14 citations · 30 across the 5 of their papers we have counts for
5 papers
CF-YOLO: Cross Fusion YOLO for Object Detection in Adverse Weather with a High-quality Real Snow Dataset
Qiqi Ding, Peng Li, Xuefeng Yan +6
Snow is one of the toughest adverse weather conditions for object detection (OD). Currently, not only there is a lack of snowy OD datasets to train cutting-edge detectors, but also…
CDFI: Compression-Driven Network Design for Frame Interpolation
Tianyu Ding, Luming Liang, Zhihui Zhu +1
DNN-based frame interpolation--that generates the intermediate frames given two consecutive frames--typically relies on heavy model architectures with a huge number of features, pr…
ImagePairs: Realistic Super Resolution Dataset via Beam Splitter Camera Rig
Hamid Reza Vaezi Joze, Ilya Zharkov, Karlton Powell +5
Super Resolution is the problem of recovering a high-resolution image from a single or multiple low-resolution images of the same scene. It is an ill-posed problem since high frequ…
DRD-Net: Detail-recovery Image Deraining via Context Aggregation Networks
Sen Deng, Mingqiang Wei, Jun Wang +3
Image deraining is a fundamental, yet not well-solved problem in computer vision and graphics. The traditional image deraining approaches commonly behave ineffectively in medium an…
Convolutional Neural Network with Median Layers for Denoising Salt-and-Pepper Contaminations
Luming Liang, Sen Deng, Lionel Gueguen +3
We propose a deep fully convolutional neural network with a new type of layer, named median layer, to restore images contaminated by the salt-and-pepper (s&p) noise. A median layer…