10 papers · 1 filter
L2P: Unlocking Latent Potential for Pixel Generation
Zhennan Chen, Junwei Zhu, Xu Chen +7
Pixel diffusion models have recently regained attention for visual generation. However, training advanced pixel-space models from scratch demands prohibitive computational and data…
DegBins: Degradation-Driven Binning for Depth Super-Resolution
Zhiqiang Yan, Zhengxue Wang, Jian Yang +1
Depth super-resolution (DSR) aims to recover a high-resolution (HR) depth map from its low-resolution (LR) counterpart. With color image guidance, this task is typically formulated…
Noise-Started One-Step Real-World Super-Resolution via LR-Conditioned SplitMeanFlow and GAN Refinement
Wei Zhu, Kai Zhang, Yu Zheng +3
Pre-trained text-to-image (T2I) diffusion models have shown strong potential for real-world image super-resolution (Real-ISR), owing to their noise-started generation process that…
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…
PASDiff: Physics-Aware Semantic Guidance for Joint Real-World Low-Light Face Enhancement and Restoration
Yilin Ni, Wenjie Li, Zhengxue Wang +3
Face images captured in real-world low light suffer multiple degradations-low illumination, blur, noise, and low visibility, etc. Existing cascaded solutions often suffer from seve…
Multi-Order Matching Network for Alignment-Free Depth Super-Resolution
Zhengxue Wang, Zhiqiang Yan, Yuan Wu +3
Recent guided depth super-resolution methods are premised on the assumption of strict spatial alignment between depth and RGB, achieving high-quality depth reconstruction. However,…