XYScanNet: A State Space Model for Single Image Deblurring
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The Flow has not summarised this story yet — read it at arXiv Computer Vision.
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.
arXiv:2505.16157v3 Announce Type: replace Abstract: Transformer-based models have made remarkable progress in image restoration (IR) tasks. However, the quadratic complexity of self-attention in Tran...
arXiv:2609.24337v1 Announce Type: new Abstract: While Mamba-based models have shown strong potential for long sequence modeling, adapting them to vision is challenging due to the requirement of local...
arXiv:2404.06135v4 Announce Type: replace Abstract: The Transformer architecture has achieved remarkable success in natural language processing and high-level vision tasks over the past few years. Ho...
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.
arXiv:2608. 09482v1 Announce Type: cross Abstract: All-in-one image restoration is a unified low-level vision task that aims to effectively recover high-quality images from inputs degraded by various types and levels of corruption using a single model.