Image reflection removal using end‐to‐end convolutional neural network
Jinjiang Li, Guihui Li, Hui Fan
Abstract
Single image reflection removal is an ill‐posed problem. To solve this problem, this study develops a network structure based on a deep encoder–decoder RRnet. Unlike most deep learning strategies applied in this context, the authors find that redundant information increases the difficulty of predicting images on the network; thus, the proposed method uses mixed reflection image cascaded edges as input to the network. The proposed network structure is divided into two parts: the first part is a deep convolutional encoder–decoder network. Its function uses the mixed reflection image and the target edge as input to predict the target layer. The second part is an identical encoder–decoder network structure. Its function uses the mixed reflection image and the reflection edge as input to predict the image reflection layer. In addition, the authors use joint loss to optimise the network model. To train the neural network, they also create an image dataset for reflection removal, which includes a true mixed reflection image and a synthetic mixed reflection image. They use four evaluation indicators to evaluate the proposed method and the other six methods. The experimental results indicate that the proposed method is superior to previous methods.