基于级联深度卷积神经网络的高性能图像超分辨率重构

High-performance image super-resolution restruction based on cascade deep convolutional network

为了进一步提高现有图像超分辨率重构方法所得图像的分辨率,提出一种高性能的深度卷积神经网络(HDCN)模型用于重构放大倍数固定的超分辨率图像。通过建立级联HDCN模型解决传统模型重构图像时放大倍数无法按需选择的问题,并在级联过程中引入深度边缘滤波器以减少级联误差,突出边缘信息,从而得到高性能的级联深度卷积神经网络(HCDCN)模型。基于Set5、Set14数据集进行超分辨率图像重构实验,证明了引入深度边缘滤波器的有效性,对比HCDCN方法与其他图像超分辨率重构方法的性能评估结果,展现了HCDCN方法的优越性能。

In order to further improve the resolution of existing image super-resolution methods, a High-performance Deep Convolution neural Network （HDCN） was proposed to reconstruct a fixed-scale super-resolution image. By cascading several HDCN models, the problem that many traditional models could not upscale images in alternative scale factors was solved, and a deep edge filter in the cascade process was introduced to reduce cascading errors, and highlight edge information, High- performance Cascade Deep Convolutional neural Network （HCDCN） was got. The super-resolution image reconstruction experiment was carried out on high-performance cascade deep convolution neural network （HCDCN） model on Set5 and Setl4 datasets. The experimental results prove the effectiveness of introducing the deep edge-aware filter. By comparing the performance evaluation results of HCDCN method and other image super-resolution reconstruction method, the superior performance of HCDCN method is demonstrated.