Junhui Hou

TL;DR: This paper constructs an Underwater Image Enhancement Benchmark (UIEB) including 950 real-world underwater images, 890 of which have the corresponding reference images and proposes an underwater image enhancement network (called Water-Net) trained on this benchmark as a baseline, which indicates the generalization of the proposed UIEB for training Convolutional Neural Networks (CNNs).

TL;DR: A novel method, Zero-Reference Deep Curve Estimation (Zero-DCE), which formulates light enhancement as a task of image-specific curve estimation with a deep network and shows that it generalizes well to diverse lighting conditions.

TL;DR: Zhang et al. as discussed by the authors proposed a zero-reference deep curve estimation (Zero-DCE) method, which formulates light enhancement as a task of image-specific curve estimation with a deep network.

TL;DR: The proposed C-CNN outperforms the state-of-the-art CNN-based classification methods, and its corresponding FL-CNN is very effective to extract sensor-specific spatial-spectral features for hyperspectral applications under both supervised and unsupervised modes.

TL;DR: A robust fall detection approach by analyzing the tracked key joints of the human body using a single depth camera is proposed, which requires low computational cost during the training and test and can work even in a dark room.