摘要
目的基于生成式对抗网络的超分辨模型(SRGAN)以感知损失函数作为优化目标,有效解决了传统基于均方误差(MSE)的损失函数导致重建图像模糊的问题。但是SRGAN的感知损失函数中并未添加明确指示模型生成对应特征的标志性信息,使得其无法精准地将数据的具体维度与语义特征对应起来,受此局限性影响,模型对于生成图像的特征信息表示不足,导致重建结果特征不明显,给后续识别处理过程带来困难。针对上述问题,在SRGAN方法的基础上,提出一种类别信息生成式对抗网络的超分辨模型(class-info SRGAN)。方法对SRGAN模型增设类别分类器,并将类别损失项添加至生成网络损失中,再利用反向传播训练更新网络参数权重,以达到为模型提供特征类别信息的目的,最终生成具有可识别特征的重建图像。创新及优势在于将特征类别信息引入损失函数,改进了超分辨模型的优化目标,使得重建结果的特征表示更加突出。结果经Celeb A数据集测试表明:添加性别分类器的class-info SRGAN的生成图像性别特征识别率整体偏高(58%97%);添加眼镜分类器的class-info SRGAN的生成图像眼镜框架更加清晰。此外,模型在Fashion-mnist与Cifar-10数据集上的结果同样表明其相较于SRGAN的重建质量更佳。结论实验结果验证了本方法在超分辨重建任务中的优势和有效性,同时结果显示:虽然class-info SRGAN更适用于具有简单、具体属性特征的图像,但总体而言仍是一种效果显著的超分辨模型。
Objective The use of image super-resolution reconstruction technology implies the utilization of a set of low-quality low-resolution images( or motion sequences) to produce the corresponding high-quality and high-resolution ones. This technology has a wide range of applications in many fields,such as military,medicine,public safety,and computer vision.In the field of computer vision,image super-resolution reconstruction enables the image to transform from the detection level to the recognition level,and even advance to the identification level. In other words,image super-resolution reconstruction can enhance image recognition capability and identification accuracy. In addition,image super-resolution reconstruction involves a dedicated analysis of a target. In this analytic scheme,a comparatively high spatial resolution image of the regionof interest is obtained instead of directly calculating the configuration of a high spatial resolution image by using large amounts of data. The conventional approaches of super-resolution reconstruction generally include example-based model,bi-cubic interpolation model,and sparse coding methods,among others. Deep learning has been considered for many associative subjects since the advent of artificial intelligence in recent years,and substantial research achievements have been realized in this field alongside the research on super-resolution reconstruction. Convolutional neural networks( CNNs) and generative adversarial networks( GANs) have resulted in numerous breakthroughs and achievements in the domain of image super-resolution reconstruction. Examples include super-resolution reconstruction with CNN( SRCNN),super-resolution reconstruction with very-deep convolutional networks( VDSR),and super-resolution reconstruction with generative adversarial network( SRGAN). Particularly in SRGAN modeling,the single-image super-resolution technology has achieved remarkable progress,especially when the perceptual loss function instead of the traditional loss function based on the mean squ
作者
杨云
张海宇
朱宇
张艳宁
Yang Yun;Zhang Haiyu;Zhu Yu;Zhang Yanning(College of Electrical &Information Engineering,Shaanxi University of Science and Technology,Xi'an 710021,China;School of Computer Science,Northwestern Polytechnieal University,Xi'an 710129,China)
出处
《中国图象图形学报》
CSCD
北大核心
2018年第12期1777-1788,共12页
Journal of Image and Graphics
基金
国家自然科学基金青年科学基金项目(61601271)
陕西省重点研发计划项目(2017NY-124)
陕西省科学技术研究发展计划项目(2014K15-03-06)
陕西省社会发展科技攻关项目(2016SF444
2015SF277)
西安市科技计划项目(NC1319(1)
NC1403
NC1403(2))~~
关键词
SRGAN
感知损失函数
MSE
类别信息
class-info
SRGAN
super-resolution based on generative adversarial network (SRGAN)
perceptual loss function
Mean Square Error (MSE)
class-info
class-info SRGAN
