Exploiting Reflection Change for Automatic Reflection Removal

Yu Li, Michael S. Brown; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2013, pp. 2432-2439

This paper introduces an automatic method for removing reflection interference when imaging a scene behind a glass surface. Our approach exploits the subtle changes in the reflection with respect to the background in a small set of images taken at slightly different view points. Key to this idea is the use of SIFT-flow to align the images such that a pixel-wise comparison can be made across the input set. Gradients with variation across the image set are assumed to belong to the reflected scenes while constant gradients are assumed to belong to the desired background scene. By correctly labelling gradients belonging to reflection or background, the background scene can be separated from the reflection interference. Unlike previous approaches that exploit motion, our approach does not make any assumptions regarding the background or reflected scenes' geometry, nor requires the reflection to be static. This makes our approach practical for use in casual imaging scenarios. Our approach is straight forward and produces good results compared with existing methods.