论文标题
多层融合网络的多分支多路复用网络的图像重建具有混合多层注意力
Image Reconstruction of Multi Branch Feature Multiplexing Fusion Network with Mixed Multi-layer Attention
论文作者
论文摘要
图像超分辨率重建比传统方法在强大的非线性表示神经网络的能力的帮助下取得了更好的结果。但是，某些现有的算法也存在一些问题，例如对分阶段功能的利用不足，忽略了早期分阶段功能融合以提高网络性能的重要性，而网络无法更关注重建过程中的高频信息。为了解决这些问题，我们提出了一个具有混合多层注意力（MBMFN）的多分支特征多路复用融合网络，该网络实现了功能的多重利用和不同特征级别的多阶段融合。为了进一步提高网络性能，我们提出了轻巧增强的剩余渠道关注（LERCA），这不仅可以有效地避免频道信息的丢失，而且还可以使网络更加关注关键渠道信息并从中受益。最后，将注意力机制引入重建过程中，以增强边缘纹理和其他细节的恢复。在几个基准集合上进行的大量实验表明，与其他高级重建算法相比，我们的算法会产生竞争激烈的客观指标，并恢复更多图像详细信息纹理信息。
Image super-resolution reconstruction achieves better results than traditional methods with the help of the powerful nonlinear representation ability of convolution neural network. However, some existing algorithms also have some problems, such as insufficient utilization of phased features, ignoring the importance of early phased feature fusion to improve network performance, and the inability of the network to pay more attention to high-frequency information in the reconstruction process. To solve these problems, we propose a multi-branch feature multiplexing fusion network with mixed multi-layer attention (MBMFN), which realizes the multiple utilization of features and the multistage fusion of different levels of features. To further improve the networks performance, we propose a lightweight enhanced residual channel attention (LERCA), which can not only effectively avoid the loss of channel information but also make the network pay more attention to the key channel information and benefit from it. Finally, the attention mechanism is introduced into the reconstruction process to strengthen the restoration of edge texture and other details. A large number of experiments on several benchmark sets show that, compared with other advanced reconstruction algorithms, our algorithm produces highly competitive objective indicators and restores more image detail texture information.
