Single Image Super-Resolution with Application to Remote-Sensing Image

Farah Deeba, Fayaz Ali Dharejo, Yuanchun Zhou, Abdul Ghaffar, Mujahid Hussain Memon, She Kun

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

To improve the resolution of satellite images, many researchers are committed to machine learning and neural network-based SR methods. SR has multiple residual network frameworks in deep learning that have improved performance and can extend thousands of layers in the system. However, each layer improves accuracy by doubling the number of layers, although training thousands of layers are too expensive, the process is slow, and there are functional recovery issues. To address these issues, we propose a super-resolution wide remote sensing residual network (WRSR), in which we increase the width and reduce the depth of the residual network, due to decreasing the depth of the network our model reduced memory costs. To enhance the resolution of the single image we showed that our method improves training loss performance by performing the weight normalization instead of augmentation technology. The results of the experiment show that the method performs well in terms of quantitative indicators (PSNR) and (SSIM).

Original languageBritish English
Title of host publication2020 Global Conference on Wireless and Optical Technologies, GCWOT 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665418607
DOIs
StatePublished - 6 Oct 2020
Event2020 Global Conference on Wireless and Optical Technologies, GCWOT 2020 - Malaga, Spain
Duration: 6 Oct 20208 Oct 2020

Publication series

Name2020 Global Conference on Wireless and Optical Technologies, GCWOT 2020

Conference

Conference2020 Global Conference on Wireless and Optical Technologies, GCWOT 2020
Country/TerritorySpain
CityMalaga
Period6/10/208/10/20

Keywords

  • Low-resolution LR
  • remote-sensing images
  • super-resolution (SR)
  • wide residual block

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