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    Image Deblurring and Super-Resolution Using Deep Convolutional Neural Networks


    Fatma, Albluwi and Krylov, Vladimir A and Dahyot, Rozenn (2018) Image Deblurring and Super-Resolution Using Deep Convolutional Neural Networks. Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing. ISSN 1551-2541

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    Abstract

    Recently multiple high performance algorithms have been developed to infer high-resolution images from low-resolution image input using deep learning algorithms. The related problem of super-resolution from blurred or corrupted low-resolution images has however received much less attention. In this work, we propose a new deep learning approach that simultaneously addresses deblurring and super-resolution from blurred low resolution images. We evaluate the state-of-the-art super-resolution convolutional neural network (SR-CNN) architecture proposed in [1] for the blurred reconstruction scenario and propose a revised deeper architecture that proves its superiority experimentally both when the levels of blur are known and unknown a priori.

    Item Type: Article
    Keywords: Image super-resolution; deblurring; deep learning; convolutional neural networks;
    Academic Unit: Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 15254
    Identification Number: https://doi.org/10.1109/MLSP.2018.8516983
    Depositing User: Rozenn Dahyot
    Date Deposited: 17 Jan 2022 16:57
    Journal or Publication Title: Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing
    Publisher: IEEE
    Refereed: Yes
    URI:

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