An Advanced Deep Residual Dense Network (DRDN) Approach for Image Super-Resolution
- DOI
- 10.2991/ijcis.d.191209.001How to use a DOI?
- Keywords
- Deep residual dense network (DRDN); Single image super-resolution; Fusion reconstruction; Residual dense connection; Multi-hop connection
- Abstract
In recent years, more and more attention has been paid to single image super-resolution reconstruction (SISR) by using deep learning networks. These networks have achieved good reconstruction results, but how to make better use of the feature information in the image, how to improve the network convergence speed, and so on still need further study. According to the above problems, a novel deep residual dense network (DRDN) is proposed in this paper. In detail, DRDN uses the residual-dense structure for local feature fusion, and finally carries out global residual fusion reconstruction. Residual-dense connection can make full use of the features of low-resolution images from shallow to deep layers, and provide more low-resolution image information for super-resolution reconstruction. Multi-hop connection can make errors spread to each layer of the network more quickly, which can alleviate the problem of difficult training caused by deepening network to a certain extent. The experiments show that DRDN not only ensure good training stability and successfully converge but also has less computing cost and higher reconstruction efficiency.
- Copyright
- © 2019 The Authors. Published by Atlantis Press SARL.
- Open Access
- This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).
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TY - JOUR AU - Wang Wei AU - Jiang Yongbin AU - Luo Yanhong AU - Li Ji AU - Wang Xin AU - Zhang Tong PY - 2019 DA - 2019/12/14 TI - An Advanced Deep Residual Dense Network (DRDN) Approach for Image Super-Resolution JO - International Journal of Computational Intelligence Systems SP - 1592 EP - 1601 VL - 12 IS - 2 SN - 1875-6883 UR - https://doi.org/10.2991/ijcis.d.191209.001 DO - 10.2991/ijcis.d.191209.001 ID - Wei2019 ER -