Browsing by Author "Cho, Sunghyun"

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    Defocus and Motion Blur Detection with Deep Contextual Features
    (The Eurographics Association and John Wiley & Sons Ltd., 2018) Kim, Beomseok; Son, Hyeongseok; Park, Seong-Jin; Cho, Sunghyun; Lee, Seungyong; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
    We propose a novel approach for detecting two kinds of partial blur, defocus and motion blur, by training a deep convolutional neural network. Existing blur detection methods concentrate on designing low-level features, but those features have difficulty in detecting blur in homogeneous regions without enough textures or edges. To handle such regions, we propose a deep encoder-decoder network with long residual skip-connections and multi-scale reconstruction loss functions to exploit high-level contextual features as well as low-level structural features. Another difficulty in partial blur detection is that there are no available datasets with images having both defocus and motion blur together, as most existing approaches concentrate only on either defocus or motion blur. To resolve this issue, we construct a synthetic dataset that consists of complex scenes with both types of blur. Experimental results show that our approach effectively detects and classifies blur, outperforming other state-of-the-art methods. Our method can be used for various applications, such as photo editing, blur magnification, and deblurring.
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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).