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Sensors 2019, 19(2), 316; https://doi.org/10.3390/s19020316

Single Image Super-Resolution Based on Global Dense Feature Fusion Convolutional Network

State Key Laboratory of Mechanics and Control of Mechanical Structures, College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, Jiangsu, China
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Received: 6 December 2018 / Revised: 6 January 2019 / Accepted: 8 January 2019 / Published: 14 January 2019
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Sensors Networks)
PDF [834 KB, uploaded 14 January 2019]

Abstract

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.
Keywords: dense feature fusion; convolutional neural network; image super-resolution dense feature fusion; convolutional neural network; image super-resolution
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Xu, W.; Chen, R.; Huang, B.; Zhang, X.; Liu, C. Single Image Super-Resolution Based on Global Dense Feature Fusion Convolutional Network. Sensors 2019, 19, 316.

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