3 papers
cs.CV2025
DiNAT-IR: Exploring Dilated Neighborhood Attention for High-Quality Image Restoration
Hanzhou Liu, Binghan Li, Chengkai Liu +1
Transformers, with their self-attention mechanisms for modeling long-range dependencies, have become a dominant paradigm in image restoration tasks. However, the high computational…
cs.CV2025
XYScanNet: A State Space Model for Single Image Deblurring
Hanzhou Liu, Chengkai Liu, Jiacong Xu +2
Deep state-space models (SSMs), like recent Mamba architectures, are emerging as a promising alternative to CNN and Transformer networks. Existing Mamba-based restoration methods p…
cs.CV2025
DeblurDiNAT: A Compact Model with Exceptional Generalization and Visual Fidelity on Unseen Domains
Hanzhou Liu, Binghan Li, Chengkai Liu +1
Recent deblurring networks have effectively restored clear images from the blurred ones. However, they often struggle with generalization to unknown domains. Moreover, these models…