OmniScaleSR: Unleashing Scale-Controlled Diffusion Prior for Faithful and Realistic Arbitrary-Scale Image Super-Resolution

Xinning Chai, Zhengxue Cheng, Yuhong Zhang, Hengsheng Zhang, Yingsheng Qin, Yucai Yang, Rong Xie, Li Song

Abstract: Arbitrary-scale super-resolution (ASSR) overcomes the limitation of traditional super-resolution (SR) that works only at a fixed scale (e.g., ×4), enabling a single model to achieve arbitrary-scale SR. Most ASSR methods explicitly incorporate implicit neural representation (INR) to achieve ASSR, but INR’s inherently regression-driven feature extraction and aggregation nature restricts their capacity to synthesize meticulous details, leading to low realism. Recently, diffusion-based realistic image super-resolution (Real-ISR) methods leverage the pre-trained diffusion prior and have shown promising results at ×4 scale. We find that they could also achieve ASSR because the powerful pre-trained diffusion prior implicitly employs SR scale adaptation by encouraging the model to always generate high-realism images. However, due to the lack of explicit SR scale controls, the model fails to effectively manage the diffusion behavior according to different SR scales, causing either excessive hallucination or blurry results, especially for ultra-high magnification. To address these limitations, we propose OmniScaleSR, a novel diffusion-based realistic arbitrary-scale super-resolution (Real-ASSR) method to achieve both high fidelity and high-realism ASSR. We introduce explicit diffusion-native SR scale controls, which could be elegantly coupled with the implicit scale adaptation, unleashing scale-controlled diffusion prior to dynamically managing the diffusion behavior in a content- and scale-aware manner. Furthermore, we incorporate multi-domain fidelity enhancement designs to achieve more faithful reconstruction. Extensive experiments on both bicubic degradation benchmarks and real-world datasets demonstrate that OmniScaleSR consistently outperforms state-of-the-art methods in terms of both fidelity and perceptual realism, with especially strong performance under high-magnification scenarios. Codes will be at https://github.com/chaixinning/OmniScaleSR.

External IDs:doi:10.1109/tcsvt.2025.3642578