activity
20182021
most citedDeep Bilateral Retinex for Low-Light Image Enhancement

16 citations · 50 across the 5 of their papers we have counts for

collaborators

9 papers

cs.CV20218 cited

Fully Non-Homogeneous Atmospheric Scattering Modeling with Convolutional Neural Networks for Single Image Dehazing

Cong Wang, Yan Huang, Yuexian Zou +1

In recent years, single image dehazing models (SIDM) based on atmospheric scattering model (ASM) have achieved remarkable results. However, it is noted that ASM-based SIDM degrades…

cs.CV20211 cited

FWB-Net:Front White Balance Network for Color Shift Correction in Single Image Dehazing via Atmospheric Light Estimation

Cong Wang, Yan Huang, Yuexian Zou +1

In recent years, single image dehazing deep models based on Atmospheric Scattering Model (ASM) have achieved remarkable results. But the dehazing outputs of those models suffer fro…

cs.CV202014 cited

LaSOT: A High-quality Large-scale Single Object Tracking Benchmark

Heng Fan, Hexin Bai, Liting Lin +11

Despite great recent advances in visual tracking, its further development, including both algorithm design and evaluation, is limited due to lack of dedicated large-scale benchmark…

cs.CV202011 cited

Recurrent Exposure Generation for Low-Light Face Detection

Jinxiu Liang, Jingwen Wang, Yuhui Quan +4

Face detection from low-light images is challenging due to limited photos and inevitable noise, which, to make the task even harder, are often spatially unevenly distributed. A nat…

eess.IV202016 cited

Deep Bilateral Retinex for Low-Light Image Enhancement

Jinxiu Liang, Yong Xu, Yuhui Quan +3

Low-light images, i.e. the images captured in low-light conditions, suffer from very poor visibility caused by low contrast, color distortion and significant measurement noise. Low…

cs.CV2019

UG Track 2: A Collective Benchmark Effort for Evaluating and Advancing Image Understanding in Poor Visibility Environments

Ye Yuan, Wenhan Yang, Wenqi Ren +3

The UG challenge in IEEE CVPR 2019 aims to evoke a comprehensive discussion and exploration about how low-level vision techniques can benefit the high-level automatic visual…