用分层重绘方法提升医学图像去噪，保留细节同时平滑背景。
Denoising via Repainting: an image denoising method using layer wise medical image repainting
- 从粗到细分层处理，用贝塞尔路径重绘图像块
- 在多个MRI数据集上PSNR和SSIM均优于现有方法
- 适合需要高保真度的医学图像处理场景
医学图像去噪对提升临床诊断可靠性及后续图像任务至关重要。本文提出一种多尺度方法，结合各向异性高斯滤波与渐进式贝塞尔路径重绘。通过构建尺度空间金字塔，在抑制噪声的同时保留关键结构细节。从最粗糙尺度开始，将部分去噪图像分割为连贯区域，并用具有代表颜色的参数化贝塞尔路径重绘。在更细尺度上迭代优化，小而复杂的结构得以准确重建，大范围均匀区域则保持稳定平滑。采用均方误差与自交约束确保路径优化过程中的形状一致性。在多个MRI数据集上的实验表明，该方法在PSNR和SSIM指标上持续优于对比方法。此粗到细框架提供了一种鲁棒、数据高效且适用于跨域去噪的解决方案，展现出良好的临床应用潜力与通用性。未来工作将拓展至三维数据。
原文摘要 · Abstract (English)
Medical image denoising is essential for improving the reliability of clinical diagnosis and guiding subsequent image-based tasks. In this paper, we propose a multi-scale approach that integrates anisotropic Gaussian filtering with progressive Bezier-path redrawing. Our method constructs a scale-space pyramid to mitigate noise while preserving critical structural details. Starting at the coarsest scale, we segment partially denoised images into coherent components and redraw each using a parametric Bezier path with representative color. Through iterative refinements at finer scales, small and intricate structures are accurately reconstructed, while large homogeneous regions remain robustly smoothed. We employ both mean square error and self-intersection constraints to maintain shape coherence during path optimization. Empirical results on multiple MRI datasets demonstrate consistent improvements in PSNR and SSIM over competing methods. This coarse-to-fine framework offers a robust, data-efficient solution for cross-domain denoising, reinforcing its potential clinical utility and versatility. Future work extends this technique to three-dimensional data.
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