# StructSR: Refuse Spurious Details in Real-World Image Super-Resolution

arXiv: https://arxiv.org/abs/2501.05777 · HTML: https://mlsanity.com/paper/2501.05777/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jan 2025.

## Built on by (most cited first, 3 of 3)

- [Coloring the Noise: Adversarial Sobolev Alignment for Faithful Image Super Resolution](https://mlsanity.com/paper/2605.23264/) (2026)
- [Local-Global Context-Aware and Structure-Preserving Image Super-Resolution](https://mlsanity.com/paper/2510.13649/) (2025)
- [Efficient Difficulty-Aware Dynamic Routing for Diffusion-Based Real-World Image Super-Resolution](https://mlsanity.com/paper/2607.15711/) (2026)

## Abstract (arXiv)

Diffusion-based models have shown great promise in real-world image super-resolution (Real-ISR), but often generate content with structural errors and spurious texture details due to the empirical priors and illusions of these models. To address this issue, we introduce StructSR, a simple, effective, and plug-and-play method that enhances structural fidelity and suppresses spurious details for diffusion-based Real-ISR. StructSR operates without the need for additional fine-tuning, external model priors, or high-level semantic knowledge. At its core is the Structure-Aware Screening (SAS) mechanism, which identifies the image with the highest structural similarity to the low-resolution (LR) input in the early inference stage, allowing us to leverage it as a historical structure knowledge to suppress the generation of spurious details. By intervening in the diffusion inference process, StructSR seamlessly integrates with existing diffusion-based Real-ISR models. Our experimental results demonstrate that StructSR significantly improves the fidelity of structure and texture, improving the PSNR and SSIM metrics by an average of 5.27% and 9.36% on a synthetic dataset (DIV2K-Val) and 4.13% and 8.64% on two real-world datasets (RealSR and DRealSR) when integrated with four state-of-the-art diffusion-based Real-ISR methods.