![]() ![]() However, due to the multiple iterations of training, the model convergence training becomes difficult with the occurrence of the gradient vanishing problem. The iterative training network uses the output of the previous network as the input for the next training to improve the quality of the predicted transmission layer images through continuous iterations. The encoder with a residual attention mechanism can focus on the background information to be recovered and avoid problems such as color distortion. Our network has a simpler structure and can be trained more efficiently than a cascaded network that requires two encoder-decoders. In this paper, RABRRN consists of an encoder with a residual attention mechanism to extract semantic features and two decoders as two branches to separately predict the transmission and reflection layers. In addition, thanks to the introduction of the residual network and ConvLSTM, this module partially deepens the network depth while accelerating the convergence rate, without causing network degradation. Both channel attention and spatial attention can be used to improve the performance of the neural network by allowing it to focus on the most important parts of the input data. To better focus on the inhomogeneity of the spatial intensity distribution, we integrated a residual attention module into the encoder used in our method to combine channel attention with spatial attention. Moreover, the reflection layer can be viewed as a translucent soft mask over the transmission layer, and the spatial intensity of both layers is inhomogeneous, depending on the camera angle and light intensity. However, as for the reflection removal task, it is not sufficient to rely only on channel concerns. Therefore, it can be assumed that the transmission and reflection layers correspond to specific channels, which implies the need to enhance the model for channel feature representation. We consider reflection removal as a typical image separation problem. In addition, these a priori designs have high requirements and limited application in reflection removal. These methods usually require a lot of manual intervention to tune the model parameters and may not be able to handle complex reflection scenarios. As can be seen above, the physical model-based approach consists of developing a mathematical model for reflection removal and using the model to estimate the reflection parameters. However, if the camera is placed far from the subject which cannot be reached by the flashlight, the flash-only image obtained at this time may be a black image. ![]() The authors of proposed a simple yet effective reflection-free cue to remove reflection with robustness from a pair of flash and ambient (no-flash) images. For example, introduced the use of ghosting cues that exploit the asymmetry between layers, thus helping to reduce the discomfort of eliminating reflections in images taken through thick glass. That is, the reflection layer is considered to be smoother relative to the transmission layer, so a smooth gradient is applied to the objective function of the reflection layer, and a sparse gradient is applied to the objective function of the transmission layer. As for the ill-posed problem, proposed the concept of relative smoothness for reflection image separation. To solve this problem, it is imperative for researchers to impose constraints and artificial priors on the solution space, thus a separation of the reflection image closer to an ideal target solution can be obtained. Compared with the other most advanced methods, our method has only 18.524M parameters, but it obtains the best results from test datasets. The experimental results show that the proposed method achieves a PSNR of 23.787 dB and an SSIM value of 0.885 from four benchmark datasets. In addition, we establish a reflection image dataset named the SCAU Reflection Image Dataset (SCAU-RID), providing sufficient real training data. For a more feasible solution to solve the problem of gradient disappearance in the iterative training of deep neural networks, the attention module is combined with a residual network to design a residual attention module so that the performance of reflection removal can be ameliorated. Therefore, we integrate spatial attention and channel attention into the model to enhance spatial and channel feature representation. We hold that reflection removal is essentially an image separation problem sensitive to both spatial and channel features. In this paper, we propose a Residual Attention Based Reflection Removal Network (RABRRN) to tackle the issue of single image reflection removal. Affected by shooting angle and light intensity, shooting through transparent media may cause light reflections in an image and influence picture quality, which has a negative effect on the research of computer vision tasks. ![]()
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