Benchmarking Single-Image Reflection Removal Algorithms

Abstract

Removing undesired reflections from a photo taken in front of a glass is of great importance for enhancing the efficiency of visual computing systems. Various approaches have been proposed and shown to be visually plausible on small datasets collected by their authors. A quantitative comparison of existing approaches using the same dataset has never been conducted due to the lack of suitable benchmark data with ground truth. This paper presents the first captured Single-image Reflection Removal dataset 'SIR2' with 40 controlled and 100 wild scenes, ground truth of background and reflection. For each controlled scene, we further provide ten sets of images under varying aperture settings and glass thicknesses. We perform quantitative and visual quality comparisons for four state-of-the-art singleimage reflection removal algorithms using four error metrics. Open problems for improving reflection removal algorithms are discussed at the end.