3 citations · 3 across the 16 of their papers we have counts for
6 papers · 1 filter
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…