---
title: "Semantic-Guided Diffusion Model for Single-Step Image Super-Resolution"
authors:
  - "Zihang Liu"
  - "Zhenyu Zhang"
  - "Hao Tang"
doi: "10.24963/ijcai.2025/186"
url: "https://ijcai.org/proceedings/2025/186"
pdf: "https://ijcai.org/proceedings/2025/0186.pdf"
venue: "Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"
year: 2025
pages: "1666-1674"
track: "Main Track"
keywords:
  - "Computer Vision: CV: Low-level Vision"
---

# Semantic-Guided Diffusion Model for Single-Step Image Super-Resolution

Zihang Liu, Zhenyu Zhang, Hao Tang

*Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence*, pages 1666-1674. DOI: [10.24963/ijcai.2025/186](https://doi.org/10.24963/ijcai.2025/186)

## Abstract

Diffusion-based image super-resolution (SR) methods have demonstrated remarkable performance. Recent advancements have introduced deterministic sampling processes that reduce inference from 15 iterative steps to a single step, thereby significantly improving the inference speed of existing diffusion models. However, their efficiency remains limited when handling complex semantic regions due to the single-step inference.
To address this limitation, we propose SAMSR, a semantic-guided diffusion framework that incorporates semantic segmentation masks into the sampling process. Specifically, we introduce the SAM-Noise Module, which refines Gaussian noise using segmentation masks to preserve spatial and semantic features. Furthermore, we develop a pixel-wise sampling strategy that dynamically adjusts the residual transfer rate and noise strength based on pixel-level semantic weights, prioritizing semantically rich regions during the diffusion process. To enhance model training, we also propose a semantic consistency loss, which aligns pixel-wise semantic weights between predictions and ground truth.
Extensive experiments on both real-world and synthetic datasets demonstrate that SAMSR significantly improves perceptual quality and detail recovery, particularly in semantically complex images.