55 citations · 65 across the 14 of their papers we have counts for
6 papers · 1 filter
An Intelligent Agentic System for Complex Image Restoration Problems
Kaiwen Zhu, Jinjin Gu, Zhiyuan You +2
Real-world image restoration (IR) is inherently complex and often requires combining multiple specialized models to address diverse degradations. Inspired by human problem-solving,…
Learning A Low-Level Vision Generalist via Visual Task Prompt
Xiangyu Chen, Yihao Liu, Yuandong Pu +4
Building a unified model for general low-level vision tasks holds significant research and practical value. Current methods encounter several critical issues. Multi-task restoratio…
A Preliminary Exploration Towards General Image Restoration
Xiangtao Kong, Jinjin Gu, Yihao Liu +4
Despite the tremendous success of deep models in various individual image restoration tasks, there are at least two major technical challenges preventing these works from being app…
GRIDS: Grouped Multiple-Degradation Restoration with Image Degradation Similarity
Shuo Cao, Yihao Liu, Wenlong Zhang +2
Traditional single-task image restoration methods excel in handling specific degradation types but struggle with multiple degradations. To address this limitation, we propose Group…
Towards Real-world Video Face Restoration: A New Benchmark
Ziyan Chen, Jingwen He, Xinqi Lin +2
Blind face restoration (BFR) on images has significantly progressed over the last several years, while real-world video face restoration (VFR), which is more challenging for more c…
Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wild
Fanghua Yu, Jinjin Gu, Zheyuan Li +6
We introduce SUPIR (Scaling-UP Image Restoration), a groundbreaking image restoration method that harnesses generative prior and the power of model scaling up. Leveraging multi-mod…