Browsing by Author "Son, Hyeongseok"

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    Naturalness-Preserving Image Tone Enhancement Using Generative Adversarial Networks
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Son, Hyeongseok; Lee, Gunhee; Cho, Sunghyun; Lee, Seungyong; Lee, Jehee and Theobalt, Christian and Wetzstein, Gordon
    This paper proposes a deep learning-based image tone enhancement approach that can maximally enhance the tone of an image while preserving the naturalness. Our approach does not require carefully generated ground-truth images by human experts for training. Instead, we train a deep neural network to mimic the behavior of a previous classical filtering method that produces drastic but possibly unnatural-looking tone enhancement results. To preserve the naturalness, we adopt the generative adversarial network (GAN) framework as a regularizer for the naturalness. To suppress artifacts caused by the generative nature of the GAN framework, we also propose an imbalanced cycle-consistency loss. Experimental results show that our approach can effectively enhance the tone and contrast of an image while preserving the naturalness compared to previous state-of-the-art approaches.
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    Real-Time Video Deblurring via Lightweight Motion Compensation
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Son, Hyeongseok; Lee, Junyong; Cho, Sunghyun; Lee, Seungyong; Umetani, Nobuyuki; Wojtan, Chris; Vouga, Etienne
    While motion compensation greatly improves video deblurring quality, separately performing motion compensation and video deblurring demands huge computational overhead. This paper proposes a real-time video deblurring framework consisting of a lightweight multi-task unit that supports both video deblurring and motion compensation in an efficient way. The multi-task unit is specifically designed to handle large portions of the two tasks using a single shared network and consists of a multi-task detail network and simple networks for deblurring and motion compensation. The multi-task unit minimizes the cost of incorporating motion compensation into video deblurring and enables real-time deblurring. Moreover, by stacking multiple multi-task units, our framework provides flexible control between the cost and deblurring quality. We experimentally validate the state-of-theart deblurring quality of our approach, which runs at a much faster speed compared to previous methods and show practical real-time performance (30.99dB@30fps measured on the DVD dataset).