5 citations · 5 across the 8 of their papers we have counts for
11 papers
DriftSR: One-Step Real-World Image Super-Resolution via Distribution Drifting
Wei Zhu, Kai Zhang, Yu Zheng +4
One-step real-world image super-resolution (Real-ISR) offers efficient inference, but recovering realistic and perceptually rich details often relies on score distillation or adver…
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,…
Transformer-Progressive Mamba Network for Lightweight Image Super-Resolution
Sichen Guo, Wenjie Li, Yuanyang Liu +3
Recently, Mamba-based super-resolution (SR) methods have demonstrated the ability to capture global receptive fields with linear complexity, addressing the quadratic computational…
FADPNet: Frequency-Aware Dual-Path Network for Face Super-Resolution
Siyu Xu, Wenjie Li, Guangwei Gao +3
Face super-resolution (FSR) under limited computational budgets remains challenging. Existing methods often treat all facial pixels equally, leading to suboptimal resource allocati…
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…
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…