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dc.contributor.authorDing, Hongen_US
dc.contributor.authorYan, Qinganen_US
dc.contributor.authorFu, Gangen_US
dc.contributor.authorXiao, Chunxiaen_US
dc.contributor.editorLee, Jehee and Theobalt, Christian and Wetzstein, Gordonen_US
dc.description.abstractEstimating the correspondence between the images using optical flow is the key component for image fusion, however, computing optical flow between a pair of facial images including backgrounds is challenging due to large differences in illumination, texture, color and background in the images. To improve optical flow results for image fusion, we propose a novel flow estimation method, wavelet flow, which can handle both the face and background in the input images. The key idea is that instead of computing flow directly between the input image pair, we estimate the image flow by incorporating multi-scale image transfer and optical flow guided wavelet fusion. Multi-scale image transfer helps to preserve the background and lighting detail of input, while optical flow guided wavelet fusion produces a series of intermediate images for further fusion quality optimizing. Our approach can significantly improve the performance of the optical flow algorithm and provide more natural fusion results for both faces and backgrounds in the images. We evaluate our method on a variety of datasets to show its high outperformance.en_US
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectComputing methodologies
dc.subjectComputational photography
dc.titleWavelet Flow: Optical Flow Guided Wavelet Facial Image Fusionen_US
dc.description.seriesinformationComputer Graphics Forum
dc.description.sectionheadersImage and Video Editing

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  • 38-Issue 7
    Pacific Graphics 2019 - Symposium Proceedings

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