DL2G: Degradation-Guided Local-to-Global Restoration for Eyeglass Reflection Removal

Abstract

Eyeglass reflection removal can restore the texture information in the reflection destructed eye area, which is meaningful for various tasks on the facial images. It is still challenging to correctly eliminate reflections, reasonably restore the lost contents, and guarantee that the final result has a consistent color and illumination with the input image. In this paper, we introduce a Degradation-guided Local-to-Global (DL2G) restoration framework to address this problem. We first propose a multiplicative reflection degradation model, which is used to alleviate reflection degradation to obtain a preliminary result. Then, in the local details restoration stage, we propose a local structure-aware diffusion model to learn the true distribution of texture details in the eye area. This helps in recovering lost contents in the regions of heavy degradation where the background is invisible. Finally, in the global consistency refinement stage, we utilize the input image as a reference image to generate the final result that is consistent with the input image in color and illumination. Extensive experiments demonstrate that our method can improve the effect of reflection removal and generate results with more reasonable semantics, exquisite details, and harmonious illumination.