卷积神经网络在单帧图像超分辨率重建任务中取得了巨大成功, 但是其重建模型多是基于单链结构, 层间联系较弱且不能充分利用网络提取的分层特征。针对这些问题, 本文设计了一种多路径递归的网络结构 (MRCN)。通过使用多路径结构来加强层之间的联系, 实现特征的有效利用并且提取丰富的高频成分, 同时使用递归结构降低训练难度。此外, 通过引入特征融合的操作使得在重建的过程中可以充分利用各层提取的特征, 并且自适应的选择有效特征。在常用的基准测试集上进行了大量实验表明, MRCN比现有的方法在重建效果上具有明显提升。

Convolutional neural network (CNN) has recently achieved a great success for single image su-per-resolution (SISR). However, most deep CNN-based super-resolution models use chained stacking to build the network, which results in the fact that the relationship between layers is weak and does not make full use of hierar-chical features. In this paper, a multi-path recursive convolutional network (MRCN) is designed to address these problems in SISR. By using multi-path structure to strengthen the relationship between layers, our network can ef-fectively utilize features and extract rich high-frequency components. At the same time, we also use recursive structure to alleviate training difficulty. In addition, by introducing the operation of feature fusion into the model, our network can make full use of the features extracted from each layer in the reconstruction process and select the ef-fective features adaptively. Extensive experiments on benchmarks datasets have shown that MRCN has a significant performance improvement against existing methods.