用聚类代替像素扫描，大幅提升超高清图像修复效率。
Scan Clusters, Not Pixels: A Cluster-Centric Paradigm for Efficient Ultra-high-definition Image Restoration
- 以聚类为中心替代逐像素处理，降低计算负担。
- 在五个超高清修复任务上达到新SOTA，计算量显著下降。
- 适合大尺寸图像处理、资源受限场景的视觉模型设计。
超高清（UHD）图像修复面临可扩展性危机：现有模型依赖逐像素操作，计算开销难以承受。尽管状态空间模型（SSMs）如Mamba具备线性复杂度，但其逐像素扫描仍是百万级像素内容的根本瓶颈。本文提出问题：是否必须处理每个像素才能理解图像？为此，我们引入C²SSM，一种视觉状态空间模型，打破这一常规，将扫描方式从像素序列转为聚类序列。核心发现是，通过神经参数化混合模型，可将UHD图像丰富的特征分布压缩为稀疏的语义中心点。C²SSM将全局建模重构为双路径流程：先对少量聚类中心进行扫描与推理，再通过合理的相似度分布将全局上下文回传至所有像素，同时轻量调制器保留细节。该聚类中心范式实现效率飞跃，在多个超高清修复任务中大幅降低计算成本并达到新最佳性能。这不仅是一种解决方案，更指明了高效大规模视觉处理的新方向：扫描聚类，而非像素。
原文摘要 · Abstract (English)
Ultra-High-Definition (UHD) image restoration is trapped in a scalability crisis: existing models, bound to pixel-wise operations, demand unsustainable computation. While state space models (SSMs) like Mamba promise linear complexity, their pixel-serial scanning remains a fundamental bottleneck for the millions of pixels in UHD content. We ask: must we process every pixel to understand the image? This paper introduces C$^2$SSM, a visual state space model that breaks this taboo by shifting from pixel-serial to cluster-serial scanning. Our core discovery is that the rich feature distribution of a UHD image can be distilled into a sparse set of semantic centroids via a neural-parameterized mixture model. C$^2$SSM leverages this to reformulate global modeling into a novel dual-path process: it scans and reasons over a handful of cluster centers, then diffuses the global context back to all pixels through a principled similarity distribution, all while a lightweight modulator preserves fine details. This cluster-centric paradigm achieves a decisive leap in efficiency, slashing computational costs while establishing new state-of-the-art results across five UHD restoration tasks. More than a solution, C$^2$SSM charts a new course for efficient large-scale vision: scan clusters, not pixels.
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