Single Image Reflection Removal Using Convolutional Neural Networks

When people take a picture through glass, the scene behind the glass is often interfered by specular reflection. Due to relatively easy implementation, most studies have tried to recover the transmitted scene from multiple images rather than single image. However, the use of multiple images is not practical for common users in real situations due to the critical shooting conditions. In this paper, we propose single-image reflection removal using convolutional neural networks. We provide a ghosting model that causes reflection effects in captured images. First, we synthesize multiple-reflection images from the input single one based on ghosting model and relative intensity. Then, we construct an end-to-end network that consists of encoder and decoder. To optimize the network parameters, we use a joint training strategy to learn the layer separation knowledge from the synthesized reflection images. For the loss function, we utilize both internal and external losses in optimization. Finally, we apply the proposed network to single-image reflection removal. Compared with the previous work, the proposed method does not need handcrafted features and specular filters for reflection removal. Experimental results show that the proposed method successfully removes reflection from both synthetic and real images as well as achieves the highest scores in peak signal-to-noise ratio, structural similarity, and feature similarity.