48 citations · 58 across the 7 of their papers we have counts for
8 papers
-PnP: Plug-and-Play Denoising for Scientific Imaging with Self-Supervised Score Priors
Yukun Cui, Jiangshe Zhang, Shuang Xu +6
Modern image restoration methods increasingly rely on large-scale supervised training, yet clean targets are often prohibitively difficult to obtain in scientific imaging modalitie…
Retina gap junctions support the robust perception by warping neural representational geometries along the visual hierarchy
Yang Yue, Shenjian Zhang, Yonghong Tian +2
Deep Neural Networks (DNNs) are vulnerable to elaborately designed adversarial noise, although they have achieved extraordinary success in many tasks. Compared with DNNs, the human…
Towards Understanding The Calibration Benefits of Sharpness-Aware Minimization
Chengli Tan, Yubo Zhou, Haishan Ye +7
Deep neural networks have been increasingly used in safety-critical applications such as medical diagnosis and autonomous driving. However, many studies suggest that they are prone…
Retinex-MEF: Retinex-based Glare Effects Aware Unsupervised Multi-Exposure Image Fusion
Haowen Bai, Jiangshe Zhang, Zixiang Zhao +3
Multi-exposure image fusion (MEF) synthesizes multiple, differently exposed images of the same scene into a single, well-exposed composite. Retinex theory, which separates image il…
Task-driven Image Fusion with Learnable Fusion Loss
Haowen Bai, Jiangshe Zhang, Zixiang Zhao +5
Multi-modal image fusion aggregates information from multiple sensor sources, achieving superior visual quality and perceptual features compared to single-source images, often impr…
Image Fusion via Vision-Language Model
Zixiang Zhao, Lilun Deng, Haowen Bai +8
Image fusion integrates essential information from multiple images into a single composite, enhancing structures, textures, and refining imperfections. Existing methods predominant…
Stabilizing Sharpness-aware Minimization Through A Simple Renormalization Strategy
Chengli Tan, Jiangshe Zhang, Junmin Liu +2
Recently, sharpness-aware minimization (SAM) has attracted much attention because of its surprising effectiveness in improving generalization performance. However, compared to stoc…
ReFusion: Learning Image Fusion from Reconstruction with Learnable Loss via Meta-Learning
Haowen Bai, Zixiang Zhao, Jiangshe Zhang +5
Image fusion aims to combine information from multiple source images into a single one with more comprehensive informational content. Deep learning-based image fusion algorithms fa…