activity
20242026
most citedBayesian Neural Networks for One-to-Many Mapping in Image Enhancement

1 citations · 2 across the 9 of their papers we have counts for

collaborators

11 papers

cs.CV2026

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…

cs.CV2026

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…

cs.CV2026

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…

cs.CV2025

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…

cs.CV2025

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…

cs.CV2025

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…

cs.CV2025

RUSplatting: Robust 3D Gaussian Splatting for Sparse-View Underwater Scene Reconstruction

Zhuodong Jiang, Haoran Wang, Guoxi Huang +2

Reconstructing high-fidelity underwater scenes remains a challenging task due to light absorption, scattering, and limited visibility inherent in aquatic environments. This paper p…

cs.CV2025★ 1 cited

Visual enhancement and 3D representation for underwater scenes: a review

Guoxi Huang, Haoran Wang, Brett Seymour +4

Underwater visual enhancement (UVE) and underwater 3D reconstruction pose significant challenges in computer vision and AI-based tasks due to complex imaging conditions in aquatic…

cs.CV2025

Marine Snow Removal Using Internally Generated Pseudo Ground Truth

Alexandra Malyugina, Guoxi Huang, Eduardo Ruiz +2

Underwater videos often suffer from degraded quality due to light absorption, scattering, and various noise sources. Among these, marine snow, which is suspended organic particles…

cs.CV2025★ 1 cited

Bayesian Neural Networks for One-to-Many Mapping in Image Enhancement

Guoxi Huang, Qirui Yang, Ruirui Lin +3

In image enhancement tasks, such as low-light and underwater image enhancement, a degraded image can correspond to multiple plausible target images due to dynamic photography condi…

cs.CV2024

BVI-RLV: A Fully Registered Dataset for Low-Light Video Enhancement

Ruirui Lin, Guoxi Huang, Joanne Lin +4

Low-light videos often exhibit spatiotemporally incoherent noise, compromising visibility and degrading performance in computer vision applications. A major challenge for enhancing…