Removing Reflections from RAW Photos
This addresses the issue of unwanted reflections in photos for consumers, but it is incremental as it builds on prior methods with a focus on RAW data and optional context photos.
The paper tackles the problem of removing real-world reflections from RAW photos for consumer photography by using a system trained on synthetic mixtures of real RAW photos, achieving state-of-the-art results on field-captured images and showing that training on RAW simulation data improves performance more than architectural variations.
We describe a system to remove real-world reflections from images for consumer photography. Our system operates on linear (RAW) photos, and accepts an optional contextual photo looking in the opposite direction (e.g., the "selfie" camera on a mobile device). This optional photo disambiguates what should be considered the reflection. The system is trained solely on synthetic mixtures of real RAW photos, which we combine using a reflection simulation that is photometrically and geometrically accurate. Our system comprises a base model that accepts the captured photo and optional context photo as input, and runs at 256p, followed by an up-sampling model that transforms 256p images to full resolution. The system produces preview images at 1K in 4.5-6.5s on a MacBook or iPhone 14 Pro. We show SOTA results on RAW photos that were captured in the field to embody typical consumer photos, and show that training on RAW simulation data improves performance more than the architectural variations among prior works.