{"type":"ScholarlyArticle","title":"Semantic-Guided Diffusion Model for Single-Step Image Super-Resolution","authors":[{"name":"Zihang Liu","slug":"zihang-liu","given":"Zihang","family":"Liu","affiliation":""},{"name":"Zhenyu Zhang","slug":"zhenyu-zhang","given":"Zhenyu","family":"Zhang","affiliation":""},{"name":"Hao Tang","slug":"hao-tang","given":"Hao","family":"Tang","affiliation":""}],"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.\r\nTo 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.\r\nExtensive experiments on both real-world and synthetic datasets demonstrate that SAMSR significantly improves perceptual quality and detail recovery, particularly in semantically complex images.","doi":"10.24963/ijcai.2025/186","url":"https://ijcai.org/proceedings/2025/186","pdf":"https://ijcai.org/proceedings/2025/0186.pdf","markdown":"https://ijcai.org/proceedings/2025/186.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/186","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Computer Vision","track":"Main Track","number":186,"pages":{"first":1666,"last":1674},"keywords":["Computer Vision: CV: Low-level Vision"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}