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dc.contributor.authorZhang, Qingen_US
dc.contributor.authorNie, Yongweien_US
dc.contributor.authorZheng, Wei-Shien_US
dc.contributor.editorLee, Jehee and Theobalt, Christian and Wetzstein, Gordonen_US
dc.description.abstractExposure correction is one of the fundamental tasks in image processing and computational photography. While various methods have been proposed, they either fail to produce visually pleasing results, or only work well for limited types of image (e.g., underexposed images). In this paper, we present a novel automatic exposure correction method, which is able to robustly produce high-quality results for images of various exposure conditions (e.g., underexposed, overexposed, and partially under- and over-exposed). At the core of our approach is the proposed dual illumination estimation, where we separately cast the underand over-exposure correction as trivial illumination estimation of the input image and the inverted input image. By performing dual illumination estimation, we obtain two intermediate exposure correction results for the input image, with one fixes the underexposed regions and the other one restores the overexposed regions. A multi-exposure image fusion technique is then employed to adaptively blend the visually best exposed parts in the two intermediate exposure correction images and the input image into a globally well-exposed image. Experiments on a number of challenging images demonstrate the effectiveness of the proposed approach and its superiority over the state-of-the-art methods and popular automatic exposure correction tools.en_US
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.titleDual Illumination Estimation for Robust Exposure Correctionen_US
dc.description.seriesinformationComputer Graphics Forum
dc.description.sectionheadersComputational Photography

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  • 38-Issue 7
    Pacific Graphics 2019 - Symposium Proceedings

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