Range-gated laser image compression and encryption scheme based on bidirectional diffusion

Abstract

The existing image encryption schemes are not suitable for the secure transmission of large amounts of data in range-gated laser imaging under high noise background. Aiming at this problem, a range-gated laser imaging image compression and encryption method based on bidirectional diffusion is proposed. The image data collected from the range-gated laser imaging source is sparsely represented by the discrete wavelet transform. Arnold chaotic system is used to scramble the sparse matrix, and then the measurement matrix is constructed by the quantum cellular neural network (QCNN) to compress the image. In addition, the random sequence generated by QCNN hyperchaotic system is used to carry out “bidirectional diffusion” operation on the compression result, so as to realize the security encryption of image data. The comparative analysis of the security encryption performance of different compression ratios shows that the histogram sample standard of the encrypted image can reach about 10, and the information entropy value is more than 7.99, which indicates that the encryption scheme effectively hides the plaintext information of the original image. When the encrypted image is attacked by different degrees of noise, this method can still reconstruct the image through the effective decryption process. The experimental results show that this method realizes the secure compression and encryption of gated-laser imaging image data, and effectively ensures the security of data while reducing the amount of channel transmission data.

References

[1] Shu-yuW, Sheng-xiangT, DongC, Guo-huaG. Optoelectronics Letters, 2019, 15: 21

[2] Andresen BjørnF, SteinvallO, Fulop GaborF, AnderssonP, ElmqvistM, Norton PaulR, TulldahlM. Proceedings of SPIE, 2007, 6542: 654216

[3] Huckridge DavidA, GöhlerB, Ebert ReinhardR, LutzmannP, AnstettG. Proceedings of SPIE, 2008, 7113: 711307

[4] Kamerman GaryW, LaurenzisM, SteinvallO, ChristnacherF, BacherE, Bishop GaryJ, Gonglewski JohnD, MetzgerN, SchertzerS, Lewis KeithL, Hollins RichardC, ScholzT, Merlet ThomasJ. Proceedings of SPIE, 2011, 8186: 818603

[5] PaulM, MarkK. Proceedings of SPIE, 1997, 3086: 2

[6] BinG, Da-huaH. Optics & Optoelectronic Technology, 2017, 15: 10(in Chinese)

[7] Wen-leiL, ZhangX, LiuK. Journal of Ordnance Equipment Engineering, 2019, 40: 199(in Chinese)

[8] XiaodongC, XiaoqiangD, JinqingL, JianpingZ, XiaojieL, HuiY, YifeiP, ChunmingL, ZhigengP. Proceedings of SPIE, 2019, 11069: 174

[9] ZhenlongM, JinqingL, XiaoqiangD, OuB. IEEE Access, 2019, 7: 103047

[10] Han-qinY. Journal of CAEIT, 2019, 14: 831(in Chinese)

[11] Daniel HerreraC, KannalaJ, HeikkilaJ. IEEE Trans. Pattern Anal. Mach. Intell., 2012, 34: 2058

[12] MeilinX, PengL, CaiwenM, WeiH, JianL, XubinF. Optik, 2018, 157: 556

[13] DonohoD L. IEEE Transactions on Information Theory, 2006, 52: 1289

[14] Candès EmmanuelJ, Romberg JustinK, TaoT. Communications on Pure and Applied Mathematics, 2006, 59: 1207

[15] LuigiF, DomenicoP. International Journal of Bifurcation and Chaos, 2004, 14: 1085

[16] ChuhanW, JunC, ChenggenQ, XiaofangZ, YongjianZ. Results in Optics, 2020, 1: 100021

[17] Ming TeW. Information Sciences, 2019, 474: 125

[18] Tropp JoelA, Gilbert AnnaC. IEEE Transactions on Information Theory, 2007, 53: 4655

[19] XiuliC, ZhihuaG, KangY, YiranC, XianxingL. Signal Processing: Image Communication, 2017, 52: 6

[20] ChanilP, LilianH. Signal Processing, 2017, 138: 129

[21] YongZ, AiguoC, YingjunT, JianwuD, GuopingW. Information Sciences, 2020, 526: 180