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Deep Edge Guided Recurrent Residual Learning for Image Super-Resolution.

Wenhan Yang, Jiashi Feng, Jianchao Yang

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |September 15, 2017
    PubMed
    Summary
    This summary is machine-generated.

    We introduce the Deep Edge Guided REcurrent rEsidual (DEGREE) network for image super-resolution. This novel approach progressively recovers high-frequency details, outperforming existing methods and achieving state-of-the-art results.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Deep Learning

    Background:

    • Image super-resolution (SR) aims to reconstruct high-resolution (HR) images from low-resolution (LR) inputs.
    • Recovering high-frequency details crucial for human perception remains a significant challenge in SR.

    Purpose of the Study:

    • To introduce a novel deep learning network, DEGREE, for progressive image super-resolution.
    • To enhance SR by incorporating edge-preserving capabilities and recurrent residual learning.

    Main Methods:

    • Developed the Deep Edge Guided REcurrent rEsidual (DEGREE) network.
    • Employed recurrent residual learning to recover the difference between LR and HR images.
    • Integrated edge maps for joint inference of sharp edge details.

    Main Results:

    • Achieved state-of-the-art performance on three benchmark datasets.
    • Demonstrated superiority over established baseline methods in image super-resolution.
    • Showcased the network's effectiveness in JPEG artifact reduction, indicating broad applicability.

    Conclusions:

    • The DEGREE network effectively recovers high-frequency details for superior image super-resolution.
    • The edge-guided and recurrent residual approach offers significant improvements.
    • DEGREE exhibits flexibility for diverse image processing tasks beyond SR.