Zero-Shot Inference-Time Rectification for Real-World Arbitrary-Scale Super-Resolution

Arbitrary-scale single image super-resolution (ASSR) has achieved tremendous success under synthetic degradations, but still faces performance deterioration in real-world scenarios where complex distortions are highly entangled with continuous zooming scales. While constructing paired real-world datasets with fractional scales attempts to alleviate this gap, such physical data is fundamentally constrained by discrete scale coverage and hardware-related biases. To overcome these limitations, we propose the Continuous Degradation Rectifier (CDR), a plug-and-play generative proxy that achieves zero-shot domain adaptation at inference time without requiring any real-world paired training data. Designed for any off-the-shelf ASSR model pre-trained on synthetic data, CDR leverages a conditional diffusion process to dynamically project scale-entangled real-world inputs into a canonical bicubic latent space, rectifying the continuous degradation manifold. Specifically, CDR achieves this through a progressive orthogonal decoupling mechanism: it first purifies scale-invariant content from real-world low-resolution images via a discrete codebook bottleneck, and subsequently extracts scaleindependent degradation and continuous scale modulation from unpaired bicubically downsampled reference image, which are orthogonally trained by scale-equivariant contrastive learning. Finally, these orthogonal representations are seamlessly integrated via scale-adaptive bandwidth modulation to guide the reverse diffusion process. Extensive experiments on real-world benchmarks demonstrate that equipping frozen, off-the-shelf ASSR models with CDR not only bypasses the need for domain-specific retraining but also establishes a new state-of-the-art.