arXiv Computer Vision
1d ago

DiNAT-IR: Exploring Dilated Neighborhood Attention for High-Quality Image Restoration

DiNAT-IR introduces Dilated Neighborhood Attention (DiNA) to balance global context and local precision in image restoration. By combining sliding-window attention with mixed dilation factors and a channel-aware module, it addresses the limitations of channel-wise self-attention and improves pixel-level accuracy. The resulting Transformer-based architecture delivers competitive performance across multiple benchmarks for low-level vision tasks.

By Hanzhou Liu, Binghan Li, Chengkai Liu, Mi Lu
arXiv AI
Jul 7

MambaLIE: Scene Light Intensity-Boosted Low-Light Image Enhancement with State Space Model

arXiv:2607. 03013v1 Announce Type: cross Abstract: Images captured by consumer electronic devices, such as mobile phones and digital cameras, often suffer from low-light degradation due to sensor limitations and imaging pipelines, which degrades visual quality and affects downstream vision tasks.

By Wanshu Fan, Xiangyu Li, Cong Wang, Kin-man Lam, Xin Yang, Haiyan Zhang, Dongsheng Zhou