7 papers
DiT4SR: Taming Diffusion Transformer for Real-World Image Super-Resolution
Zheng-Peng Duan, Jiawei Zhang, Xin Jin +6
Large-scale pre-trained diffusion models are becoming increasingly popular in solving the Real-World Image Super-Resolution (Real-ISR) problem because of their rich generative prio…
Frequency-domain Learning with Kernel Prior for Blind Image Deblurring
Jixiang Sun, Fei Lei, Jiawei Zhang +2
While achieving excellent results on various datasets, many deep learning methods for image deblurring suffer from limited generalization capabilities with out-of-domain data. This…
A Diffusion-Based Framework for Occluded Object Movement
Zheng-Peng Duan, Jiawei Zhang, Siyu Liu +5
Seamlessly moving objects within a scene is a common requirement for image editing, but it is still a challenge for existing editing methods. Especially for real-world images, the…
DeblurDiff: Real-World Image Deblurring with Generative Diffusion Models
Lingshun Kong, Jiawei Zhang, Dongqing Zou +4
Diffusion models have achieved significant progress in image generation. The pre-trained Stable Diffusion (SD) models are helpful for image deblurring by providing clear image prio…
Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution
Jiahua Xiao, Jiawei Zhang, Dongqing Zou +3
Real-world image super-resolution (Real-ISR) has achieved a remarkable leap by leveraging large-scale text-to-image models, enabling realistic image restoration from given recognit…
Drift to Remember
Jin Du, Xinhe Zhang, Hao Shen +7
Lifelong learning in artificial intelligence (AI) aims to mimic the biological brain's ability to continuously learn and retain knowledge, yet it faces challenges such as catastrop…