Semantic-Guided Diffusion Model for Single-Step Image Super-Resolution
This work addresses a specific bottleneck in efficient image super-resolution for applications requiring high-quality upscaling of semantically complex images.
The paper tackles the problem of single-step diffusion models for image super-resolution struggling with complex semantic regions by proposing SAMSR, a semantic-guided diffusion framework that incorporates segmentation masks into the sampling process, resulting in significant improvements in perceptual quality and detail recovery on real-world and synthetic datasets.
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. Our code is released at https://github.com/Liu-Zihang/SAMSR.