234 citations · 442 across the 17 of their papers we have counts for
24 papers · 1 filter
Learning Heavily-Degraded Prior for Underwater Object Detection
Chenping Fu, Xin Fan, Jiewen Xiao +3
Underwater object detection suffers from low detection performance because the distance and wavelength dependent imaging process yield evident image quality degradations such as ha…
Practical exposure correction via compensation
Long Ma, Nan An, Jinyuan Liu +4
In computer vision, correcting the exposure level is a fundamental task for enhancing the visual quality of observations with inappropriate lightness. However, existing methodologi…
Semantic-aware Texture-Structure Feature Collaboration for Underwater Image Enhancement
Di Wang, Long Ma, Risheng Liu +1
Underwater image enhancement has become an attractive topic as a significant technology in marine engineering and aquatic robotics. However, the limited number of datasets and impe…
CoCoNet: Coupled Contrastive Learning Network with Multi-level Feature Ensemble for Multi-modality Image Fusion
Jinyuan Liu, Runjia Lin, Guanyao Wu +3
Infrared and visible image fusion targets to provide an informative image by combining complementary information from different sensors. Existing learning-based fusion approaches a…
Unsupervised Misaligned Infrared and Visible Image Fusion via Cross-Modality Image Generation and Registration
Di Wang, Jinyuan Liu, Xin Fan +1
Recent learning-based image fusion methods have marked numerous progress in pre-registered multi-modality data, but suffered serious ghosts dealing with misaligned multi-modality d…
Target-aware Dual Adversarial Learning and a Multi-scenario Multi-Modality Benchmark to Fuse Infrared and Visible for Object Detection
Jinyuan Liu, Xin Fan, Zhanbo Huang +4
This study addresses the issue of fusing infrared and visible images that appear differently for object detection. Aiming at generating an image of high visual quality, previous ap…