基于多尺度双阶段网络的图像超分辨率重建

Image super-resolution reconstruction based on multi-scale two-stage network

针对目前图像超分辨率重建算法中所存在的特征信息提取不充分、重建图像细节信息模糊等问题,提出了一种多尺度双阶段网络来实现图像的超分辨率重建。首先,考虑到单尺度卷积层会出现特征信息提取不充分的现象,故而以多尺度卷积层为大体框架,设计网络模型;其次,考虑到重建后的图像效果,将整体网络分为2个阶段,第1阶段根据输入的低分辨率图像进行特征信息的提取和重建,第2阶段对重建后的图像进行更深一步的特征细化,从而提高重建图像的视觉效果;整体网络中还引入了跳跃连接和注意力模块,以加强特征信息的有效传播;最后,以数据集Set5、Set14、Urban100、BSDS100和Manga109作为测试集展开实验,峰值信噪比和结构相似度作为图像质量的评价指标。实验结果表明,二者的值相比以往均有所提高,且重建图像视觉效果较好。因此,该算法在客观评价和主观视觉上都取得了较好的结果。

Aiming at the problems of the insufficient feature information extraction and the blurring of the reconstructed image details in current image super-resolution reconstruction algorithm,a multi-scale two-stage network was proposed to realize image super-resolution reconstruction.First of all,considering the phenomenon of insufficient feature information extraction in single-scale convolution layer,a network model was designed based on the general framework of multi-scale convolution layer.Secondly,considering the effect of the reconstructed image,the whole network was divided into two stages:the first stage was to extract and reconstruct the feature information according to the input low-resolution image,and the second stage was to further refine the features of the reconstructed image,so as to improve the visual effect of the reconstructed image.Jump connection and attention module were also introduced in the overall network to enhance the effective transmission of feature information.Finally,the data sets Set5,Set14,Urban100,BSDS100 and Manga109 were used as the test sets of the experiment,and the peak signal-to-noise ratio and the structural similarity were used as the evaluation indicators of image quality.The experiment shows that the values of both are improved and the visual effect of reconstructed image is good.Therefore,the algorithm has achieved good results in both objective evaluation and subjective vision.