扫描聚类而非像素：面向高效超高清图像复原的聚类中心范式
Scan Clusters, Not Pixels: A Cluster-Centric Paradigm for Efficient Ultra-high-definition Image Restoration
将像素级序列扫描替换为聚类中心序列扫描，为超高清图像复原提供线性复杂度且保留全局上下文的新范式，可迁移到其他需要处理海量像素的视觉任务。
原文依据 shifting from pixel-serial to cluster-serial scanning
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论超高清（UHD）图像复原正陷入可扩展性危机：现有模型受限于逐像素操作，需要不可持续的计算量。尽管 Mamba 等状态空间模型（SSM）承诺线性复杂度，但其像素串行扫描对于超高清内容中的数百万像素仍是根本瓶颈。我们提出：必须处理每个像素才能理解图像吗？本文引入 C^2SSM，一种视觉状态空间模型，通过从像素串行扫描转向聚类串行扫描来打破这一禁忌。我们的核心发现是，超高清图像丰富的特征分布可以通过神经参数化混合模型蒸馏为一组稀疏的语义中心。C^2SSM 利用这一点将全局建模重构为一种新颖的双路径过程：先对少量聚类中心进行扫描和推理，然后通过有原则的相似性分布将全局上下文扩散回所有像素，同时一个轻量级调制器保留精细细节。这种以聚类为中心的范式在效率上实现了决定性飞跃，大幅削减计算成本，同时在五项超高清复原任务上确立了新的最优结果。C^2SSM 不仅是一个解决方案，更为高效的大规模视觉开辟了新道路：扫描聚类，而非像素。代码见 https://github.com/5chen/C2SSM。
英文原文摘要 1354 字符
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^2SSM, 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^2SSM 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^2SSM charts a new course for efficient large-scale vision: scan clusters, not pixels. The code is available at https://github.com/5chen/C2SSM.