多尺度残差聚合特征网络图像超分辨重建

Image Super-Resolution Reconstruction Based on Multi-Scale Residual Aggregation Feature Network

针对现有基于深度卷积神经网络模型的图像超分辨重建技术存在图像特征提取尺度单一和中间层次特征利用不充分等问题,提出了一种多尺度残差聚合特征网络模型。首先,该模型利用不同扩展系数的扩展卷积和残差连接设计了一种混合扩展卷积残差块(HERB),有效地提取到图像多个尺度的特征信息;其次,引入了一种特征聚合机制(AM),解决了网络中间层次特征利用不充分的问题。在常用的5种数据集上进行的实验结果表明,所提网络模型在主观视觉效果和客观评价指标上都比其他模型具有更好的性能。

Aiming at the problems of single image feature extraction scale and insufficient utilization of middle level features in the existing image super-resolution reconstruction technology based on depth convolution neural network model, a multi-scale residual aggregation feature network model for image super-resolution reconstruction is proposed. First, the proposed network model uses expanded convolutions with different expanded coefficients and residual connection to construct a hybrid expanded convolution residual block(HERB), which can effectively extract multi-scale feature information of an image. Second, a feature aggregation mechanism(AM) is used to solve the problem of insufficient utilization of features among middle levels of the network. Experiments results on five commonly used data sets show that the proposed network model has better performance than other models in subjective visual effect and objective evaluation index.