密集残差网络红外图像超分辨率重构

单幅红外图像超分辨率重构算法作为红外图像分辨率提升应用的关键技术,近年来得到了广泛的研究。为了提高红外图像的分辨力,提出了一种基于残差密集对抗式生成网络的单幅红外图像分辨力提升方法。与以往基于对抗式生成网络的分辨力提升方法不同,本文方法的新颖性主要包含两个方面。首先,在网络架构方面进行改进,以提高性能。设计密集残差网络作为对抗式生成网络的生成网络,充分利用了低分辨率图像的有效特征。在生成网络中引入了一种连续内存机制,以利用密集的剩余块。其次,将Wasserstein-GAN作为损失函数,对判别网络模型进行修正,以达到稳定训练的目的。利用红外高分辨率图像数据集进行了大量的实验,结果表明,该方法在客观评价和主观评价方面均优于目前最新的方法。

Super resolution reconstruction method with dense residual network for infrared image

As the key technology of infrared image resolution enhancement,single image super-resolution reconstruction algorithm has been widely studied in recent years.In order to improve the resolution of infrared image super-resolution reconstruction,a method of single infrared image super-resolution reconstruction based on dense residual generation network is proposed.Different from the previous super-resolution methods based on the generative countermeasure network,the novelty of this method mainly includes two aspects.Firstly,improve the network architecture to improve performance.The dense residual network is designed as the generation network of the generative countermeasure network,which makes full use of the hierarchical characteristics of the low resolution image.A continuous memory mechanism is introduced in the generation network to make use of the dense remaining blocks.Secondly,Wasserstein GAN is used as the loss function to modify the discriminant network model to achieve the purpose of stable training.A large number of experiments have been carried out by using infrared high resolution image data set.The results show that the method is superior to the latest method in both objective and subjective evaluation.