ABSTRACT Historical photographs and artworks often suffer from degradation, missing regions, or stylistic corruption due to aging, scanning artefacts, or incomplete archival processes. While recent image inpainting and restoration models achieve plausible visual reconstructions, they often disregard the cultural and temporal context of the content—producing restorations that are visually coherent yet stylistically anachronistic. In this paper, we present TimeBrush , a temporally guided diffusion‐based framework for historical image restoration. By conditioning the generation process on explicit temporal prompts (e.g., art period, century), and reinforcing stylistic alignment through a learned style consistency discriminator, TimeBrush faithfully reconstructs missing content while preserving culturally significant visual traits. Our framework integrates a Temporal Prompt Encoder and a Style Consistency Discriminator, allowing restorations to be faithful both temporally and stylistically. TimeBrush improves Style Accuracy by over 7% compared with state‐of‐the‐art baselines, while also having better perceptual quality. These results indicate TimeBrush's promising opportunities for AI‐assisted cultural heritage preservation and museum digitization.
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