密集连接的生成对抗网络图像超分辨率重建

Super-Resolution Reconstruction of Densely Connected Generative Adversarial Network Images

针对图像超分辨率重建过程中出现的边缘细节模糊、图像特征丢失的问题,提出基于密集连接的生成对抗网络图像超分辨率重建算法。该算法由生成网络和判别网络组成,在生成网络结构中,将原始低分辨率图像作为网络的输入,为了实现对特征的充分利用,采用密集连接方式将浅层网络的特征输送到深层网络结构的每一层,有效避免图像特征的丢失。并在末端进行亚像素卷积,对图像进行反卷积操作,完成图像最终的超分辨率重建,大大减少了训练耗时。在判别网络结构中,采用6个卷积模块和一个全连接层对真伪图像进行甄别,采用对抗博弈的思想,提升重建图像的质量。实验结果表明,本文算法在视觉效果评估、峰值信噪比值、结构相似性值以及耗时等多方面指标上都有了很大的改善,恢复出较为丰富的图像细节信息,取得了较好的视觉效果和综合特性。

Aiming at the problems of blurred edge details and loss of image features in the process of image superresolution reconstruction,a super-resolution reconstruction algorithm based on dense connection generative adversarial network is proposed.This algorithm consists of a generative network and a discriminative network.In the generative network structure,the original low-resolution image is used as the input of the network.In order to make full use of the features,the features of the shallow network are transferred to each layer of the deep network structure using dense connection,so as to effectively avoid the loss of image features.Sub-pixel convolution is performed at the end,and the image is deconvolved to complete the final super-resolution reconstruction of the image,which greatly reduces the training time.In the discriminative network structure,6 convolutional modules and a fully connected layer are used to identify true and false images,and the idea of adversarial games is used to improve the quality of reconstructed images.Experimental results show that the proposed algorithm has greatly improved the visual effect assessment,peak signal to noise ratio value,structural similarity value,time-consuming,and indicators.It has restored richer image detail information and achieved better visual effects and comprehensive characteristic.