Automated Adaptive Approach for Specular Highlight Removal in Digital Dentistry: A Proof-of-Concept Study for...

Background/Objectives: Digital intraoral photography is widely used for clinical documentation, longitudinal monitoring, and AI-assisted dental image analysis. However, specular highlights caused by saliva and intense illumination can obscure tooth texture and compromise image fidelity. This study aimed to develop an automated method for removing specular highlights from intraoral images while preserving tooth surface texture. Methods: A three-stage pipeline consisting of adaptive threshold prediction, mask generation, and image inpainting was proposed. Initially, the Hue, Saturation, Value (HSV) statistical features were extracted from each image and used to train a regression model that predicts an image-specific threshold. Subsequently, the predicted threshold was applied in the CIE LAB color space, followed by a condition-based dynamic adjustment algorithm to refine the mask area and distribution. Finally, an Aggregated Contextual Transformation (AOT)-based generator network was used to restore the masked regions. Results: The proposed dynamic adjustment reduced over-masking compared with regression-only processing and better preserved tooth surface texture. Pixel distribution analysis demonstrated a lower distributional discrepancy, with the Wasserstein distance reduced from 2.9601 to 1.3505 and the Kullback-Leibler divergence reduced from 0.3451 to 0.0618. In the clinical expert evaluation, the proposed method was preferred in 69.5% of the 200 evaluation responses, and the preference difference was statistically significant (p < 0.001). Conclusions: As a proof-of-concept study conducted under controlled conditions using synthetic images, the proposed pipeline reduced specular highlights while better preserving tooth surface texture than the baseline approaches. These findings suggest that the pipeline may support standardized preprocessing of dental image datasets, although broader applications such as long-term monitoring and AI-assisted diagnostic workflows require validation on real clinical photographs.