5 citations · 12 across the 6 of their papers we have counts for
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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…