11 citations · 33 across the 16 of their papers we have counts for
20 papers
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…
DPO-SR: Direct Perceptual Preference Optimization for Real-World Image Super-Resolution
Rongyuan Wu, Lingchen Sun, Zhengqiang Zhang +5
Benefiting from pre-trained text-to-image (T2I) diffusion models, real-world image super-resolution (Real-ISR) methods can synthesize rich and realistic details. However, due to th…
NSARM: Next-Scale Autoregressive Modeling for Robust Real-World Image Super-Resolution
Xiangtao Kong, Rongyuan Wu, Shuaizheng Liu +2
Most recent real-world image super-resolution (Real-ISR) methods employ pre-trained text-to-image (T2I) diffusion models to synthesize the high-quality image either from random Gau…
GPSToken: Gaussian Parameterized Spatially-adaptive Tokenization for Image Representation and Generation
Zhengqiang Zhang, Rongyuan Wu, Lingchen Sun +1
Effective and efficient tokenization plays an important role in image representation and generation. Conventional methods, constrained by uniform 2D/1D grid tokenization, are infle…
Fine-structure Preserved Real-world Image Super-resolution via Transfer VAE Training
Qiaosi Yi, Shuai Li, Rongyuan Wu +3
Impressive results on real-world image super-resolution (Real-ISR) have been achieved by employing pre-trained stable diffusion (SD) models. However, one critical issue of such met…
NTIRE 2025 the 2nd Restore Any Image Model (RAIM) in the Wild Challenge
Jie Liang, Radu Timofte, Qiaosi Yi +7
In this paper, we present a comprehensive overview of the NTIRE 2025 challenge on the 2nd Restore Any Image Model (RAIM) in the Wild. This challenge established a new benchmark for…
One-Step Diffusion for Detail-Rich and Temporally Consistent Video Super-Resolution
Yujing Sun, Lingchen Sun, Shuaizheng Liu +3
It is a challenging problem to reproduce rich spatial details while maintaining temporal consistency in real-world video super-resolution (Real-VSR), especially when we leverage pr…
Progressive Rendering Distillation: Adapting Stable Diffusion for Instant Text-to-Mesh Generation without 3D Data
Zhiyuan Ma, Xinyue Liang, Rongyuan Wu +3
It is highly desirable to obtain a model that can generate high-quality 3D meshes from text prompts in just seconds. While recent attempts have adapted pre-trained text-to-image di…
Pixel-level and Semantic-level Adjustable Super-resolution: A Dual-LoRA Approach
Lingchen Sun, Rongyuan Wu, Zhiyuan Ma +3
Diffusion prior-based methods have shown impressive results in real-world image super-resolution (SR). However, most existing methods entangle pixel-level and semantic-level SR obj…
Adversarial Diffusion Compression for Real-World Image Super-Resolution
Bin Chen, Gehui Li, Rongyuan Wu +4
Real-world image super-resolution (Real-ISR) aims to reconstruct high-resolution images from low-resolution inputs degraded by complex, unknown processes. While many Stable Diffusi…
One-Step Effective Diffusion Network for Real-World Image Super-Resolution
Rongyuan Wu, Lingchen Sun, Zhiyuan Ma +1
The pre-trained text-to-image diffusion models have been increasingly employed to tackle the real-world image super-resolution (Real-ISR) problem due to their powerful generative i…
NTIRE 2024 Restore Any Image Model (RAIM) in the Wild Challenge
Jie Liang, Radu Timofte, Qiaosi Yi +6
In this paper, we review the NTIRE 2024 challenge on Restore Any Image Model (RAIM) in the Wild. The RAIM challenge constructed a benchmark for image restoration in the wild, inclu…
Improving the Stability and Efficiency of Diffusion Models for Content Consistent Super-Resolution
Lingchen Sun, Rongyuan Wu, Jie Liang +3
The generative priors of pre-trained latent diffusion models (DMs) have demonstrated great potential to enhance the visual quality of image super-resolution (SR) results. However,…
SeeSR: Towards Semantics-Aware Real-World Image Super-Resolution
Rongyuan Wu, Tao Yang, Lingchen Sun +3
Owe to the powerful generative priors, the pre-trained text-to-image (T2I) diffusion models have become increasingly popular in solving the real-world image super-resolution proble…
Pixel-Aware Stable Diffusion for Realistic Image Super-resolution and Personalized Stylization
Tao Yang, Rongyuan Wu, Peiran Ren +2
Diffusion models have demonstrated impressive performance in various image generation, editing, enhancement and translation tasks. In particular, the pre-trained text-to-image stab…