7 citations · 22 across the 16 of their papers we have counts for
6 papers · 2 filters
HarnessIR: Harnessing Multimodal Foundation Models for Universal Real-World Image Restoration
Xiangtao Kong, Shuaizheng Liu, Rongyuan Wu +6
Real-world low-quality images suffer from complex mixed degradations, including but not limited to noise, blur, atmospheric effects, etc. Recent agentic methods usually model real-…
PixRestore: Unified Image Restoration via Pixel Diffusion Transformer
Lingchen Sun, Rongyuan Wu, Xiangtao Kong +6
Unified image restoration (UIR) aims to recover high-quality (HQ) content from low-quality (LQ) images with different degradations using a single model. Most recent methods adapt l…
GGT-100K: Generative Ground Truth for Generalizable Real-World Image Restoration
Xiangtao Kong, Jixin Zhao, Lingchen Sun +2
Real-world image restoration (IR) is bottlenecked by the scarcity of high-quality paired training data. Synthetic datasets are abundant but often fail to model real-world degradati…
VOSR: A Vision-Only Generative Model for Image Super-Resolution
Rongyuan Wu, Lingchen Sun, Zhengqiang Zhang +4
Most of the recent generative image super-resolution (SR) methods rely on adapting large text-to-image (T2I) diffusion models pretrained on web-scale text-image data. While effecti…
GDPO-SR: Group Direct Preference Optimization for One-Step Generative Image Super-Resolution
Qiaosi Yi, Shuai Li, Rongyuan Wu +3
Recently, reinforcement learning (RL) has been employed for improving generative image super-resolution (ISR) performance. However, the current efforts are focused on multi-step ge…
Self-transcendence: Is External Feature Guidance Indispensable for Accelerating Diffusion Transformer Training?
Lingchen Sun, Rongyuan Wu, Zhengqiang Zhang +4
Recent works such as REPA have shown that guiding diffusion models with external semantic features (e.g., DINO) can significantly accelerate the training of diffusion transformers…