Faithful or Hallucinated? A Fidelity Metric and Benchmark for Real-World Super-Resolution

Real-world image super-resolution aims to restore high-resolution images from low-resolution inputs under unknown degradations. Reliable SR should be both perceptually appealing and faithful to the LR observation, meaning that restored content should not contradict the visual evidence in the input. Automatically assessing such fidelity is challenging without paired HR references. Existing metrics either ignore LR consistency or rely on degraded LR proxies that may penalize valid restoration changes. Human judgments can identify input-inconsistent content but are costly to collect at scale. These challenges motivate an HR-reference-free metric for LR–SR fidelity assessment. To this end, we propose FAIM, a Fidelity-Aware Input-consistency Metric for real-world super-resolution. FAIM leverages frozen multi-layer DINOv3 features to assess LR–SR fidelity by jointly modeling spatial discrepancies and region-level contextual inconsistencies. We further introduce FAIM-Bench, a human-annotated benchmark containing 3,720 LR–SR pairs constructed from 465 real-world LR images and eight diffusion-based $\times 4$ SR models. It includes separate ratings of LR quality, SR perceptual quality, and LR–SR fidelity, together with 13,020 unique pairwise fidelity comparisons. Experiments show that FAIM agrees more strongly with human fidelity judgments than existing metrics, including on unseen SR methods. In addition, FAIM supports zero-shot artifact localization without additional localization supervision.