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Communication Dans Un Congrès Année : 2022

Deep learning-based image restoration for low-power and lossy networks

Aya Sakhri
  • Fonction : Auteur
  • PersonId : 1143721
Eric Rondeau
Mohamed Omar Chida
  • Fonction : Auteur
  • PersonId : 1174452
Chaima Tounsi-Omezzine
  • Fonction : Auteur
  • PersonId : 1174453

Résumé

Multimedia Internet of Things (IoMT) is witnessing explosive growth due to its applications in multiple areas. To cope with limited resources of low-power and lossy networks (LLN), it is common that : (i) images are captured with a degraded quality due to limited camera capabilities, (ii) a low-cost lossy compression is applied to reduce the amount of data to deliver which introduces additional distortion and (iii) transmissions are prone to losses that induce holes in the images, further degrading their quality and making them difficult to use. In this work, we propose a complete efficient encoding-transmission-reconstruction chain. In addition to the use of a low complexity image compression method, an appropriate packetization scheme is proposed. At the destination, more powerful resources are leveraged to apply deep learning models to compensate for the distortion caused by the adopted lossy compression as well as to fill in the holes induced by packet losses. The obtained results show the effectiveness of our proposal.
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Dates et versions

hal-03814102 , version 1 (13-10-2022)

Identifiants

  • HAL Id : hal-03814102 , version 1

Citer

Moufida Maimour, Aya Sakhri, Eric Rondeau, Mohamed Omar Chida, Chaima Tounsi-Omezzine, et al.. Deep learning-based image restoration for low-power and lossy networks. 5th Edition of the International Conference on Advanced Aspects of Software Engineering, ICAASE’22, Sep 2022, Constantine, Algeria. ⟨hal-03814102⟩
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