![]() (ii) Obtain the edge information of the original mixed reflection image and use the edge separation technique to separate the original mixed reflection edge information into the target edge and the reflection edge. (i) Use HDRnet techniques to obtain aligned sets of images with different reflections. In summary, our approach is summarised in three steps. The role of the second network is to predict the reflected image by utilising the cascaded reflection edge with the mixed reflection image. The role of the first network is to predict the target image by cascading the target edge with a reflected image. Specifically, our network consists of two identical encoder–decoder network frameworks. At the same time, to achieve efficient reflection removal, we add a residual block network in the middle of the encoder–decoder network. The encoder and edge information are used to extract more accurate semantic features, and the decoder generates the relevant layers through deconvolution. In this paper, we propose a depth encoder and decoder network based on the edge cascade. Therefore, we use edge information to guide reflection separation. The reflection layer is more sparsely distributed than the target layer. In addition, we know from the nature of the mixed reflection image that the gradient of the target layer and the reflection layer are different, and the edge distribution is also different. A deep learning method with appropriately selected training data can avoid forming a model using the form of an equation. With the rapid development of deep learning, the problem of image reflection elimination has been better solved. It is important that we use reflection and target scene to infer results. However, the reflection content is related to the restoration of the target scene. (iii) The traditional method mainly focuses on the restoration of the target scene. (ii) The traditional method often solves the reflection elimination problem by solving the ‘reflection elimination equation’, which finally turns the problem into a linear combination problem of the target image and the reflection image so that the effect is greatly discounted. Moreover, compared with the high-level semantic information, the a priori knowledge only describes a limited range of reflection characteristics, so using the traditional method is limited and does not have universality. Since a priori knowledge is mostly based on low-level pixel information, this information is greatly affected by environmental changes and textural differences. (i) The traditional method is mostly based on manual characteristics and relies on a priori assumptions. Obviously, the traditional method has two main drawbacks. The traditional method mainly uses the solution of ( 1). To solve this reflection elimination problem, researchers have proposed many solutions. Since the number of unknowns in the formula is twice the number of equations, the single image reflection removal problem is extremely ill-posed. ![]() It can be seen from the formula that the essence of the reflection removal problem is that two images, B and R, are decomposed from a mixed image I. (1)where B represents the target image, R represents the reflected image, and I represents an image with a reflection. The experimental results indicate that the proposed method is superior to previous methods. They use four evaluation indicators to evaluate the proposed method and the other six methods. 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. In addition, the authors use joint loss to optimise the network model. Its function uses the mixed reflection image and the reflection edge as input to predict the image reflection layer. The second part is an identical encoder–decoder network structure. Its function uses the mixed reflection image and the target edge as input to predict the target layer. The proposed network structure is divided into two parts: the first part is a deep convolutional encoder–decoder network. ![]() 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. To solve this problem, this study develops a network structure based on a deep encoder–decoder RRnet. Single image reflection removal is an ill-posed problem.
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