Arbitrary-Scale SR (ASISR) remains fundamentally limited by cross-scale distribution shift: once the inference scale leaves the training range, noise, blur, and artifacts accumulate sharply. We revisit this challenge from a cross-scale distribution transition perspective and propose CASR, a simple yet highly efficient cyclic SR framework that reformulates ultra-magnification as a sequence of in-distribution scale transitions. This design ensures stable inference at arbitrary scales while requiring only a single model. CASR tackles two major bottlenecks: distribution drift across iterations and patch-wise diffusion inconsistencies. The proposed SSAM module aligns structural distributions via superpixel aggregation, preventing error accumulation, while SARM module restores high-frequency textures by enforcing correlation-guided consistency and preserving self-similarity structure through correlation alignment. Despite using only a single model, our approach significantly reduces distribution drift, preserves long-range texture consistency, and achieves superior generalization even at extreme magnification.

翻译：任意尺度超分辨率（ASISR）本质上受限于跨尺度分布偏移：一旦推理尺度脱离训练范围，噪声、模糊和伪影便会急剧累积。我们重新审视了此挑战，并从一个跨尺度分布过渡的角度出发，提出了CASR——一个简单但高效的循环超分辨率框架。该框架将超放大问题重构为一系列分布内尺度过渡的序列。此设计确保在仅使用单一模型的情况下，也能在任意尺度上进行稳定推理。CASR解决了两个主要瓶颈：跨迭代的分布漂移以及块状扩散不一致性。所提出的SSAM模块通过超像素聚合来对齐结构分布，防止误差累积；而SARM模块则通过强制执行相关性引导的一致性，并通过相关性对齐保持自相似性结构，从而恢复高频纹理。尽管仅使用单一模型，我们的方法显著减少了分布漂移，保持了长程纹理一致性，并即便在极端放大倍数下也实现了优越的泛化能力。