3 papers
cs.CV2026
MFSR: MeanFlow Distillation for One Step Real-World Image Super Resolution
Ruiqing Wang, Kai Zhang, Yuanzhi Zhu +3
Diffusion- and flow-based models have advanced Real-world Image Super-Resolution (Real-ISR), but their multi-step sampling makes inference slow and hard to deploy. One-step distill…
cs.CV2024
DORNet: A Degradation Oriented and Regularized Network for Blind Depth Super-Resolution
Zhengxue Wang, Zhiqiang Yan, Jinshan Pan +3
Recent RGB-guided depth super-resolution methods have achieved impressive performance under the assumption of fixed and known degradation (e.g., bicubic downsampling). However, in…
cs.CV2024
Attention-Guided Multi-scale Interaction Network for Face Super-Resolution
Xujie Wan, Wenjie Li, Guangwei Gao +3
Recently, CNN and Transformer hybrid networks demonstrated excellent performance in face super-resolution (FSR) tasks. Since numerous features at different scales in hybrid network…