位：航空学院

发表刊物：Sensors

摘要：Deep neural networks (DNNs) have been widely adopted in single image super-resolution (SISR) recently with great success. As a network goes deeper, intermediate features become hierarchical. However, most SISR methods based on DNNs do not make full use of the hierarchical features. The features cannot be read directly by the subsequent layers, therefore, the previous hierarchical information has little influence on the subsequent layer output, and the performance is relatively poor. To address this issue, a novel global dense feature fusion convolutional network (DFFNet) is proposed, which can take full advantage of global intermediate features. Especially, a feature fusion block (FFblock) is introduced as the basic module. Each block can directly read raw global features from previous ones and then learns the feature spatial correlation and channel correlation between features in a holistic way, leading to a continuous global information memory mechanism. Experiments on the benchmark tests show that the proposed method DFFNet achieves favorable performance against the state-of-art methods. © 2019 by the authors. Licensee MDPI, Basel, Switzerland.

ISSN号：1424-8220

是否译文：否

发表时间：2019-01-02

合写作者：Xu, Wang,陈仁文,Huang, Bin,张翔,Liu, Chuan

通讯作者：陈仁文,汪旭