Structured Discrete Graph Generation Model for Fragmented Image Recovery

Abstract

Fragmented image recovery is of significant importance in computer vision, such as cultural relic and artwork restoration, archival document recovery, and digital forensics. The goal is to recover the original image topology from an unordered set of fragments and spatially align and stitch them together. The adjacency relationships among fragments are discrete, sparse, and highly structured, making it difficult for traditional methods to effectively handle global topological consistency. To address this challenge, we propose a fragment adjacency recovery method based on a conditional graph diffusion model. First, we perform discrete denoising pretraining with structural masking to learn structure-aware node representations from perturbed adjacency matrices, using graph neural networks for message passing. Building on this, we design a masked discrete diffusion process tailored for fragment reconstruction, which progressively restores the connectivity between fragments. Furthermore, to enhance the controllability of the generation process, we introduce a topology-guided mechanism that steers the generation of adjacency structures via a topological scoring function, ensuring that the reconstructed fragment graph satisfies global topological constraints. Experimental results demonstrate that our method achieves state-of-the-art performance on hand-torn calligraphy, painting replica datasets and document datasets, outperforming existing approaches in both accuracy and robustness.