6 papers · 1 filter
GraLoD: Graphics-Inspired Continuous Level-of-Detail Learning for Image Restoration
Hu Gao, Lizhuang Ma, Yulong Chen
The spatial support required for image restoration varies across degradation types, image regions, and reconstruction stages. However, most existing methods rely on predefined mult…
Learning to Restore More: Continual Capability Expansion for Pretrained Image Restoration Models
Hu Gao, Yulong Chen, Lizhuang Ma
Image restoration models are typically trained with a fixed set of capabilities. When new restoration requirements emerge, existing solutions usually train additional models or joi…
Causal-AgentIR: Self-Evolving Causal Memory for Adaptive Image Restoration Agents
Hu Gao, Yulong Chen, Lizhuang Ma
Image restoration agents have recently emerged as a flexible paradigm for handling diverse and unpredictable degradations in real-world scenarios. Existing agents typically formula…
PhyMRI-SR: Toward Physics-Aware MRI Image Super-Resolution
Lihua Wei, Huatong Gao, Jia Gong +4
Magnetic resonance imaging (MRI) super-resolution is vital for improving diagnostic accessibility, yet most methods treat it as a deterministic mapping from a fixed low-resolution…
Learning Adaptive Dynamical Features via Multi- Liquid-Mamba for All-in-one Image Restoration
Hu Gao, Changshuo Wang, Yulong Chen +1
Image restoration aims to recover high-quality images from degraded observations. Recent Mamba-based image restoration models have demonstrated strong potential in modeling long-ra…
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…