Frequency-Guided Temporal Correction for Hallucination Suppression in Diffusion-Based Real-World Image Super-Resolution

Diffusion models perform strongly in real-world image super-resolution (Real-ISR), yet their generative priors can introduce structural distortions and spurious details. Without ground truth (GT) at inference, distinguishing useful refinement from hallucinations remains challenging. Using GT only for offline analysis, we examine how deviations from a selected early prediction and recent prediction history relate to reconstruction errors within each frequency band. We find a frequency-dependent reference preference: the selected early prediction provides stronger error-related cues in the low-frequency band, whereas recent prediction history is more informative in the high-frequency band. Based on this finding, we propose Frequency-Guided Temporal Correction (FGTC), a training-free and plug-and-play framework for multi-step diffusion-based Real-ISR. FGTC selects a structurally reliable early low-frequency reference and maintains recent high-frequency prediction history, then performs frequency-decoupled correction with deviation-adaptive strength before feeding the corrected prediction back into the original reverse diffusion process. Experiments on three benchmarks with StableSR and SeeSR show consistent improvements in reconstruction fidelity, including PSNR gains of 0.92–1.31 dB, while reducing structural distortions and spurious textures without backbone retraining or GT at inference.