Image Inpainting via Stochastic Dynamics

• URL: http://arxiv.org/abs/2607.24140v1
• Date: Mon, 27 Jul 2026 08:20:23 GMT
• Title: Image Inpainting via Stochastic Dynamics
• Abstract summary: We propose a non-parametric method without network training based on data-guided dynamics.<n>We show that the proposed method outperforms Mean Fill, Telea, and Navier-Stokes inpainting in PSNR, SSIM, and visual quality.<n>These results demonstrate the effectiveness of empirical reference statistics as a non-parametric prior for image inpainting.
• Score: 3.1988753364712115
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image inpainting aims to recover missing regions while preserving structural consistency. We propose a non-parametric method without network training based on data-guided stochastic dynamics. Starting from a masked image, the missing pixels are evolved through a reverse-time stochastic differential equation with a kernel-weighted correction estimated directly from a reference dataset. This empirical correction guides the reconstruction toward high-density regions of the data distribution without training a neural network or fitting a parametric density model. Experiments on MNIST, Fashion-MNIST, and MVTec show that the proposed method outperforms Mean Fill, Telea, and Navier-Stokes inpainting in PSNR, SSIM, and visual quality. On CelebA, it remains competitive and produces plausible completions for structure-sensitive occlusions. These results demonstrate the effectiveness of empirical reference statistics as a non-parametric prior for image inpainting.

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