3 papers
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…
eess.IV2025
Training Neural Networks on RAW and HDR Images for Restoration Tasks
Andrew Yanzhe Ke, Lei Luo, Xiaoyu Xiang +4
The vast majority of standard image and video content available online is represented in display-encoded color spaces, in which pixel values are conveniently scaled to a limited ra…