Gated fusion network for joint image deblurring and super-resolution

Xinyi Zhang, Hang Dong, Zhe Hu, Wei Sheng Lai, Fei Wang, Ming Hsuan Yang

    Research output: Contribution to conferencePaperpeer-review


    Single-image super-resolution is a fundamental task for vision applications to enhance the image quality with respect to spatial resolution. If the input image contains degraded pixels, the artifacts caused by the degradation could be amplified by super-resolution methods. Image blur is a common degradation source. Images captured by moving or still cameras are inevitably affected by motion blur due to relative movements between sensors and objects. In this work, we focus on the super-resolution task with the presence of motion blur. We propose a deep gated fusion convolution neural network to generate a clear high-resolution frame from a single natural image with severe blur. By decomposing the feature extraction step into two task-independent streams, the dual-branch design can facilitate the training process by avoiding learning the mixed degradation all-in-one and thus enhance the final high-resolution prediction results. Extensive experiments demonstrate that our method generates sharper super-resolved images from low-resolution inputs with high computational efficiency.

    Original languageEnglish
    Publication statusPublished - 2019 Jan 1
    Event29th British Machine Vision Conference, BMVC 2018 - Newcastle, United Kingdom
    Duration: 2018 Sep 32018 Sep 6


    Conference29th British Machine Vision Conference, BMVC 2018
    Country/TerritoryUnited Kingdom

    All Science Journal Classification (ASJC) codes

    • Computer Vision and Pattern Recognition


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