Zero-Shot Blind Inpainting with Flow Matching

The blind inpainting (BI) problem, a challenging variant of image inpainting, aims to reconstruct missing or corrupted regions when the corruption mask is unknown. Recent deep-learning approaches have predominantly adopted data-driven paradigms, whose generalization depends on the diversity and quality of paired training data. In this paper, we revisit zero-shot BI using pretrained generative priors. The only prior zero-shot method, RGI, explicitly optimizes a sparsity-regularized mask. We show that its solution is inherently sensitive to the unknown corruption budget and can become degenerate when the generative prior interpolates the corrupted observation. To address these limitations, we propose ZSBI-FM, a mask-free framework that models corruption as a structured sparse reconstruction residual. Our nested SCAD objective combines pixel-level sparsity with SLIC superpixel groups to handle both isolated and spatially coherent corruptions, while an unsupervised stopping criterion limits overfitting. Across diverse mask geometries and fillings, ZSBI-FM improves PSNR over the strongest competing method by $2.02$–$2.49$ dB on FFHQ and $0.78$–$1.40$ dB on ImageNet, and remains robust under additional measurement noise. On simulated BTAD, it also enables accurate defect localization without defective training images or pixel-level annotations.