10 papers
Breaking Spatial Uniformity: Prior-Guided Mamba with Radial Serialization for Lens Flare Removal
Zijia Fu, Yuanfei Huang, Lizhi Wang +1
Lens flares, caused by complex optical aberrations, severely degrade image quality especially in nighttime photography. Although recent restoration methods have made remarkable pro…
Learning Physics-Informed Noise Models from Dark Frames for Low-Light Raw Image Denoising
Hansen Feng, Lizhi Wang, Yiqi Huang +3
Recently, the mainstream practice for training low-light raw image denoising methods has shifted towards employing synthetic data. Noise modeling, which focuses on characterizing t…
Rethinking Model Redundancy for Low-light Image Enhancement
Tong Li, Lizhi Wang, Hansen Feng +3
Low-light image enhancement (LLIE) is a fundamental task in computational photography, aiming to improve illumination, reduce noise, and enhance the image quality of low-light imag…
Revealing Latent Information: A Physics-inspired Self-supervised Pre-training Framework for Noisy and Sparse Events
Lin Zhu, Ruonan Liu, Xiao Wang +2
Event camera, a novel neuromorphic vision sensor, records data with high temporal resolution and wide dynamic range, offering new possibilities for accurate visual representation i…
YOND: Practical Blind Raw Image Denoising Free from Camera-Specific Data Dependency
Hansen Feng, Lizhi Wang, Yiqi Huang +3
The rapid advancement of photography has created a growing demand for a practical blind raw image denoising method. Recently, learning-based methods have become mainstream due to t…
PDE: Gene Effect Inspired Parameter Dynamic Evolution for Low-light Image Enhancement
Tong Li, Lizhi Wang, Hansen Feng +2
Low-light image enhancement (LLIE) is a fundamental task in computational photography, aiming to improve illumination, reduce noise, and enhance image quality. While recent advance…