Unsupervised single-image reflection removal

Reflections often degrade the quality of images by obstructing the background scenes. This is not desirable for everyday users, and it negatively impacts the performance of multimedia applications that process images with reflections. Most current methods for removing reflections utilize supervised learning models. These models require a vast number of image pairs of the same scenes with and without reflections to perform well. However, collecting such image pairs is challenging and costly. Thus, most current supervised models are trained on small datasets that cannot cover the numerous possibilities of real-life images with reflections. In this thesis, we propose an unsupervised method for single-image reflection removal. Instead of learning from a large dataset, we optimize the parameters of two cross-coupled deep convolutional networks on a target image to generate two exclusive background and reflection layers. In particular, we design a network model that embeds semantic features extracted from the input image and utilizes these features in the separation of the background layer from the reflection layer. We show through objective and subjective studies on benchmark datasets that the proposed method substantially outperforms current methods in the literature. The proposed method does not require large datasets for training, removes reflections from single individual images, and does not impose constraints or assumptions on the input images.