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cs.CV2026
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…
cs.CV2026
DRNet: All-in-One Image Restoration via Prior-Guided Dynamic Reparameterization
Ao Li, Xiaoning Liu, Sheng Li +5
All-in-one image restoration aims to handle diverse degradations within a single model. However, existing methods often suffer from three key limitations: 1) per-input computationa…
cs.CV2024
UnSAMFlow: Unsupervised Optical Flow Guided by Segment Anything Model
Shuai Yuan, Lei Luo, Zhuo Hui +4
Traditional unsupervised optical flow methods are vulnerable to occlusions and motion boundaries due to lack of object-level information. Therefore, we propose UnSAMFlow, an unsupe…