首页> 外文期刊>Journal of optical technology >Study on the key technology of optical encryption based on adaptive compressive ghost imaging for a large-sized object
【24h】

Study on the key technology of optical encryption based on adaptive compressive ghost imaging for a large-sized object

机译:基于自适应压缩重影的大型物体光学加密关键技术研究

获取原文
获取原文并翻译 | 示例
获取外文期刊封面目录资料

摘要

Computational ghost imaging is a good optical encryption method, but it can have difficulty imaging a large-sized object and can be time consuming. To solve the problem, we propose a novel optical encryption method based on block adaptive compressive sensing with computational ghost imaging. In this model, we divide the large-sized image into several blocks. Then, each block is considered as a single image while performing the ghost imaging. Each block has its own sampling ratio according to the human visual system. In the recovery process, we use a compressive sensing algorithm to reconstruct the image. Compared with computational ghost imaging, the quality of the recovery image is better; thus, a large-sized image can also be recovered with high quality with this method. In addition, the quantity of transmitted information is reduced compared with block computational ghost imaging, resulting in less space, high-efficiency data storage, or transmission time. With its advantages of high-quality reconstructed information, high security, and fast transmission, this technique can be immediately applied to imaging applications and data storage. (C) 2017 Optical Society of America
机译:计算重影成像是一种很好的光学加密方法,但它对大型物体的成像可能很困难,而且可能很耗时。针对该问题,我们提出了一种基于块自适应压缩感知的计算鬼成像的光学加密方法。在这个模型中,我们将大尺寸图像分成几个块。然后,在执行重影成像时,将每个块视为单个图像。根据人类视觉系统,每个块都有自己的采样率。在恢复过程中,我们使用压缩感知算法来重建图像。与计算重影相比,恢复图像质量更好;因此,使用这种方法也可以高质量地恢复大尺寸图像。此外,与块计算重影相比,传输的信息量减少了,从而减少了空间、高效的数据存储或传输时间。该技术具有信息重建质量高、安全性高、传输速度快等优点,可立即应用于成像应用和数据存储。(C) 2017年美国光学学会

著录项

  • 来源
    《Journal of optical technology》 |2017年第7期|471-476|共6页
  • 作者单位

    Univ Shanghai Sci & Technol, Coll Commun & Art Design, 516 Jun Gong Rd, Shanghai 200093, Peoples R China;

    China United Network Commun Corp, Jinan Branch, Jinan 250000, Shandong, Peoples R China;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 英语
  • 中图分类 光学;
  • 关键词

获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号