检索规则说明:AND代表“并且”;OR代表“或者”;NOT代表“不包含”;(注意必须大写,运算符两边需空一格)
检 索 范 例 :范例一: (K=图书馆学 OR K=情报学) AND A=范并思 范例二:J=计算机应用与软件 AND (U=C++ OR U=Basic) NOT M=Visual
作 者:韩玉兰 崔玉杰 罗轶宏 兰朝凤 HAN Yulan;CUI Yujie;LUO Yihong;LAN Chaofeng(School of Measurement and Control Technology and Communication Engineering,Harbin University of Science and Technology,Harbin 150080,China;Heilongjiang Province Key Laboratory of Pattern Recognition and Information Perception,Harbin University of Science and Technology,Harbin 150080,China)
机构地区:[1]哈尔滨理工大学测控技术与通信工程学院,哈尔滨150080 [2]哈尔滨理工大学模式识别与信息感知黑龙江省重点实验室,哈尔滨150080
出 处:《电子与信息学报》2024年第12期4563-4574,共12页Journal of Electronics & Information Technology
基 金:国家自然科学基金(11804068);黑龙江省省属高等学校基本科研业务(2020-KYYWF-0342)。
摘 要:生成对抗网络因其为盲超分辨率重构提供了新的思路而备受关注。针对现有方法未充分考虑图像退化过程中的低频保留特性而对高低频成分采用相同的处理方式,缺乏对频率细节有效利用,难以获得较好重构效果的问题,该文提出一种基于密集残差和质量评估引导的频率分离生成对抗超分辨率重构网络。该网络采用频率分离思想,对图像的高频和低频信息分开处理,从而提高高频信息捕捉能力,简化低频特征处理。该文对生成器中的基础块进行设计,将空间特征变换层融入密集宽激活残差中,增强深层特征表征能力的同时对局部信息差异化处理。此外,利用视觉几何组网络(VGG)设计了专门针对超分辨率重构图像的无参考质量评估网络,为重构网络提供全新的质量评估损失,进一步提高重构图像的视觉效果。实验结果表明,同当前先进的同类方法比,该方法在多个数据集上具有更佳的重构效果。由此表明,采用频率分离思想的生成对抗网络进行超分辨率重构,可以有效利用图像频率成分,提高重构效果。With generative adversarial networks have attracted much attention because they provide new ideas for blind super-resolution reconstruction.Considering the problem that the existing methods do not fully consider the low-frequency retention characteristics during image degradation,but use the same processing method for high and low-frequency components,which lacks the effective use of frequency details and is difficult to obtain better reconstruction result,a frequency separation generative adversarial super-resolution reconstruction network based on dense residual and quality assessment is proposed.The idea of frequency separation is adopted by the network to process the high-frequency and low-frequency information of the image separately,so as to improve the ability of capturing high-frequency information and simplify the processing of low-frequency features.The base block in the generator is designed to integrate the spatial feature transformation layer into the dense wide activation residuals,which enhances the ability of deep feature representation while differentiating the local information.In addition,no-reference quality assessment network is designed specifically for super-resolution reconstructed images using Visual Geometry Group(VGG),which provides a new quality assessment loss for the reconstruction network and further improves the visual effect of reconstructed images.The experimental results show that the method has better reconstruction effect on multiple datasets than the current state-of-the-art similar methods.It is thus shown that super-resolution reconstruction using generative adversarial networks with the idea of frequency separation can effectively utilize the image frequency components and improve the reconstruction effect.
关 键 词:超分辨率 生成对抗网络 频率分离 质量评估 密集残差
分 类 号:TN911.73[电子电信—通信与信息系统] TP391[电子电信—信息与通信工程]
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在链接到云南高校图书馆文献保障联盟下载...
云南高校图书馆联盟文献共享服务平台 版权所有©
您的IP:216.73.216.28