Abstract
Arbitrary-scale super-resolution (ASSR) overcomes the limitation of traditional super-resolution (SR) methods that operate only at fixed scales (e.g., 4x), enabling a single model to handle arbitrary magnification. Most existing ASSR approaches rely on implicit neural representation (INR), but its regression-driven feature extraction and aggregation intrinsically limit the ability to synthesize fine details, leading to low realism. Recent diffusion-based realistic image super-resolution (Real-ISR) models leverage powerful pre-trained diffusion priors and show impressive results at the 4x setting. We observe that they can also achieve ASSR because the diffusion prior implicitly adapts to scale by encouraging high-realism generation. However, without explicit scale control, the diffusion process cannot be properly adjusted for different magnification levels, resulting in excessive hallucination or blurry outputs, especially under ultra-high scales. To address these issues, we propose OmniScaleSR, a diffusion-based realistic arbitrary-scale SR framework designed to achieve both high fidelity and high realism. We introduce explicit, diffusion-native scale control mechanisms that work synergistically with implicit scale adaptation, enabling scale-aware and content-aware modulation of the diffusion process. In addition, we incorporate multi-domain fidelity enhancement designs to further improve reconstruction accuracy. Extensive experiments on bicubic degradation benchmarks and real-world datasets show that OmniScaleSR surpasses state-of-the-art methods in both fidelity and perceptual realism, with particularly strong performance at large magnification factors. Code will be released at this https URL.
Abstract (translated)
任意尺度超分辨率（ASSR）克服了传统超分辨率（SR）方法仅在固定尺度下操作的限制，使得单一模型能够处理任意级别的放大。大多数现有的ASSR方法依赖于隐式神经表示（INR），但其基于回归驱动的特征提取和聚合机制内在地限制了合成精细细节的能力，导致现实感较低。最近基于扩散的真实图像超分辨率（Real-ISR）模型利用强大的预训练扩散先验，并在4倍放大设置下表现出令人印象深刻的结果。我们观察到它们也可以实现ASSR，因为扩散先验通过鼓励高现实感生成而隐式地适应尺度变化。然而，在没有显式尺度控制的情况下，扩散过程无法针对不同的放大级别进行适当调整，这会导致过多的幻觉或模糊输出，尤其是在超高尺度下。 为了解决这些问题，我们提出了OmniScaleSR，这是一种基于扩散的真实任意尺度超分辨率框架，旨在同时实现高保真度和高现实感。我们引入了显式的、与扩散本源兼容的尺度控制机制，这些机制与隐式尺度适应协同工作，能够以尺度感知和内容感知的方式调节扩散过程。此外，我们将多域保真度增强设计纳入其中，进一步提高重建精度。 在双三次降级基准测试和现实世界数据集上进行的大量实验表明，OmniScaleSR在保真度和感知真实感方面超越了最先进的方法，在大放大倍数下表现尤为突出。代码将在[此处](此链接应指向代码发布页面)提供。
URL
https://arxiv.org/abs/2512.04699
