6 papers
TEASR: Training-Efficient Any-Step Diffusion Transformer for Real-World Image Super-Resolution
Xiang Gao, Chenxin Zhu, Yushun Fang +2
Diffusion models excel in Real-World Image Super-Resolution (Real-ISR) due to their powerful generative priors but suffer from slow iterative sampling. Although existing one-step d…
LL-Bench: Rethinking Low-Level Vision Evaluation in the Era of Large-Scale Generative Models
Lu Liu, Huiyu Duan, Chenxin Zhu +6
Large-scale generative models have demonstrated remarkable capabilities across image generation and editing tasks. However, their performance in low-level vision tasks, which requi…
A2BFR: Attribute-Aware Blind Face Restoration
Chenxin Zhu, Yushun Fang, Lu Liu +5
Blind face restoration (BFR) aims to recover high-quality facial images from degraded inputs, yet its inherently ill-posed nature leads to ambiguous and uncontrollable solutions. R…
Nighttime Hazy Image Enhancement via Progressively and Mutually Reinforcing Night-Haze Priors
Chen Zhu, Huiwen Zhang, Mu He +2
Enhancing the visibility of nighttime hazy images is challenging due to the complex degradation distributions. Existing methods mainly address a single type of degradation (e.g., h…
API: Empowering Generalizable Real-World Image Dehazing via Adaptive Patch Importance Learning
Chen Zhu, Huiwen Zhang, Yujie Li +2
Real-world image dehazing is a fundamental yet challenging task in low-level vision. Existing learning-based methods often suffer from significant performance degradation when appl…
EA-ViT: Efficient Adaptation for Elastic Vision Transformer
Chen Zhu, Wangbo Zhao, Huiwen Zhang +9
Vision Transformers (ViTs) have emerged as a foundational model in computer vision, excelling in generalization and adaptation to downstream tasks. However, deploying ViTs to suppo…