提出query感知的注意力选择机制，提升图像超分辨率效率与质量
Crafting Query-Aware Selective Attention for Single Image Super-Resolution
- 根据查询相似性动态选择关键窗口，聚焦重要区域
- 在城市数据集上最高提升0.14 dB PSNR，计算复杂度线性增长
- 适合追求高效高质图像重建的研究者与工程师
单图像超分辨率（SISR）旨在从低分辨率输入中重建高分辨率图像，增强细节。尽管基于视觉变换器（ViT）的模型通过捕捉长距离依赖关系提升了性能，但其存在二次计算开销，或采用不显式关注查询相关区域的选择性注意力机制。现有工作忽略了如何有效设计适用于SISR的选择性注意力机制。本文提出SSCAN，通过查询相似性动态选择最相关的键值窗口，确保特征提取聚焦且高效。相比全局或启发式应用注意力的方法，本方法引入查询感知的窗口选择策略，使注意力计算更贴合重要图像区域。通过固定大小窗口设计，SSCAN降低内存使用，并实现线性令牌-令牌复杂度，具备大图像可扩展性。实验表明，SSCAN优于现有基于注意力的SISR方法，在城市数据集上最高实现0.14 dB PSNR提升，兼顾计算效率与重建质量。
原文摘要 · Abstract (English)
Single Image Super-Resolution (SISR) reconstructs high-resolution images from low-resolution inputs, enhancing image details. While Vision Transformer (ViT)-based models improve SISR by capturing long-range dependencies, they suffer from quadratic computational costs or employ selective attention mechanisms that do not explicitly focus on query-relevant regions. Despite these advancements, prior work has overlooked how selective attention mechanisms should be effectively designed for SISR. We propose SSCAN, which dynamically selects the most relevant key-value windows based on query similarity, ensuring focused feature extraction while maintaining efficiency. In contrast to prior approaches that apply attention globally or heuristically, our method introduces a query-aware window selection strategy that better aligns attention computation with important image regions. By incorporating fixed-sized windows, SSCAN reduces memory usage and enforces linear token-to-token complexity, making it scalable for large images. Our experiments demonstrate that SSCAN outperforms existing attention-based SISR methods, achieving up to 0.14 dB PSNR improvement on urban datasets, guaranteeing both computational efficiency and reconstruction quality in SISR.
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