2 citations · 3 across the 7 of their papers we have counts for
10 papers
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…
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…
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…
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…
InstructRestore: Region-Customized Image Restoration with Human Instructions
Shuaizheng Liu, Jianqi Ma, Lingchen Sun +2
Despite the significant progress in diffusion prior-based image restoration, most existing methods apply uniform processing to the entire image, lacking the capability to perform r…
Perceive, Understand and Restore: Real-World Image Super-Resolution with Autoregressive Multimodal Generative Models
Hongyang Wei, Shuaizheng Liu, Chun Yuan +1
By leveraging the generative priors from pre-trained text-to-image diffusion models, significant progress has been made in real-world image super-resolution (Real-ISR). However, th…
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…
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…
Perception-Distortion Balanced Super-Resolution: A Multi-Objective Optimization Perspective
Lingchen Sun, Jie Liang, Shuaizheng Liu +2
High perceptual quality and low distortion degree are two important goals in image restoration tasks such as super-resolution (SR). Most of the existing SR methods aim to achieve t…