Y
Yuxu Lu
Researcher at Wuhan University of Technology
Publications - 23
Citations - 250
Yuxu Lu is an academic researcher from Wuhan University of Technology. The author has contributed to research in topics: Computer science & Noise reduction. The author has an hindex of 3, co-authored 11 publications receiving 29 citations.
Papers
More filters
Journal ArticleDOI
An enhanced CNN-enabled learning method for promoting ship detection in maritime surveillance system
TL;DR: An enhanced convolutional neural network (CNN) is proposed to improve ship detection under different weather conditions by redesigning the sizes of anchor boxes, predicting the localization uncertainties of bounding boxes, introducing the soft non-maximum suppression, and reconstructing a mixed loss function.
Journal ArticleDOI
Low-Light Image Enhancement With Regularized Illumination Optimization and Deep Noise Suppression
TL;DR: A hybrid regularized variational model, which combines L0-norm gradient sparsity prior with structure-aware regularization, is presented to refine the coarse illumination map originally estimated using Max-RGB and is introduced to enhance the low-light images through regularized illumination optimization and deep noise suppression.
Journal ArticleDOI
Lightweight deep network-enabled real-time low-visibility enhancement for promoting vessel detection in maritime video surveillance
Yu Guo,Yuxu Lu,Ryan Wen Liu +2 more
Journal ArticleDOI
Deep Network-Enabled Haze Visibility Enhancement for Visual IoT-Driven Intelligent Transportation Systems
TL;DR: TSDNet as discussed by the authors proposes a deep network-enabled three-stage dehazing network (termed TSDNet) for promoting the visual IoT-driven intelligent transportation systems (ITSs).
Journal ArticleDOI
MTRBNet: Multi-Branch Topology Residual Block-Based Network for Low-Light Enhancement
TL;DR: A multi-branch topology residual block (M TRB)-based network (MTRBNet), which can alleviate training difficulties and more efficiently use the parameters between neurons, is proposed, which achieves superior performance compared with several state-of-the-art methods.