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    Super-Resolution on Degraded Low-Resolution Images Using Convolutional Neural Networks


    Albluwi, Fatma and Krylov, Vladimir A and Dahyot, Rozenn (2019) Super-Resolution on Degraded Low-Resolution Images Using Convolutional Neural Networks. 2019 27th European Signal Processing Conference (EUSIPCO). pp. 1-5. ISSN 2076-1465

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    Abstract

    Single Image Super-Resolution (SISR) has witnessed a dramatic improvement in recent years through the use of deep learning and, in particular, convolutional neural networks (CNN). In this work we address reconstruction from low-resolution images and consider as well degrading factors in images such as blurring. To address this challenging problem, we propose a new architecture to tackle blur with the down-sampling of images by extending the DBSRCNN architecture [1]. We validate our new architecture (DBSR) experimentally against several state of the art super-resolution techniques.

    Item Type: Article
    Keywords: Super-Resolution; Degraded; Low-Resolution; Images; Convolutional Neural Networks;
    Academic Unit: Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 15251
    Identification Number: https://doi.org/10.23919/EUSIPCO.2019.8903000
    Depositing User: Rozenn Dahyot
    Date Deposited: 17 Jan 2022 12:56
    Journal or Publication Title: 2019 27th European Signal Processing Conference (EUSIPCO)
    Publisher: IEEE
    Refereed: Yes
    URI:

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