5 citations · 12 across the 6 of their papers we have counts for
6 papers
Removing Multiple Hybrid Adverse Weather in Video via a Unified Model
Yecong Wan, Mingwen Shao, Yuanshuo Cheng +2
Videos captured under real-world adverse weather conditions typically suffer from uncertain hybrid weather artifacts with heterogeneous degradation distributions. However, existing…
S2Gaussian: Sparse-View Super-Resolution 3D Gaussian Splatting
Yecong Wan, Mingwen Shao, Yuanshuo Cheng +1
In this paper, we aim ambitiously for a realistic yet challenging problem, namely, how to reconstruct high-quality 3D scenes from sparse low-resolution views that simultaneously su…
Cross-Consistent Deep Unfolding Network for Adaptive All-In-One Video Restoration
Yuanshuo Cheng, Mingwen Shao, Yecong Wan +3
Existing Video Restoration (VR) methods always necessitate the individual deployment of models for each adverse weather to remove diverse adverse weather degradations, lacking the…
DRM-IR: Task-Adaptive Deep Unfolding Network for All-In-One Image Restoration
Yuanshuo Cheng, Mingwen Shao, Yecong Wan +1
Existing All-In-One image restoration (IR) methods usually lack flexible modeling on various types of degradation, thus impeding the restoration performance. To achieve All-In-One…
Restoring Images Captured in Arbitrary Hybrid Adverse Weather Conditions in One Go
Ye-Cong Wan, Ming-Wen Shao, Yuan-Shuo Cheng +2
Adverse conditions typically suffer from stochastic hybrid weather degradations (e.g., rainy and hazy night), while existing image restoration algorithms envisage that weather degr…
Rain Removal and Illumination Enhancement Done in One Go
Yecong Wan, Yuanshuo Cheng, Mingwen Shao
Rain removal plays an important role in the restoration of degraded images. Recently, data-driven methods have achieved remarkable success. However, these approaches neglect that t…