论文标题
轻量级空间通道自适应协调图像重建的多级改进增强网络
Lightweight Spatial-Channel Adaptive Coordination of Multilevel Refinement Enhancement Network for Image Reconstruction
论文作者
论文摘要
从深度学习的迅速发展中受益，许多基于CNN的图像超分辨率方法已经出现并取得了比传统算法更好的结果。但是，大多数算法很难同时适应空间区域和通道特征，更不用说它们之间的信息交换了。此外，注意模块之间的信息交换对于研究人员而言甚至不太明显。为了解决这些问题，我们提出了对多级改进增强网络（MREN）的轻型空间通道自适应协调。具体而言，我们构建了一个空间通道自适应协调块，该块使网络能够在不同的接受场下学习空间区域和通道特征感兴趣的信息。此外，在空间部分和通道部分之间的相应特征处理级别的信息在跳跃连接的帮助下交换，以实现两者之间的协调。我们通过简单的线性组合操作在注意模块之间建立了通信桥梁，以便更准确，连续地指导网络注意感兴趣的信息。在几个标准测试集上进行的广泛实验表明，我们的MREN在具有很少数量的参数和非常低的计算复杂性的其他高级算法中取得了优于其他高级算法的性能。
Benefiting from the vigorous development of deep learning, many CNN-based image super-resolution methods have emerged and achieved better results than traditional algorithms. However, it is difficult for most algorithms to adaptively adjust the spatial region and channel features at the same time, let alone the information exchange between them. In addition, the exchange of information between attention modules is even less visible to researchers. To solve these problems, we put forward a lightweight spatial-channel adaptive coordination of multilevel refinement enhancement networks(MREN). Specifically, we construct a space-channel adaptive coordination block, which enables the network to learn the spatial region and channel feature information of interest under different receptive fields. In addition, the information of the corresponding feature processing level between the spatial part and the channel part is exchanged with the help of jump connection to achieve the coordination between the two. We establish a communication bridge between attention modules through a simple linear combination operation, so as to more accurately and continuously guide the network to pay attention to the information of interest. Extensive experiments on several standard test sets have shown that our MREN achieves superior performance over other advanced algorithms with a very small number of parameters and very low computational complexity.
