Semantic-Guided Diffusion Model for Single-Step Image Super-Resolution (2505.07071v1)

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. Our code is released at https://github.com/Liu-Zihang/SAMSR.

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

The paper "Semantic-Guided Diffusion Model for Single-Step Image Super-Resolution" introduces a novel approach to image super-resolution (SR) that builds upon recent advances in diffusion models. Diffusion-based techniques have become popular for image enhancement tasks due to their ability to generate high-quality outputs. However, they typically require iterative processes, which can be computationally expensive. Recent efforts have aimed at accelerating these models, notably through deterministic sampling strategies that reduce inference time drastically.

The semantic-guided framework presented in this paper, SAMSR, seeks to improve upon existing single-step SR approaches by introducing semantic segmentation masks into the sampling process. The innovation lies in the semantic enrichment of the diffusion process through the SAM-Noise Module and pixel-wise adaptive adjustments during sampling. The SAM-Noise Module refines Gaussian noise by applying segmentation masks, thereby preserving spatial and semantic features. The pixel-wise sampling strategy dynamically alters parameters based on semantic richness, ensuring precise recovery of detailed regions.

Key Contributions

1. Semantic Integration via SAM: The use of segmentation masks derived from the Segment Anything Model (SAM) allows for the incorporation of spatial and semantic features into the diffusion process. This renders the sampling adaptive to the underlying semantic structure of the input image.
2. Pixel-wise Sampling Strategy: By assigning semantic weights to individual pixels, the model adjusts residual transfer rates and noise strengths according to pixel-level semantic information. This prioritizes semantically dense regions in the image during the diffusion process.
3. Semantic Consistency Loss: Introducing a loss function that aligns predicted and ground truth semantic characteristics enhances training by ensuring semantic fidelity.

Experimental Validation

Extensive experiments validate SAMSR's efficacy across real-world and synthetic datasets. It demonstrates substantial enhancements in perceptual quality and detail recovery, particularly in semantically complex images. Quantitative measures such as CLIPIQA and MUSIQ used in real-world dataset evaluations underscore the improvements in non-reference image quality.

Implications and Future Directions

The proposed approach significantly boosts the diffusion model's single-step inference capabilities, particularly in images where semantic complexity poses a challenge to uniform sampling methods. By bridging the gap between semantic segmentation and image super-resolution, this framework suggests promising avenues for future research in applying semantic guidance to other generative tasks within AI, such as image editing and inpainting. The SAMSR model can be further refined and scaled to address larger datasets and diverse deployments, paving the way for more computationally efficient solutions in image processing.

In conclusion, this paper contributes significantly to the field of image super-resolution by leveraging semantic segmentation to inform diffusion processes, thereby addressing efficiency concerns and improving output quality. The integration of semantic guidance in SR models not only enhances the perceptual realism of generated images but also opens up new possibilities for semantic-aware generative tasks.