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人眼视觉系统中的视觉感知差异是图像质量评价过程中的重要组成部分,通过感知失真图像与原始图像之间的视觉差异,可对图像的失真程度进行判断,然而在无参考图像质量评价中无法获取原始未失真的图像,且缺乏对失真图像的视觉感知差异。通过对深度学习中的生成对抗网络进行分析,提出一种基于生成视觉感知差异的无参考图像质量评价模型。利用生成对抗网络产生与失真图像相对应的视觉感知差异图像,并将其与失真图像输入质量评价网络以进一步学习图像的失真信息,从而达到评估图像质量的目的。在TID2008和TID2013数据库上的实验结果表明,与CNN、SOM、CORNIA等模型相比,该模型能够使失真图像质量预测准确度提升1个百分点以上,且对不同种类失真也表现出良好的预测性能。 相似文献
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基于.net 2.0三层架构的青岛市数字城建档案馆 总被引:3,自引:2,他引:1
为了提高城市建设档案的管理水平和利用率,为数字城市打下良好的基础,基于.ner2.0组件技术,采用反射工厂模式设计数据访问层,实现了基于分层架构的"青岛市数字城建档案馆"系统.系统综合运用.net framework2.0、Asp.net2.0、A5p.net Aiax、水晶报表等技术,借助GIS实现二维电子地图与城建档案的无缝衔接,提供了电子图片档案、声像挡案的在线浏览和播放功能,实现了城建档案数据、图像、声像的有机结合,提高了城建档案的管理效率和使用率,为相同类型的城建系统提供了一定的参考作用. 相似文献
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家庭和一般单位养苏铁,时间长了,常会碰到这样的问题,花盆越换越大,而苏铁也一年比一年高,到后来,没地方放不说,就是想搬一下也不容易。盆内根系紧密,盆土坚硬,导致生长不良,失去其固有的观赏价值。为解决这一问题,我们在前后三年的时间内,对自己的两盆苏铁进行截干养桩、打头扦插,收到了很好的更新复壮效 相似文献
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The resolution and quality of the depth map captured by depth cameras are limited due to sensor hardware limitations, which becomes a roadblock for further computer vision applications. In order to solve this problem, we propose a new method to enhance low-resolution depth maps using high-resolution color images. The structural-aware term is introduced because of the availability of structural information in color images and the assumption of identical structural features within local neighborhoods of color images and depth images captured from the same scene. We integrate the structural-aware term with color similarity and depth similarity within local neighborhoods to design a local weighting filter based on structural features. To use non-local self-similarity of images, the local weighting filter is combined with the concept of non-local means, and then a non-local weighting filter based on structural features is designed. Some experimental results show that super-resolution depth image can be reconstructed well by the process of the non-local filter and the local filter based on structural features. The proposed method can reconstruct much better high-resolution depth images compared with previously reported methods. 相似文献