DeGlare: Self-Supervised Specular Removal for Industrial Metallic Surfaces via Multi-Illumination Priors

In industrial machine vision, removing specular highlights is crucial for accurate surface analysis; however, existing open research remains largely confined to natural images, limiting its efficacy on highly reflective metallic surfaces. When used for zero-shot inference, these methods generate artifacts, making them unreliable for specialized industrial scenes. Since current state-of-the-art methods rely on pixel-level supervision, adapting them to novel industrial subjects is often impractical. Such adaptation requires highlight-free ground truths across hundreds of multi-view scenes, necessitating hardware-intensive cross-polarization, which is difficult to implement at scale in uncontrolled real-world settings. To address these challenges, we propose DeGlare, a flexible training framework for highlight removal in specialized industrial settings. Our approach leverages only a single-view scene by exploiting its multi-illumination observations together with a latent permutation strategy, enabling an encoder-decoder–style architecture to implicitly learn diffuse–specular decomposition. This eliminates the dependency on the burdensome manual annotation required by state-of-the-art supervised methods. We demonstrate the practical effectiveness of our approach on a real-world industrial robotic rig data set, where DeGlare achieves promising performance under limited data and annotation constraints.