Eyeglass Reflection Removal with Joint Learning of Reflection Elimination and Content Inpainting

Eyeglass reflection removal is of great importance to the portrait image processing. However, it remains a challenge to eliminate the reflections on the glass and restore the textual contents of eyes without introducing visual artifacts. Addressing this problem, in this paper, we propose an Eyeglass Reflection Removal Network (ER
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Net) by learning reflection elimination and content inpainting jointly. The reflection elimination branch is effective in weak reflection regions, and the content inpainting branch is dedicated to content reasoning in strong reflection regions. We then propose a result fusion module (RFM), which adaptively fuses the elimination result and the inpainting result according to the reflection intensity of each pixel, to produce high-quality result. We also design a memory module for improving the content inpainting result, and propose an eye-symmetry loss to avoid visual artifacts. Additionally, we construct the first Real-world eyeglass Reflection (ReyeR) dataset for eyeglass reflection removal. Extensive quantitative and qualitative experiments demonstrate the superiority of the ER
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Net over state-of-the-art methods for eyeglass reflection removal.