Text-Aware Image Restoration with Diffusion Models

Abstract

While diffusion models have achieved remarkable success in natural image restoration, they often fail to faithfully recover textual regions, frequently producing plausible yet incorrect text-like patterns, a phenomenon we term text-image hallucination. To address this limitation, we propose Text-Aware Image Restoration (TAIR), a task requiring simultaneous recovery of visual content and textual fidelity. For this purpose, we introduce SA-Text, a large-scale benchmark of 100K high-quality scene images with dense annotations of diverse and complex text instances. We further present a multi-task diffusion framework, TeReDiff, which leverages internal features of diffusion models to jointly train a text-spotting module with the restoration module. This design allows intermediate text predictions from the text-spotting module to condition the diffusion-based restoration process during denoising, thereby enhancing text recovery. Extensive experiments demonstrate that our approach faithfully restores textual regions, outperforms existing diffusion-based methods, and achieves new state-of-the-art results on TextZoom, an STISR benchmark considered a subtask of TAIR. The code, weights, and dataset will be publicly released.