Image Reflection Removal Using the Wasserstein Generative Adversarial Network

Imaging through a semi-transparent material such as glass often suffers from the reflection problem, which degrades the image quality. Reflection removal is a challenging task since it is severely ill-posed. Traditional methods, while all require long computation time on minimizing different objective functions with huge matrices, do not necessarily give satisfactory performance. In this paper, we propose a novel deep-learning based method to allow fast removal of reflection. Similar to the traditional multiple-image approaches, the proposed algorithm first captures the multi-view images of a scene. Then the images are fed to a convolutional neural network to obtain the depth information along the edges of the image. It is sent to a Wasserstein generative adversarial networks (WGAN) for estimating the edges of the background. Finally, the background edges are used in another WGAN to reconstruct the background image. Experimental results show that the proposed method can achieve state-of-the-art performance, and is significantly faster than the traditional methods due to the use of the deep learning methods.