Abstract:
Aiming at the problem that the existing rain removal network cannot better preserve the texture details of the image edge, an image rain removal network based on the fusion residual and attention mechanism is proposed. First, shallow learning is performed on the original image, from which key shallow features are extracted. Then, in order to avoid the loss of shallow information, the multi-attention module is fused with the residual structure to form an attention residual block, and the extracted shallow feature information is input into it for higher semantic feature learning, in which the multi-attention module is used to obtain different shapes and sizes. The multi-scale characteristics of the striation construct the dependence between different channels and make the network pay more attention to the image information characteristics of rain streaks and high-frequency regions. Finally, the feature reconstruction is performed through the convolutional layer to obtain a clear image of the removal of rain patterns. The experimental results show that the algorithm achieves a peak signal-to-noise ratio of 28.91, 36.86 dB and 35.14 dB, respectively on Rain100H, Rain100L and Rain12 open test sets, and a structural similarity of 95.0%, 99.0% and 97.1%. Experiments show that the objective evaluation indexes of the proposed algorithm are better than other comparison algorithms, and the subjective visual effect can be effectively improved, which can remove different density rain patterns while better preserving the details of the image.s of the proposed algorithm are better than other comparison algorithms, and the subjective visual effect can be effectively improved, which can remove different density rain patterns while better preserving the details of the image.