3 papers
cs.CV2026
Preserving Full Degradation Details for Blind Image Super-Resolution
Hongda Liu, Longguang Wang, Ye Zhang +3
The performance of image super-resolution relies heavily on the accuracy of degradation information, especially under blind settings. Due to the absence of true degradation models…
cs.CV2025
Pluggable Style Representation Learning for Multi-Style Transfer
Hongda Liu, Longguang Wang, Weijun Guan +2
Due to the high diversity of image styles, the scalability to various styles plays a critical role in real-world applications. To accommodate a large amount of styles, previous mul…
cs.CV2025
SaMam: Style-aware State Space Model for Arbitrary Image Style Transfer
Hongda Liu, Longguang Wang, Ye Zhang +2
Global effective receptive field plays a crucial role for image style transfer (ST) to obtain high-quality stylized results. However, existing ST backbones (e.g., CNNs and Transfor…