Generative Single Image Reflection Separation
This addresses the problem of separating reflections from images for computer vision applications, but it is incremental as it builds on prior work with a new method for a known bottleneck.
The paper tackles the ill-posed problem of single image reflection separation by assuming known scene categories, proposing a novel network architecture that generates realistic transmitted and reflected scenes using adversarial learning and weak supervision. Experimental results show it performs favorably against existing methods on real and synthetic datasets.
Single image reflection separation is an ill-posed problem since two scenes, a transmitted scene and a reflected scene, need to be inferred from a single observation. To make the problem tractable, in this work we assume that categories of two scenes are known. It allows us to address the problem by generating both scenes that belong to the categories while their contents are constrained to match with the observed image. A novel network architecture is proposed to render realistic images of both scenes based on adversarial learning. The network can be trained in a weakly supervised manner, i.e., it learns to separate an observed image without corresponding ground truth images of transmission and reflection scenes which are difficult to collect in practice. Experimental results on real and synthetic datasets demonstrate that the proposed algorithm performs favorably against existing methods.