基于可嵌入式网络结构的图像超分辨率重建方法

针对卷积神经网络中的图像超分辨率重建模型训练不稳定与收敛速度较慢等问题，提出一种可嵌入式并行网络框架（EPNF），用于单幅图像超分辨率重建任务。将现有的图像超分辨率网络模型作为EPNF框架的深层结构部分嵌入到该框架中，能够以较小参数代价加快所嵌入的超分辨率模型的收敛速度，在一定程度上提高模型的准确率。在EPNF网络结构的基础上，提出一种新的超分辨率重建方法EPNF_DCSR，采用稠密跳跃连接构造高分辨率（HR）图像的高频成分，并使用单层卷积构造HR图像的低频成分，合成一幅HR输出图像。实验结果表明，与当前主流的图像超分辨率算法相比，EPNF_DCSR具有更好的图像生成效果。

Image Super-Resolution Reconstruction Method Based on Embeddable Network Structure

The existing Super-Resolution(SR)image reconstruction models based on Convolutional Neural Networks(CNN)have multiple deficiencies,such as instable model training process and low convergence speed.To address the problem,this paper proposes an Embeddable Parallel Network Framework(EPNF)for the super-resolution reconstruction of a single image.The existing network models for super-resolution image reconstruction can be embedded into EPNF as the deep structure part of the framework,improving their convergence speed and accuracy by taking a small loss of parameters.On the basis of EPNF,this paper also proposes a novel super-resolution image reconstruction method,EPNF_DCSR.The method uses the dense jump connections to restore the high-frequency details of high-resolution images,and a single-layer convolution to restore low-frequency details.Finally a high-resolution image is output.Experimental results show that EPNF_DCSR has better image reconstruction performance than the existing mainstream image reconstruction algorithms.