Deep Wavelet Prediction for Image Super-Resolution

Tiantong Guo, Hojjat Seyed Mousavi, Tiep Huu Vu, Vishal Monga

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    69 Scopus citations

    Abstract

    Recent advances have seen a surge of deep learning approaches for image super-resolution. Invariably, a network, e.g. a deep convolutional neural network (CNN) or auto-encoder is trained to learn the relationship between low and high-resolution image patches. Recognizing that a wavelet transform provides a 'coarse' as well as 'detail' separation of image content, we design a deep CNN to predict the 'missing details' of wavelet coefficients of the low-resolution images to obtain the Super-Resolution (SR) results, which we name Deep Wavelet Super-Resolution (DWSR). Out network is trained in the wavelet domain with four input and output channels respectively. The input comprises of 4 sub-bands of the low-resolution wavelet coefficients and outputs are residuals (missing details) of 4 sub-bands of high-resolution wavelet coefficients. Wavelet coefficients and wavelet residuals are used as input and outputs of our network to further enhance the sparsity of activation maps. A key benefit of such a design is that it greatly reduces the training burden of learning the network that reconstructs low frequency details. The output prediction is added to the input to form the final SR wavelet coefficients. Then the inverse 2d discrete wavelet transformation is applied to transform the predicted details and generate the SR results. We show that DWSR is computationally simpler and yet produces competitive and often better results than state-of-the-art alternatives.

    Original languageEnglish (US)
    Title of host publicationProceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017
    PublisherIEEE Computer Society
    Pages1100-1109
    Number of pages10
    ISBN (Electronic)9781538607336
    DOIs
    StatePublished - Aug 22 2017
    Event30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017 - Honolulu, United States
    Duration: Jul 21 2017Jul 26 2017

    Publication series

    NameIEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
    Volume2017-July
    ISSN (Print)2160-7508
    ISSN (Electronic)2160-7516

    Other

    Other30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017
    CountryUnited States
    CityHonolulu
    Period7/21/177/26/17

    All Science Journal Classification (ASJC) codes

    • Computer Vision and Pattern Recognition
    • Electrical and Electronic Engineering

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