混合阶通道注意力网络的单图像超分辨率重建

目前用于图像超分辨率重建的通道注意力机制存在注意力预测破坏每个通道和其权重的直接对应关系以及仅仅只考虑一阶或二阶通道注意力而没有综合考虑优势互补的问题，因此提出一种混合阶通道注意力网络的单图像超分辨率重建算法。首先，该网络框架利用局部跨通道相互作用策略将之前一、二阶通道注意力模型采用的升降维改为核为k的一维卷积。这样不仅使得通道注意力预测更直接准确，而且得到的模型相比之前的通道注意力模型更简单；同时，采用改进一、二阶通道注意力模型以综合利用不同阶通道注意力的优势，提高网络判别能力。在基准数据集上的实验结果表明，和现有的超分辨率算法相比，所提算法重建图像的纹理细节和高频信息能得到更好的恢复，且在Set5和BSD100数据集上感知指数（PI）分别平均提高0.3和0.1。这表明此网络能更准确地预测通道注意力并综合利用了不同阶通道注意力，一定程度上提升了性能。

Mixed-order channel attention network for single image super-resolution reconstruction

For the current channel attention mechanism used for super-resolution reconstruction, there are problems that the attention prediction destroys the direct corresponding relationship between each channel and its weight and the mechanism only considers the first-order or second-order channel attention without comprehensive consideration of the advantage complementation. Therefore, a mixed-order channel attention network for image super-resolution reconstruction was proposed. First of all, by using the local cross-channel interaction strategy, increase and reduction in channel dimension used by the first-order and second-order channel attention models were changed into a fast one-dimensional convolution with kernel k, which not only makes the channel attention prediction more direct and accurate but makes the resulting model simpler than before. Besides, the improved first and second-order channel attention models above were adopted to comprehensively take the advantages of channel attentions of different orders, thus improving network discrimination. Experimental results on the benchmark datasets show that compared with the existing super-resolution algorithms, the proposed method has the best recovered texture details and high frequency information of the reconstructed images and the Perceptual Indictor (PI) on Set5 and BSD100 datasets are increased by 0.3 and 0.1 on average respectively. It shows that this network is more accurate in predicting channel attention and comprehensively uses channel attentions of different orders, so as to improve the performance.