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SAM-DiffSR: Structure-Modulated Diffusion Model for Image Super-Resolution (2402.17133v1)

Published 27 Feb 2024 in cs.CV

Abstract: Diffusion-based super-resolution (SR) models have recently garnered significant attention due to their potent restoration capabilities. But conventional diffusion models perform noise sampling from a single distribution, constraining their ability to handle real-world scenes and complex textures across semantic regions. With the success of segment anything model (SAM), generating sufficiently fine-grained region masks can enhance the detail recovery of diffusion-based SR model. However, directly integrating SAM into SR models will result in much higher computational cost. In this paper, we propose the SAM-DiffSR model, which can utilize the fine-grained structure information from SAM in the process of sampling noise to improve the image quality without additional computational cost during inference. In the process of training, we encode structural position information into the segmentation mask from SAM. Then the encoded mask is integrated into the forward diffusion process by modulating it to the sampled noise. This adjustment allows us to independently adapt the noise mean within each corresponding segmentation area. The diffusion model is trained to estimate this modulated noise. Crucially, our proposed framework does NOT change the reverse diffusion process and does NOT require SAM at inference. Experimental results demonstrate the effectiveness of our proposed method, showcasing superior performance in suppressing artifacts, and surpassing existing diffusion-based methods by 0.74 dB at the maximum in terms of PSNR on DIV2K dataset. The code and dataset are available at https://github.com/lose4578/SAM-DiffSR.

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Authors (7)
  1. Chengcheng Wang (14 papers)
  2. Zhiwei Hao (16 papers)
  3. Yehui Tang (63 papers)
  4. Jianyuan Guo (40 papers)
  5. Yujie Yang (29 papers)
  6. Kai Han (184 papers)
  7. Yunhe Wang (145 papers)
Citations (3)
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Summary

Structure-Modulated Diffusion Model for Image Super-Resolution: A Comprehensive Analysis

The research paper titled "SAM-DiffSR: Structure-Modulated Diffusion Model for Image Super-Resolution" by Chengcheng Wang et al. presents a novel approach to enhance the capabilities of diffusion-based image super-resolution (SR) models. This paper introduces the SAM-DiffSR framework, which leverages fine-grained structural information from the Segment Anything Model (SAM) to improve image restoration without introducing additional computational cost during inference. Below, a detailed examination of the proposed methodology, results, and the implications of this work on the field of image super-resolution is provided.

Methodology

At the core of the SAM-DiffSR framework is the integration of structural modulation in the diffusion process. The segmentation masks generated by SAM are employed to introduce detailed structure-level information into the noise distribution tailored for distinct semantic regions during the forward diffusion process. The critical aspects of the proposed method include:

  1. Structural Position Encoding (SPE) Module: This module encodes structural position information into the intuitive segmentation mask generated by SAM. The resultant SPE mask modulates the noise mean in each segmentation area during the forward diffusion process.
  2. Training Strategy: The model employs these SPE-modulated noise distributions for training the diffusion model to estimate and restore high-resolution images from low-resolution counterparts. The significant advantage of this design is that it circumvents additional computational overhead during inference as it exploits pre-computed masks, making it efficient and scalable.
  3. Denoising Network: The framework uses a U-Net-based denoising network to predict noise, adjusted with the SPE mask, indicating a robust approach for modeling typical noise in image restoration tasks.

Results and Evaluation

The paper's experimental investigations highlight the marked improvements achieved by the SAM-DiffSR model on several image SR benchmarks, including DIV2K. The results demonstrate a maximum PSNR gain of 0.74 dB on DIV2K over other diffusion-based models, a robust outcome underscoring the model’s efficacy in texture and structure restoration. The SAM-DiffSR framework achieves superior performance metrics with marginal computational overhead during training, aligning with real-world applicability requirements.

The artifact suppression capabilities of SAM-DiffSR are particularly noteworthy. The ablation studies confirm the effectiveness of SAM-DiffSR in both structural detail preservation and artifact mitigation compared to existing GAN and flow-based methodologies. Quantitative evaluations using PSNR, SSIM, and FID also reinforce the framework's superior perceptual quality, manifesting as fewer artifacts and better structure preservation in the generated images.

Implications and Future Directions

The integration of SAM marks a significant advancement in incorporating fine-grained structure-level detail into the diffusion process, a previously underexplored aspect in image SR research. This paper opens avenues for further exploration into non-uniform noise generation strategies modulated by structural data, potentially enhancing other vision-related tasks.

Conceptually, this approach encourages further investigation into the amalgamation of semantic segmentation frameworks like SAM with generative models to redefine noise distribution processes in various image restoration tasks. It also suggests potential optimizations in real-time applications of SR in fields such as medical imaging and remote sensing, where computational efficiency is paramount.

Future research could look into optimizing the segmentation mask generation process to address variability in mask quality and extend the application's applicability to video super-resolution tasks. Moreover, dialogue between segmentation and denoising models offers promising research trajectories, with SAM-DiffSR serving as a capable foundation for subsequent innovations in this area.

In conclusion, the SAM-DiffSR framework offers a significant contribution by demonstrating the utility of structure-aware noise modulation and advancing the efficacy and computational viability of diffusion-based image super-resolution approaches. Its impact on both theoretical understanding and practical applications of SR is poised to inspire further developments within the domain.

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  1. GitHub - lose4578/SAM-DiffSR: SAM-DiffSR: Structure-Modulated Diffusion Model for Image Super-Resolution (122 stars)