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基于计算全息图的双重加密算法研究 被引量:7

A Double Encryption Algorithm Research Based on Computer Generated Hologram
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摘要 为了确保现代网络信息传输的安全性问题,提出了一种基于压缩感知理论和分块Arnold变换置乱的计算全息图双重加密算法。该方法制作出原始图像的纯相位全息图,利用压缩感知的随机测量矩阵作为密钥对全息图进行初次加密,将加密后的图像通过分块Arnold变换置乱再次加密。使用密钥对经过两次加密的图像解密可重现原始图像。该全息加密方法相较于传统光学加密设计灵活、光路简单,且两次加密均具有随机性,从而大大提高了信息传输的安全性。结果表明,解密恢复的图像质量理想、安全性高、稳健性强。通过搭建基于硅基液晶空间光调制器的全息显示系统对提出的加密方法进行了实验验证。 To ensure the security issues of information transmission in modern network, a double encryption algorithm of computer generated hologram based on compressed sensing theory and block Arnold transformation scrambling has been proposed. The phase-only hologram of original image has been produced, the hologram has been encrypted by using the random measurement matrix of compressed sensing as a key. It has been encrypted again by block Arnold transformation scrambling. The image after two-time encryption can be decrypted by using the keys and reproduce the original one. Compare to conventional optical holographic encryption, this method has a more flexible design, simple optical path and the two encryptions both have randomness, so it improves the security of information transmission greatly. Result shows that the decryption image is ideal, safe and robust. The proposed method is verified by building a holographic display system based on silicon liquid crystal spatial light modulator.
出处 《中国激光》 EI CAS CSCD 北大核心 2015年第9期284-289,共6页 Chinese Journal of Lasers
基金 安徽省自然科学基金(1508085MF121) 校基金(zryy1311)
关键词 全息 图像加密 计算全息 压缩感知 ARNOLD变换 空间光调制器 holography image encryption computer generated hologram compressed sensing arnold transformation spatial light modulator
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  • 1朱珍超,张玉清.基于量子理论的秘密共享方案研究[J].通信学报,2009,30(S2):127-132. 被引量:2
  • 2XIAO Guozhen,LU Mingxin,QIN Lei,LAI Xuejia.New field of cryptography: DNA cryptography[J].Chinese Science Bulletin,2006,51(12):1413-1420. 被引量:16
  • 3朱焕东,黄春晖.量子密码技术及其应用[J].国外电子测量技术,2006,25(12):1-5. 被引量:7
  • 4位恒政,彭翔,张鹏,刘海涛,封松林.双随机相位加密系统的选择明文攻击[J].光学学报,2007,27(5):824-829. 被引量:15
  • 5D Donoho. Compressed sensing[ J]. IEEE Trans Inform Theory,2006,52(4) : 1289 - 1306.
  • 6M A T Figueiredo, R D Nowak, S J Wright. Gradient projection for sparse reconstruction: Appfication to compressed sensing and other inverse problems [ J ]. IEEE J Selected Topics in Signal Processing: Special Issue on Convex Optimization Methods for Signal Processing, 2007,1(4) :586 - 598.
  • 7I Daubechies, M Defrise, C De Mol. An iterative thresholding algorithm for finear inverse problems with a sparsity constraint [ J]. Comm Pure Appl Math,2004,57( 11 ):1413 - 1457.
  • 8T Blumensath, M Davies. Iterative hard thresholding for compressed sensing[ J]. Appl Comput Harmon Anal, 2009, 27 ( 3 ) : 265 - 274.
  • 9A C Gilbert, S Guha, P Indyk, S Muthukrishnan, M J Strauss. Near-optimal sparse Fourier representations via sampling[ A]. Proc. of the 2002 ACM Symposium on Theory of Computing STOC[C]. Montreal, Quebec, Canada, 2002. 152 - 161.
  • 10E Candbs, J Romberg, T Tao. Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information [ J]. IEEE Trans Inform Theory ,2006,52(2) :489- 509.

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