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Wen-Huang Cheng
Researcher at National Chiao Tung University
Publications - 204
Citations - 4085
Wen-Huang Cheng is an academic researcher from National Chiao Tung University. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 27, co-authored 181 publications receiving 2767 citations. Previous affiliations of Wen-Huang Cheng include National Chung Hsing University & Peking University.
Papers
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Journal ArticleDOI
Computer-aided classification of lung nodules on computed tomography images via deep learning technique.
TL;DR: This study attempted to simplify the image analysis pipeline of conventional CAD with deep learning techniques and introduced models of a deep belief network and a convolutional neural network in the context of nodule classification in computed tomography images.
Proceedings ArticleDOI
FingerPad: private and subtle interaction using fingertips
Liwei Chan,Rong-Hao Liang,Ming-Chang Tsai,Kai-Yin Cheng,Chao-Huai Su,Mike Y. Chen,Wen-Huang Cheng,Bing-Yu Chen +7 more
TL;DR: FingerPad is a nail-mounted device that turns the tip of the index finger into a touchpad, allowing private and subtle interaction while on the move and preserves natural haptic feedback without affecting the native function of the fingertips.
Journal ArticleDOI
LR3M: Robust Low-Light Enhancement via Low-Rank Regularized Retinex Model
TL;DR: This paper introduces a robust low-light enhancement approach, aiming at well enhancing low- light images/videos and suppressing intensive noise jointly, based on the proposed Low-Rank Regularized Retinex Model (LR3M), which is the first to inject low-rank prior into a RetineX decomposition process to suppress noise in the reflectance map.
Proceedings ArticleDOI
Joint Enhancement and Denoising Method via Sequential Decomposition
TL;DR: In this article, a joint low-light enhancement and denoising strategy is proposed to obtain well-enhanced lowlight images while getting rid of the inherent noise issue simultaneously.
Journal ArticleDOI
Intelligent deployment of UAVs in 5G heterogeneous communication environment for improved coverage
TL;DR: The proposed approach utilizes the priority-wise dominance and the entropy approaches for providing solutions to the two problems considered in this paper, namely, Macro Base Station (MBS) decision problem and the cooperative UAV allocation problem.