2 citations · 2 across the 11 of their papers we have counts for
6 papers · 1 filter
Restore Text First, Enhance Image Later: Two-Stage Scene Text Image Super-Resolution with Glyph Structure Guidance
Minxing Luo, Linlong Fan, Wang Qiushi +7
Current image super-resolution methods show strong performance on natural images but distort text, creating a fundamental trade-off between image quality and textual readability. T…
Ultra-High-Definition Reference-Based Landmark Image Super-Resolution with Generative Diffusion Prior
Zhenning Shi, Zizheng Yan, Yuhang Yu +6
Reference-based Image Super-Resolution (RefSR) aims to restore a low-resolution (LR) image by utilizing the semantic and texture information from an additional reference high-resol…
GSFixer: Improving 3D Gaussian Splatting with Reference-Guided Video Diffusion Priors
Xingyilang Yin, Qi Zhang, Jiahao Chang +6
Reconstructing 3D scenes using 3D Gaussian Splatting (3DGS) from sparse views is an ill-posed problem due to insufficient information, often resulting in noticeable artifacts. Whil…
TinySR: Pruning Diffusion for Real-World Image Super-Resolution
Linwei Dong, Qingnan Fan, Yuhang Yu +4
Real-world image super-resolution (Real-ISR) focuses on recovering high-quality images from low-resolution inputs that suffer from complex degradations like noise, blur, and compre…
BokehDiff: Neural Lens Blur with One-Step Diffusion
Chengxuan Zhu, Qingnan Fan, Qi Zhang +4
We introduce BokehDiff, a novel lens blur rendering method that achieves physically accurate and visually appealing outcomes, with the help of generative diffusion prior. Previous…
Text-Aware Real-World Image Super-Resolution via Diffusion Model with Joint Segmentation Decoders
Qiming Hu, Linlong Fan, Yiyan Luo +3
The introduction of generative models has significantly advanced image super-resolution (SR) in handling real-world degradations. However, they often incur fidelity-related issues,…