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.

中文翻译：

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使用端到端卷积神经网络去除图像反射

单幅图像反射去除是不适的问题。为了解决这个问题，本研究开发了一种基于深度编解码器RRnet的网络结构。与这种情况下应用的大多数深度学习策略不同，作者发现冗余信息增加了在网络上预测图像的难度。因此，所提出的方法使用混合反射图像级联边缘作为网络的输入。提议的网络结构分为两部分：第一部分是深度卷积编码器-解码器网络。其功能使用混合反射图像和目标边缘作为输入来预测目标图层。第二部分是相同的编码器-解码器网络结构。它的功能使用混合反射图像和反射边缘作为输入来预测图像反射层。此外，作者使用联合损失来优化网络模型。为了训练神经网络，他们还创建了一个用于反射消除的图像数据集，其中包括一个真实的混合反射图像和一个合成的混合反射图像。他们使用四个评估指标来评估所提出的方法和其他六个方法。实验结果表明，该方法优于以前的方法。