Parallel Attribute Computation For Distributed Component Forests

Reflections in images are typically caused due to presence of glass like reflective objects or surfaces that affect the overall visual appeal and hence undesirable. There has been extensive interest in the past to use data driven approaches for both single image as well as multi image reflection removal. However, recently there has been only minor incremental improvements in single image reflection removal given the challenging ill-posed nature of the problem. Reference based methods has yielded state of the art performance in areas such as super resolution, however has been unexplored for reflection removal. in this paper, we propose a novel multi-stage deep learning based method for reference based reflection removal. We also propose a novel visual attribute cue that represents the reflection free semantic content of the input image. This cue is generated using the reference image while maintaining the geometric structure of the input image. We formulate the reference based reflection removal problem as extraction of visual attribute cues followed by a guided image restoration. We perform qualitative and quantitative evaluation to demonstrate the superiority of the proposed approach over the existing state of the art single image reflection removal methods.