Öǧrenme Transferi Temelli Hava Araci Görüntülerinin Süper Çözünürlüǧü

Aslan Ahmet Haykir, Ilkay Oksuz

Araştırma sonucu: ???type-name???Konferans katkısıbilirkişi

Özet

Images created using the Super Resolution method can generate more information compared to their low resolution counterparts. A super-resolved image, which is created using an original image captured by an imaging source is not only more meaningful to human perception but also has advantages on downstream tasks such as object detection and pattern recognition. In this work, we aim to apply the Super Resolution method to the Aerial Images captured for surveillance to enable more information about the original scenes. To achieve this Super Resolution Generative Adversarial Network (SRGAN), which is based on the Generative Adversarial Networks architecture is used. We also applied transfer learning methodology to achieve better image quality. Public xView and DOTA datasets which contain images mostly captured by satellites around the world are used to train a generative model via SRGAN architecture. Furthermore, DIV2K dataset is used to pre-train a generative model, and then the transfer learning technique is used to train separate models on xView and DOTA validation datasets. Perceptual Index (PI) and Root Mean Squared Error (RMSE) which are used on European Conference on Computer Vision -Perceptual Image Restoration and Manipulation Workshop 2018 are computed as the performance metrics. We have seen that the model which gives the best PI results, i.e. better perceptual quality, on xView and DOTA validation datasets is the one trained using the DIV2K dataset and the model which gives the best RMSE results, i.e. better reconstruction quality, is the one trained using the transfer learning technique.

Tercüme edilen katkı başlığıTransfer Learning Based Super Resolution of Aerial Images
Orijinal dilTürkçe
Ana bilgisayar yayını başlığı2022 30th Signal Processing and Communications Applications Conference, SIU 2022
YayınlayanInstitute of Electrical and Electronics Engineers Inc.
ISBN (Elektronik)9781665450928
DOI'lar
Yayın durumuYayınlandı - 2022
Etkinlik30th Signal Processing and Communications Applications Conference, SIU 2022 - Safranbolu, Turkey
Süre: 15 May 202218 May 2022

Yayın serisi

Adı2022 30th Signal Processing and Communications Applications Conference, SIU 2022

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???event.eventtypes.event.conference???30th Signal Processing and Communications Applications Conference, SIU 2022
Ülke/BölgeTurkey
ŞehirSafranbolu
Periyot15/05/2218/05/22

Bibliyografik not

Publisher Copyright:
© 2022 IEEE.

Keywords

  • Aerial Images
  • Deep Learning
  • Generative Adversarial Networks
  • Super Resolution

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