3 citations · 3 across the 16 of their papers we have counts for
19 papers
Fast Image Super-Resolution via Consistency Rectified Flow
Jiaqi Xu, Wenbo Li, Haoze Sun +8
Diffusion models (DMs) have demonstrated remarkable success in real-world image super-resolution (SR), yet their reliance on time-consuming multi-step sampling largely hinders thei…
V-Bridge: Bridging Video Generative Priors to Versatile Few-shot Image Restoration
Shenghe Zheng, Junpeng Jiang, Wenbo Li
Large-scale video generative models are trained on vast and diverse visual data, enabling them to internalize rich structural, semantic, and dynamic priors of the visual world. Whi…
Test-Time Preference Optimization for Image Restoration
Bingchen Li, Xin Li, Jiaqi Xu +4
Image restoration (IR) models are typically trained to recover high-quality images using L1 or LPIPS loss. To handle diverse unknown degradations, zero-shot IR methods have also be…
PocketSR: The Super-Resolution Expert in Your Pocket Mobiles
Haoze Sun, Linfeng Jiang, Fan Li +9
Real-world image super-resolution (RealSR) aims to enhance the visual quality of in-the-wild images, such as those captured by mobile phones. While existing methods leveraging larg…
Dual Prompting Image Restoration with Diffusion Transformers
Dehong Kong, Fan Li, Zhixin Wang +4
Recent state-of-the-art image restoration methods mostly adopt latent diffusion models with U-Net backbones, yet still facing challenges in achieving high-quality restoration due t…
Pixel to Gaussian: Ultra-Fast Continuous Super-Resolution with 2D Gaussian Modeling
Long Peng, Anran Wu, Wenbo Li +9
Arbitrary-scale super-resolution (ASSR) aims to reconstruct high-resolution (HR) images from low-resolution (LR) inputs with arbitrary upsampling factors using a single model, addr…
Segment Any-Quality Images with Generative Latent Space Enhancement
Guangqian Guo, Yong Guo, Xuehui Yu +3
Despite their success, Segment Anything Models (SAMs) experience significant performance drops on severely degraded, low-quality images, limiting their effectiveness in real-world…
Directing Mamba to Complex Textures: An Efficient Texture-Aware State Space Model for Image Restoration
Long Peng, Xin Di, Zhanfeng Feng +6
Image restoration aims to recover details and enhance contrast in degraded images. With the growing demand for high-quality imaging (\textit{e.g.}, 4K and 8K), achieving a balance…
UniRestorer: Universal Image Restoration via Adaptively Estimating Image Degradation at Proper Granularity
Jingbo Lin, Zhilu Zhang, Wenbo Li +4
Recently, considerable progress has been made in all-in-one image restoration. Generally, existing methods can be degradation-agnostic or degradation-aware. However, the former are…
Unveiling Hidden Details: A RAW Data-Enhanced Paradigm for Real-World Super-Resolution
Long Peng, Wenbo Li, Jiaming Guo +7
Real-world image super-resolution (Real SR) aims to generate high-fidelity, detail-rich high-resolution (HR) images from low-resolution (LR) counterparts. Existing Real SR methods…
Dog-IQA: Standard-guided Zero-shot MLLM for Mix-grained Image Quality Assessment
Kai Liu, Ziqing Zhang, Wenbo Li +5
Image quality assessment (IQA) serves as the golden standard for all models' performance in nearly all computer vision fields. However, it still suffers from poor out-of-distributi…
QMambaBSR: Burst Image Super-Resolution with Query State Space Model
Xin Di, Long Peng, Peizhe Xia +5
Burst super-resolution aims to reconstruct high-resolution images with higher quality and richer details by fusing the sub-pixel information from multiple burst low-resolution fram…
RestoreAgent: Autonomous Image Restoration Agent via Multimodal Large Language Models
Haoyu Chen, Wenbo Li, Jinjin Gu +7
Natural images captured by mobile devices often suffer from multiple types of degradation, such as noise, blur, and low light. Traditional image restoration methods require manual…
UltraPixel: Advancing Ultra-High-Resolution Image Synthesis to New Peaks
Jingjing Ren, Wenbo Li, Haoyu Chen +6
Ultra-high-resolution image generation poses great challenges, such as increased semantic planning complexity and detail synthesis difficulties, alongside substantial training reso…
DaLPSR: Leverage Degradation-Aligned Language Prompt for Real-World Image Super-Resolution
Aiwen Jiang, Zhi Wei, Long Peng +3
Image super-resolution pursuits reconstructing high-fidelity high-resolution counterpart for low-resolution image. In recent years, diffusion-based models have garnered significant…
Towards Realistic Data Generation for Real-World Super-Resolution
Long Peng, Wenbo Li, Renjing Pei +5
Existing image super-resolution (SR) techniques often fail to generalize effectively in complex real-world settings due to the significant divergence between training data and prac…
Efficient Real-world Image Super-Resolution Via Adaptive Directional Gradient Convolution
Long Peng, Yang Cao, Renjing Pei +5
Real-SR endeavors to produce high-resolution images with rich details while mitigating the impact of multiple degradation factors. Although existing methods have achieved impressiv…
Low-Res Leads the Way: Improving Generalization for Super-Resolution by Self-Supervised Learning
Haoyu Chen, Wenbo Li, Jinjin Gu +6
For image super-resolution (SR), bridging the gap between the performance on synthetic datasets and real-world degradation scenarios remains a challenge. This work introduces a nov…
CoSeR: Bridging Image and Language for Cognitive Super-Resolution
Haoze Sun, Wenbo Li, Jianzhuang Liu +5
Existing super-resolution (SR) models primarily focus on restoring local texture details, often neglecting the global semantic information within the scene. This oversight can lead…