基于特征融合注意网络的图像超分辨率重建

近年来, 基于深度卷积神经网络的单图像超分辨率重建, 取得了显著的进展, 但是, 仍然存在诸如特征利用率低、网络参数量大和重建图像细节纹理模糊等问题. 我们提出了基于特征融合注意网络的单图像超分辨率方法, 网络模型主要包括特征融合子网络和特征注意子网络. 特征融合子网络可以更好地融合不同深度的特征信息, 以及增加跨通道的学习能力; 特征注意子网络则着重关注高频信息, 以增强边缘和纹理. 实验结果表明: 无论是主观视觉效果, 还是客观度量, 我们方法的超分辨率性能明显优于其他代表性的方法.

Feature Fusion Attention Network for Image Super-resolution

In recent years, single-image super-resolution (SISR) reconstruction based on deep convolutional neural networks has made significant progress, but there are still problems such as low feature utilization, large number of network parameters and blurred texture of the reconstructed image. We propose a new SISR network based on feature fusion attention mechanism, which mainly consists of a feature fusion sub-network and a feature attention sub-network. The feature fusion sub-network can better fuse feature information of different depths and increase the cross-channel learning ability; the feature attention sub-network focuses on high frequency information to enhance edges and textures. Experimental results demonstrate that the super-resolution performance of our method is significantly better than those of other representative methods in both subjective vision quality and objective metrics.