11 papers
PixIE: Prompted Pixel-Space Low-Light Image Enhancement
Ruirui Lin, Guoxi Huang, David Bull +1
Low-light images suffer from severe noise, contrast loss, and semantic ambiguity, making enhancement a joint problem of denoising and detail recovery. We propose PixIE, a feed-forw…
BVI-Mamba: Video Enhancement Using a Visual State-Space Model for Low-Light and Underwater Environments
Guoxi Huang, Ruirui Lin, Yini Li +2
Videos captured in low-light and underwater conditions often suffer from distortions such as noise, low contrast, color imbalance, and blur. These issues not only limit visibility…
Prune Wisely, Reconstruct Sharply: Compact 3D Gaussian Splatting via Adaptive Pruning and Difference-of-Gaussian Primitives
Haoran Wang, Guoxi Huang, Fan Zhang +2
Recent significant advances in 3D scene representation have been driven by 3D Gaussian Splatting (3DGS), which has enabled real-time rendering with photorealistic quality. 3DGS oft…
Dynamic Weight-based Temporal Aggregation for Low-light Video Enhancement Under Extreme Noise
Ruirui Lin, Guoxi Huang, Nantheera Anantrasirichai
Low-light video enhancement (LLVE) is challenging due to noise, low contrast, and color degradation. While learning-based methods enable fast inference, they often fail under heavy…
From Restoration to Reconstruction: Rethinking 3D Gaussian Splatting for Underwater Scenes
Guoxi Huang, Haoran Wang, Zipeng Qi +3
Underwater image degradation poses significant challenges for 3D reconstruction, where simplified physical models often fail in complex scenes. We propose \textbf{R-Splatting}, a u…
Semantic-guided Gaussian Splatting for High-Fidelity Underwater Scene Reconstruction
Zhuodong Jiang, Haoran Wang, Guoxi Huang +2
Accurate 3D reconstruction in degraded imaging conditions remains a key challenge in photogrammetry and neural rendering. In underwater environments, spatially varying visibility c…