5 papers
From Physical Degradation Models to Task-Aware All-in-One Image Restoration
Hu Gao, Xiaoning Lei, Xichen Xu +2
All-in-one image restoration aims to adaptively handle multiple restoration tasks with a single trained model. Although existing methods achieve promising results by introducing pr…
Physically Interpretable Multi-Degradation Image Restoration via Deep Unfolding and Explainable Convolution
Hu Gao, Xiaoning Lei, Xichen Xu +2
Although image restoration has advanced significantly, most existing methods target only a single type of degradation. In real-world scenarios, images often contain multiple degrad…
Learning to Restore Multi-Degraded Images via Ingredient Decoupling and Task-Aware Path Adaptation
Hu Gao, Xiaoning Lei, Ying Zhang +3
Image restoration (IR) aims to recover clean images from degraded observations. Despite remarkable progress, most existing methods focus on a single degradation type, whereas real-…
MBMamba: When Memory Buffer Meets Mamba for Structure-Aware Image Deblurring
Hu Gao, Xiaoning Lei, Xichen Xu +2
The Mamba architecture has emerged as a promising alternative to CNNs and Transformers for image deblurring. However, its flatten-and-scan strategy often results in local pixel for…
Reconstructing Topology-Consistent Face Mesh by Volume Rendering from Multi-View Images
Yating Wang, Ran Yi, Xiaoning Lei +3
Industrial 3D face assets creation typically reconstructs topology-consistent face meshes from multi-view images for downstream production. However, high-quality reconstruction usu…