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Dual-Convolutional Enhanced Residual Network for Single Super-Resolution of Remote Sensing Images

  • Xuewei Li
  • Hongqian Shen
  • Chenhan Wang
  • Han Jiang
  • Ruiguo Yu
  • Jianrong Wang
  • Mankun Zhao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11306)

Abstract

The image super-resolution aims to recover a high-resolution image using a single or sequential low-resolution images. The super resolution methods based on deep learning, especially the deep convolutional neural network, have achieved good results. In this paper,we propose Dual-Convolutional Enhanced Residual Network (DCER) for remote sensing images based on residual learning, which concatenates the feature maps of different convolutional kernel sizes (3 \(\times \) 3, 5 \(\times \) 5). On the one hand, it can learn more high-frequency detail information by combining the local details of different scales; on the other hand, it reduces network parameters and greatly shorten the training time. The experimental results show that DCER achieves favorable performance of accuracy and visual performance against the state-of-the-art methods with the scale factor 2x, 4x and 8x.

Keywords

Dual-Convolutional Enhanced Residual Network (DCER) Single super-resolution Remote sensing images 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Xuewei Li
    • 1
  • Hongqian Shen
    • 1
  • Chenhan Wang
    • 2
  • Han Jiang
    • 2
  • Ruiguo Yu
    • 1
  • Jianrong Wang
    • 1
  • Mankun Zhao
    • 1
  1. 1.School of Computer Science and TechnologyTianjin UniversityTianjinChina
  2. 2.Beijing AXIS Technology Company LimitedBeijingChina

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