Single Image Reflection Removal Using Deep Encoder-Decoder Network
This addresses a challenging ill-posed problem in computer vision for applications like photography or surveillance, but it is incremental as it builds on existing deep learning approaches.
The paper tackles the problem of removing reflections from images captured through glass by proposing a deep encoder-decoder network trained on synthetic data, and it significantly outperforms state-of-the-art methods on real-world images.
Image of a scene captured through a piece of transparent and reflective material, such as glass, is often spoiled by a superimposed layer of reflection image. While separating the reflection from a familiar object in an image is mentally not difficult for humans, it is a challenging, ill-posed problem in computer vision. In this paper, we propose a novel deep convolutional encoder-decoder method to remove the objectionable reflection by learning a map between image pairs with and without reflection. For training the neural network, we model the physical formation of reflections in images and synthesize a large number of photo-realistic reflection-tainted images from reflection-free images collected online. Extensive experimental results show that, although the neural network learns only from synthetic data, the proposed method is effective on real-world images, and it significantly outperforms the other tested state-of-the-art techniques.