激光与光电子学进展, 2016, 53 (4): 041002, 网络出版: 2016-03-25   

基于压缩感知的光学干涉双灰度图像加密系统

Optical Interference Double Gray Image Encryption System Based on Compressive Sensing
作者单位
1 通辽职业学院机电工程学院, 内蒙古 通辽 028000
2 南阳师范学院物理与电子工程学院, 河南 南阳 473061
摘要
提出了一种基于压缩感知(CS)和干涉原理的双灰度图像加密方法。该方法分别从两幅灰度图像中随机提取50%的数据,并将这些数据通过融合形成一幅合成图像(SI)。再将该合成图像加密至三个纯相位板(POMs)中。其中一个随机相位板使用随机函数生成,另外两个通过解析的方法得到。解密时,利用分束片对3 个POMs 的衍射场进行叠加,在光学解密装置中利用CCD 记录合成图像,再从合成图像中分别提取的两幅原始图像信息。尽管对于每一幅原始图像来说,只能准确提取其50%的数据,但是压缩感知重构算法可以高质量的重现这两幅原始图像。与先前提出的方法相比,该方法加密过程是一个完全采用解析算法的过程,并且非常省时,因为在解密过程中没有迭代算法。此外,该方法也消除了先前提出的光学干涉加密方法存在的轮廓像问题,具有较高的安全性。计算机模拟结果证实了该方法的有效性。
Abstract
A novel double gray image encryption method is proposed based on compressive sensing (CS) and interference principle. The 50% image data of two gray images are random extracted by the proposed method, these data are combined to form a synthetic image (SI), and then it is hidden into three phase only masks (POMs). One of the phase masks is produced by random function and the other two are obtained by analytic method. For decryption, the diffraction fields of the three POMs are superposed by employing the beam splitters. The intensity of the complex field, namely the SI, is captured by CCD camera, and then the information of two original images is extracted from the SI. Although only 50% fragmentary data are extracted, the subsequent CS reconstruction will retrieve a high quality image from the fragmentary information. Compared with the earlier interference- based method, the proposed approach is a process of employing resolution algorithm completely and time-saving since no iterative algorithm is involved in the encryption process. Moreover, the silhouette problem existing in the earlier method is resolved by the proposed method with higher security. Simulation results are presented to support the validity of the proposed approach.

白音布和, 吕晓东, 李根全, 秦怡. 基于压缩感知的光学干涉双灰度图像加密系统[J]. 激光与光电子学进展, 2016, 53(4): 041002. Bai Yinbuhe, Lü Xiaodong, Li Genquan, Qin Yi. Optical Interference Double Gray Image Encryption System Based on Compressive Sensing[J]. Laser & Optoelectronics Progress, 2016, 53(4): 041002.

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