Deep learning-based moving object segmentation: Recent progress and research prospects
R Jiang, R Zhu, H Su, Y Li, Y Xie, W Zou - Machine Intelligence Research, 2023 - Springer
Moving object segmentation (MOS), aiming at segmenting moving objects from video
frames, is an important and challenging task in computer vision and with various …
frames, is an important and challenging task in computer vision and with various …
EFINet: Restoration for low-light images via enhancement-fusion iterative network
C Liu, F Wu, X Wang - … on Circuits and Systems for Video …, 2022 - ieeexplore.ieee.org
The lighting environment in the real world is so complex that most existing low-light image
restoration methods suffer from color cast and local over-exposure. In order to solve these …
restoration methods suffer from color cast and local over-exposure. In order to solve these …
HFMNet: Hierarchical feature mining network for low-light image enhancement
Images captured in low-light environments often suffer from issues related to dark
illumination and damaged details, which results in poor visibility. To address these …
illumination and damaged details, which results in poor visibility. To address these …
Low-light image enhancement with multi-scale attention and frequency-domain optimization
Low-light image enhancement aims to improve the perceptual quality of images captured in
conditions of insufficient illumination. However, such images are often characterized by low …
conditions of insufficient illumination. However, such images are often characterized by low …
Unsupervised moving object segmentation using background subtraction and optimal adversarial noise sample search
Abstract Moving Objects Segmentation (MOS) is a fundamental task in many computer
vision applications such as human activity analysis, visual object tracking, content based …
vision applications such as human activity analysis, visual object tracking, content based …
Consistency-constancy bi-knowledge learning for pedestrian detection in night surveillance
Pedestrian detection in the night surveillance is a challenging yet not largely explored task.
As the success of the detector in the daytime surveillance and the convenient acquisition of …
As the success of the detector in the daytime surveillance and the convenient acquisition of …
Density-aware haze image synthesis by self-supervised content-style disentanglement
The key procedure of haze image synthesis with adversarial training lies in the
disentanglement of the feature involved only in haze synthesis, ie, the style feature, from the …
disentanglement of the feature involved only in haze synthesis, ie, the style feature, from the …
Panoptic-level image-to-image translation for object recognition and visual odometry enhancement
Image-to-image translation methods have progressed from only considering the image-level
information to integrating the global-and instance-level information. However, only the …
information to integrating the global-and instance-level information. However, only the …
Learning Nighttime Semantic Segmentation the Hard Way
Nighttime semantic segmentation is an important but challenging research problem for
autonomous driving. The major challenges lie in the small objects or regions from the under …
autonomous driving. The major challenges lie in the small objects or regions from the under …
Illumination-Aware Low-Light Image Enhancement with Transformer and Auto-Knee Curve
Images captured under low-light conditions suffer from several combined degradation
factors, including low brightness, low contrast, noise, and color bias. Many learning-based …
factors, including low brightness, low contrast, noise, and color bias. Many learning-based …