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<p>Single Image Defocus Deblurring (SIDD) remains challenging due to spatially varying blur kernels, particularly in processing high-resolution images where traditional methods often struggle with artifact generation, detail preservation, and computational efficiency. This paper presents Swin-Diff, a novel architecture integrating diffusion models with Transformer-based networks for robust defocus deblurring. Our approach employs a two-stage training strategy where a diffusion model generates prior information in a compact latent space, which is then hierarchically fused with intermediate features to guide the regression model. The architecture incorporates a dual-dimensional self-attention mechanism operating across channel and spatial domains, enhancing long-range modeling capabilities while maintaining linear computational complexity. Extensive experiments on three public datasets (DPDD, RealDOF, and RTF) demonstrate Swin-Diff’s superior performance, achieving average improvements of 1.37% in PSNR, 3.6% in SSIM, 2.3% in MAE, and 25.2% in LPIPS metrics compared to state-of-the-art methods. Our results validate the effectiveness of combining diffusion models with hierarchical attention mechanisms for high-quality defocus blur removal.</p>

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Swin-Diff: a single defocus image deblurring network based on diffusion model

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年2月17日

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  • Hanyan Liang,
  • Shuyao Chai,
  • Xixuan Zhao,
  • Jiangming Kan

摘要

Single Image Defocus Deblurring (SIDD) remains challenging due to spatially varying blur kernels, particularly in processing high-resolution images where traditional methods often struggle with artifact generation, detail preservation, and computational efficiency. This paper presents Swin-Diff, a novel architecture integrating diffusion models with Transformer-based networks for robust defocus deblurring. Our approach employs a two-stage training strategy where a diffusion model generates prior information in a compact latent space, which is then hierarchically fused with intermediate features to guide the regression model. The architecture incorporates a dual-dimensional self-attention mechanism operating across channel and spatial domains, enhancing long-range modeling capabilities while maintaining linear computational complexity. Extensive experiments on three public datasets (DPDD, RealDOF, and RTF) demonstrate Swin-Diff’s superior performance, achieving average improvements of 1.37% in PSNR, 3.6% in SSIM, 2.3% in MAE, and 25.2% in LPIPS metrics compared to state-of-the-art methods. Our results validate the effectiveness of combining diffusion models with hierarchical attention mechanisms for high-quality defocus blur removal.