1 citations · 1 across the 4 of their papers we have counts for
4 papers
Blind-Spot Guided Diffusion for Self-supervised Real-World Denoising
Shen Cheng, Haipeng Li, Haibin Huang +2
In this work, we present Blind-Spot Guided Diffusion, a novel self-supervised framework for real-world image denoising. Our approach addresses two major challenges: the limitations…
Learning to See in the Extremely Dark
Hai Jiang, Binhao Guan, Zhen Liu +5
Learning-based methods have made promising advances in low-light RAW image enhancement, while their capability to extremely dark scenes where the environmental illuminance drops as…
MoiréXNet: Adaptive Multi-Scale Demoiréing with Linear Attention Test-Time Training and Truncated Flow Matching Prior
Liangyan Li, Yimo Ning, Kevin Le +4
This paper introduces a novel framework for image and video demoiréing by integrating Maximum A Posteriori (MAP) estimation with advanced deep learning techniques. Demoiréing addre…
Low-Light Image Enhancement via Generative Perceptual Priors
Han Zhou, Wei Dong, Xiaohong Liu +3
Although significant progress has been made in enhancing visibility, retrieving texture details, and mitigating noise in Low-Light (LL) images, the challenge persists in applying c…