Abstract
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 SDAM module aligns structural distributions via superpixel aggregation, preventing error accumulation, while SARM module restores high-frequency textures by enforcing autocorrelation and embedding LR self-similarity priors. 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.
Abstract (translated)
任意尺度超分辨率（Arbitrary-Scale SR，ASISR）在本质上受到跨尺度分布偏移的限制：一旦推断尺度超出训练范围，噪声、模糊和伪影就会迅速累积。我们从跨尺度分布转换的角度重新审视这一挑战，并提出了CASR，这是一种简单但高度高效的循环SR框架，将超倍率重构重定义为一系列同分布尺度变换的过程。这种设计确保了在任意尺度上进行稳定的推断，同时只需使用一个模型即可实现。 CASR解决了两个主要瓶颈：跨迭代的分布漂移和块级扩散的一致性问题。我们提出的SDAM模块通过超级像素聚合对齐结构分布，防止错误累积；而SARM模块则通过强制自相关并嵌入低分辨率自我相似先验来恢复高频纹理。尽管仅使用一个模型，我们的方法显著减少了分布漂移，保持了长程纹理的一致性，并在极端放大时实现了优越的泛化能力。 简而言之，CASR提供了一种新颖的方法来解决ASISR中的关键问题，从而实现更加稳定、高效且具有一般性的超分辨率重构。
URL
https://arxiv.org/abs/2602.22159
