Active single-pixel imaging (also known as illumination-modulated single-pixel imaging) employs a spatial light modulator to illuminate a scene with structured patterns. The scheme of active single-pixel imaging is similar to a wireless broadcast system, allowing that multiple receivers use a single-pixel detector to capture an image simultaneously from a different place. The use of basis patterns allows for high-quality reconstructions and an efficient sampling process, but the public knowledge of the basis patterns is not a favorable feature for security applications. In order to develop a secured broadcast single-pixel imaging system, we propose to employ block-permutated Hadamard basis patterns for illumination. The randomness in permutation operations introduces strong security characteristics for the system. Both simulation and experimental results demonstrate our proposed scheme has satisfactory imaging quality and efficiency. This work generates a new insight for the application of single-pixel imaging and provides a solution for developing a secured imaging system for non-visible wavebands.
© 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
Single-pixel imaging is a novel imaging scheme that allows one to capture a scene without using a conventional two-dimensional (2-D) imaging sensor. Single-pixel imaging has some unique features . It allows for the use of a low-cost single-pixel detector instead of an expensive pixelated detector, which has demonstrated essential for imaging over invisible wavebands . It is easier and less expensive to manufacture single-pixel detectors with a large active area, which makes single-pixel imaging techniques more suitable for imaging under weak-light conditions than conventional imaging techniques. With these advantages, single-pixel imaging has been recently extensively applied in different fields such as terahertz imaging , remote sensing , three-dimensional (3-D) imaging [5–7], microscopy , scattering imaging , hyperspectral imaging , X-ray imaging , shadowless imaging , and multi-modality imaging . In addition, the research of encrypted single-pixel imaging has attracted much attention in recent years [14–21].
To acquire spatial information using a single-pixel detector, single-pixel imaging modulates either illumination or detection light signals to encode spatial information into a one-dimensional (1-D) light signals. The resulting 1-D signals can be collected by a detector without spatial resolution (i.e. single-pixel detector or bucket detector). Approaches based on illumination modulation are termed active single-pixel imaging. We find some similarities between an active single-pixel imaging system and a wireless broadcast system. The resulting light signals emitted by the objects under modulated illumination in an active single-pixel imaging system are similar to the amplitude modulated signals emitted from a base station in a broadcast system. The utilized single-pixel detector in an active single-pixel imaging system is like a receiving antenna in a broadcast system. Thus, active single-pixel imaging has a property that as long as the detector receives the light signals one is able to recover the object image. Consequently, single-pixel detectors need not direct line-of-sight to the object. This property enables single-pixel broadcast imaging . Objects under modulated illumination emit light signals. As such, multiple receivers located at different places can capture the same image simultaneously. However, this property would lead to risks of information leakage, because there is no way to ban any unauthorized receivers to collect the broadcasted light signals. Thus, developing a secured single-pixel broadcast imaging system is meaningful.
When the object to be imaged is confidential, the object image needs protection. It will be favorable that the detected light signals and the reconstructed object image are in an encrypted format. It is also favorable that optical imaging and information encryption are performed simultaneously with the same system. In previous works, optical encryption approaches  have been extensively investigated, such as 4-f lens system , holography , optical interference , photo counting technology , joint transform correlator , and ptychography .
An encrypted single-pixel imaging system can be formulated as a symmetrical optical encryption model–the object image is considered as the plaintext, the illumination patterns as the encryption (decryption) key, and the single-pixel measurements as the ciphertext. The object image (plaintext) can be recovered from the single-pixel measurements (ciphertext) only when the illumination (key) is known.
In previous works [14–21], random illumination patterns have been used in encrypted single-pixel imaging systems, because the randomness of the illumination is a suitable key for image information protection. However, from the perspective of imaging quality and sampling efficiency, random patterns are not an optimal choice in single-pixel imaging. Although one can use methods such as compressive sensing , Gerchberg-Saxton iteration  and error correction coding [18,32,33] to improve the imaging quality, the results are not satisfactory enough, which limits practicability of the technique. Recently, single-pixel imaging techniques using basis patterns [34,35] have been demonstrated able to reproduce high-quality reconstructions, but the public knowledge of the basis patterns is not a favorable feature for security applications. Thus, encrypted basis-scan single-pixel imaging has not been reported yet.
In this paper, we propose a novel encrypted basis-scan single-pixel imaging technique which is achieved by pixel-level permutation for every single basis pattern. We experimentally demonstrate that the proposed technique not only provides satisfactory security level but also preserves the merit of high reconstruction quality and sampling efficiency. The proposed technique enables a secured single-pixel broadcast system. This work generates a new insight for the application of single-pixel imaging. It also provides a solution for developing a secured imaging system for non-visible wavebands.
2.1 Single-pixel imaging with random patterns and basis patterns
As Fig. 1 shows, the proposed secured broadcast imaging scheme is based on an active single-pixel imaging system. At the illumination end (a sender), a spatial light modulator (the digital projector in the figure) is employed to generate structured illuminations onto an object scene. The 2-D spatial information of the object scene is encoded into a 1-D intensity sequence. As such, the detection end (a receiver) is able to capture a scene by collecting scattering light with a single-pixel detector. By using encrypted patterns for illumination, the 2-D spatial information stored in the 1-D intensity sequence is protected by a key. The object image (plaintext) can be recovered from the single-pixel measurements (ciphertext) only when the key is known. Otherwise, the reconstruction is still in a ciphertext form.
In a general single-pixel imaging model, a sequence of 2-D intensity patterns is employed to illuminate the object image . A 1-D single-pixel measurements is recorded by a single-pixel detector. The mathematical relationship between , and can be described by Eq. (1),14] or basis patterns for basis-scan single-pixel imaging [34,35].
For random patterns, the image can be approximately reconstructed by correlating the illumination patterns with the single-pixel measurements , described by Eq. (2).14–21] about encrypted single-pixel imaging. The use of random patterns leads to a disadvantage of noise contamination and low sampling efficiency.
2.2 Proposed single-pixel imaging with encrypted basis patterns
In this paper, basis-scan single-pixel imaging scheme is adopted. Basis-scan single-pixel imaging uses corresponding basis patterns for illumination. For example, Hadamard single-pixel imaging  uses Hadamard basis patterns while Fourier single-pixel imaging  uses Fourier basis patterns (also known as sinusoidal patterns). Due to the orthogonality of basis patterns, basis-scan single-pixel imaging allows for perfect reconstruction in fully-sampled condition. By utilizing the sparsity of natural scene images, basis-scan single-pixel imaging is able to reconstruct an image with sharp features even under under-sampling condition (that is, measurements less than total number of image pixels). Thus, basis-scan single-pixel imaging exhibits advantages of high imaging sampling speed efficiency and high imaging quality. However, the basis patterns are public knowledge and constant for different object images.
To encrypt object images, we can exploit the relativity between the illumination patterns and the object image so as to achieve object image encryption equivalently. Hadamard basis patterns are chosen to be encrypted, as they are binary and, therefore, suitable for high-speed generation by using a digital micro-mirror device (DMD). It should be noted that DMD-based fast Fourier single-pixel imaging techniques have also been reported recently. Z. Zhang et al. proposed to binary originally grayscale Fourier basis patterns by using a spatial dithering strategy . Y. Zhang et al. proposed to binary Fourier basis patterns by using an adaptive spatial filter . J. Yang et al. proposed to generate grayscale Fourier basis patterns by using computer-generated binary holograms . Thus, the proposed scheme is applicable for Fourier single-pixel imaging. For basis patterns encryption, we propose to use permutation. Permutation (or scrambling) refers to the rearrangement of elements in a different order for a given sequence. An example of permutation operation performed on a 2-D intensity pattern is illustrated in Fig. 2. The image to be encrypted is in a size of pixels, referring to the plaintext. The image can be represented by an -element matrix. First, the image is reshaped to an -element array. Given a key which indicates the exact pixel position mapping relationship, the plaintext array is then permutated accordingly which results in a ciphertext array at the same length. Finally, the ciphertext array is reshaped back to an -element matrix and the encrypted image is derived. With the correct key, the original image can be recovered (decrypted) from the permutated result. Otherwise, the decryption is still in a ciphertext form revealing no plaintext information. In addition, the permutated Hadamard patterns are still binary so that it permits high-speed single-pixel imaging. Although permutation has been previously adopted in many optical encryption and optical information hiding works [39–46], it is the first time that permutation is used in basis-scan single-pixel imaging.
Hadamard single-pixel imaging is based on Hadamard transform. A Hadamard transform can decompose an arbitrary grayscale image into a superposition of Hadamard binary basis pattern multiplied with Hadamard coefficients (). In single-pixel imaging, if the Hadamard basis patterns are sequentially employed as illumination patterns, the single-pixel measurements are equivalent Hadamard coefficients of object image , illustrated in Eq. (3). Thus, the object image can be computationally reconstructed from the single-pixel measurements through an inverse 2-D Hadamard transform, illustrated in Eq. (4).Eq. (5),Eq. (6) shows, the corresponding single-pixel measurement is when the encrypted pattern is used for illumination.Eq. (7).
For unauthorized users without the key , the original object image cannot be recovered from and the security protection of imaging result can be achieved.
In practice, the permutation operation is not necessarily performed on pixel level but also on a ‘super pixel’ level. ‘Super pixel’ refers to pixel binning, that is, an image block consists of × pixels. As Fig. 3 shows, when increases, the security strength of permutation encryption is weakened since the key space is reduced but the imaging quality and speed can be enhanced. This can be explained in the following. The basis-scan single-pixel imaging requires much smaller number of illuminations than conventional single-pixel imaging with random illumination patterns for two reasons. First, the basis patterns are orthogonal for a transform while random intensity patterns are not. Second, smooth images of a natural scene are characterized by that the energy concentrates at the low frequency band in the transformation domain. Thus, the acquisition of only partial spectrum components is sufficient to reconstruct object image with acceptable quality. When the object image is permutated on a pixel basis and becomes a random intensity image, the orthogonal basis criterion is still satisfied but the low frequency concentration criterion is no longer satisfied.
The energy is evenly distributed in the entire transformation domain. By increasing the block size , the permutated object image tends to be smoother and the low frequency concentration criterion can be satisfied again to some extent, which is favorable for efficient data acquisition. In terms of imaging quality and sampling efficiency, the proposed technique has an advantage over random pattern single-pixel imaging, but has certain degradation compared with conventional basis-scan single-pixel imaging without encryption. The value of can be appropriately designed to balance the security strength and imaging quality.
3. Results and discussion
The proposed technique is verified by both computer simulation and optical experiments in this section.
3.1 Simulation results
A test image ‘cameraman’ with 256 × 256 pixels is encrypted and decrypted for different values of and sampling ratios. The Hadamard basis patterns for illumination are permutated with three permutation block sizes , , and . In Fig. 4, we show the reconstructed images decrypted by using the correct key (upper row) and an incorrect key (bottom row). As the results show, the object is not recognizable from the images decrypted with an incorrect key, which demonstrates the effectiveness of the proposed technique in terms of security.
For further demonstration, we quantitatively evaluate the security of the proposed technique by employing two commonly used methods, auto-correlation coefficients of neighboring pixels and plaintext-ciphertext cross-correlation coefficients .
For a plaintext image of a natural scene, the intensity values of neighboring pixels are highly correlated. Instead, the correlation is low for an ideal encrypted ciphertext. Thus, calculating the auto-correlation coefficients of neighboring pixels is an effective approach for quantitative evaluation of the degree of security. Table 1 shows the auto- correlation coefficients for the simulation reconstructions (including the plaintext and the reconstructions without decryption). To calculate the auto-correlation coefficients, we shift the images horizontally with 0, 1, 4, and 8 pixels, respectively.
The results show that the auto-correlation coefficients for ciphertext images are lower than those for the plaintext especially when or when the pixel shift is equal or larger than block size, which is favorable for information protection.
Additionally, the results of security degree evaluated by the cross correlations between plaintext image and ciphertext are shown in Table 2. It can be seen that the cross correlation values are rather low, which indicates a good performance in terms of security. For comparison, we also calculate the cross correlations between the ciphertexts and two other reference images [Figs. 4(g) and 4(h)]. As the results show, the cross correlations tend to zero. There is no significant difference between plaintext-ciphertext cross correlations and reference-ciphertext cross correlations. Some of the plaintext-ciphertext cross correlations [for example, Figs. 4(d) and 4(f)] are even lower than the reference-ciphertext counterparts. It indicates that any attacker has difficulties in identifying which plaintext image the ciphertext image corresponds to.
According to the results given by both security level evaluation methods, we conclude that the proposed technique is able to provide a considerable security level.
In the second simulation, we verify the proposed technique in terms of sampling efficiency. As are sparse in Hadamard domain, images of a natural scene can be recovered for under-sampling (the number of illuminations is smaller than the total number of patterns ). In practice, one can acquire the Hadamard transform of an image with a ‘zig-zag’ order from the origin . In Fig. 5, we show the initial reconstruction and decryption pairs for different permutation block sizes and sampling ratios. Additionally, we take ghost imaging in comparison. To quantitatively evaluate the quality of reconstructions, structural similarity index (SSIM)  is employed.
As the figure shows, the original object image is not recognizable from the initial reconstructions. Only after permutation decryption with the correct key, the plaintext image appears. The figure also shows that the reconstructed image quality improves as the number of illumination patterns (sampling ratio) increases in all schemes (including encrypted Hadamard single-pixel imaging, conventional Hadamard single-pixel imaging without encryption, and encrypted single-pixel imaging with random patterns). However, the reconstructed image quality improves very slowly by increasing illumination numbers for single-pixel imaging with random patterns. It is evident that the proposed technique can achieve significantly better reconstructed image quality than encrypted single-pixel imaging with random patterns under the same number of illuminations. In other words, much less illuminations are required in the proposed technique to achieve the same imaging quality, which indicates a significantly faster imaging speed. As the results shown in Fig. 5, the imaging quality of the proposed technique is degraded compared with conventional Hadamard single-pixel imaging. However, as increases, the quality in the former scheme becomes closer to the latter. When the sampling ratio , in both encrypted and non-encrypted Hadamard single-pixel imaging, the reconstructed image quality can be identical to the original image.
Relatively, the security strength with a smaller block size is higher than the one with larger block size. For the quality of decryptions is close to that of reconstructions without encryption, which demonstrates that a larger substantially results in better efficiency.
The computational time of the proposed technique in image reconstruction and decryption is evaluated. The results are shown in Table 3. Please note that the results are the averaged computational time for 10,000 loops. We implement image reconstruction and decryption using MATLAB R2014a on the computer equipped with an Intel Xeon E3-1231 v3 @3.4GHz CPU, 8GB memory, and Windows 7 Ultimate x64 operating system. For the proposed technique, image reconstruction refers to an inverse 2-D Hadamard transform and decryption refers to a permutation process [Eq. (7)]. As the results show, the computational time for both inverse Hadamard transform and permutation decryption is at a millisecond (ms) level, which can be negligible. Thus, we conclude that the proposed technique is computationally efficient.
3.2 Experimental results
We perform an experiment to demonstrate the proposed secured single-pixel broadcast imaging system. The schematic diagram of our experimental set-up is shown in Fig. 6. We use two photodiodes to simulate an unauthorized receiver (Photodiode 1, PD1) who has an incorrect key and an authorized receiver (Photodiode 2, PD2) who has the correct key for decryption. Both photodiodes are hidden from the object scene behind a piece of ground glass. A pair of toys is used as a complex 3-D scene. We use a DLP development kit (Texas Instruments DLP 4100) for spatial light illumination. The DLP development kit is equipped with a 0.7-inch DMD which has 1024 × 768 micro mirrors and the mirror size is 13.6 × 13.6 μm2. We generate three sets of patterns–original Hadamard patterns, encrypted Hadamard patterns with Key 1 and , 2, 4, and 8, and encrypted Hadamard patterns with Key 2 and 1, 2, 4, and 8. A 3-watt white LED is used for the illumination source. In order to eliminate noise caused by background illumination, we employ differential Hadamard single-pixel imaging, that is, a pattern followed by its inversed pattern is projected. The resolution of illumination patterns is 256 × 256 pixels. Thus, the total number of measurements of fully-sample reconstruction is 131,072 ( = 256 × 256 × 2). The generated patterns are displayed on the DMD and projected onto the scene through a lens system. Please note that the lens system is from a commercial digital projector (Toshiba T-95). A photodiode (HAMAMATSU S1227-1010BR) is used to collect the intensity of resulting light fields from the scene. The electronic signals are transferred to the computer via a data acquisition board [National Instruments USB-6343 (BNC)].
As the results shown in Fig. 7, the object scene is not recognizable from the recovered encrypted images for different sampling ratios and values of , which demonstrates our proposed technique can hide information well under different situations. The results for these images further decrypted with the correct key and an incorrect key are shown in Fig. 7. The results verify the effectiveness of the proposed technique. Figure 7 also presents a comparison among different sampling ratios. As the sampling ratio increases, the decrypted images become clearer and less noisy. When the sampling ratio reaches 100%, the results for encrypted systems with different values and unencrypted systems are identical. The experimental results are consistent with the simulation results.
We propose an encrypted Hadamard single-pixel imaging system to achieve secured broadcast imaging in this paper. We employ permutated Hadamard basis patterns, instead of random intensity illumination patterns, for securing object image information. The randomness in permutation operations introduces strong security characteristics for the system. The trade-off between security strength and reconstructed image quality for varying permutation block sizes is discussed. Simulation and experimental results demonstrate our proposed technique has significantly better imaging quality and higher sampling efficiency than conventional encrypted single-pixel imaging systems using random intensity illumination patterns. This work generates a new insight for the application of single-pixel imaging and provides a solution for developing a secured imaging system for non-visible wavebands.
National Natural Science Foundation of China (NSFC) (61475064); Chinese Postdoctoral Science Foundation (2017M622763); Pearl River Talent Plan (Postdoctoral Scheme, 2016), Guangdong Province.
The authors thank Dr. Bowen Jiang for her precious time and invaluable comments on this work, Xiao Ma for drawing Fig. 1, Dr. Shiping Li for her help with the experimental equipment preparation, and Qinqiu Fang for linguistic assistance.
References and links
1. J. H. Shapiro, “Computational ghost imaging,” Phys. Rev. A 78(6), 061802 (2008). [CrossRef]
2. B. I. Erkmen and J. H. Shapiro, “Ghost imaging: from quantum to classical to computational,” Adv. Opt. Photonics 2(4), 405–450 (2010). [CrossRef]
3. W. Chan, K. Charan, D. Takhar, K. Kelly, R. Baraniuk, and D. Mittleman, “A single-pixel terahertz imaging system based on compressed sensing,” Appl. Phys. Lett. 93(12), 121105 (2008). [CrossRef]
5. B. Sun, M. P. Edgar, R. Bowman, L. E. Vittert, S. Welsh, A. Bowman, and M. J. Padgett, “3D computational imaging with single-pixel detectors,” Science 340(6134), 844–847 (2013). [CrossRef] [PubMed]
7. M. J. Sun, M. P. Edgar, G. M. Gibson, B. Sun, N. Radwell, R. Lamb, and M. J. Padgett, “Single-pixel three-dimensional imaging with time-based depth resolution,” Nat. Commun. 7, 12010 (2016). [CrossRef] [PubMed]
8. R. S. Aspden, N. R. Gemmell, P. A. Morris, D. S. Tasca, L. Mertens, M. G. Tanner, R. A. Kirkwood, A. Ruggeri, A. Tosi, R. W. Boyd, G. S. Buller, R. H. Hadfield, and M. J. Padgett, “Photon-sparse microscopy: visible light imaging using infrared illumination,” Optica 2(12), 1049 (2015). [CrossRef]
9. E. Tajahuerce, V. Durán, P. Clemente, E. Irles, F. Soldevila, P. Andrés, and J. Lancis, “Image transmission through dynamic scattering media by single-pixel photodetection,” Opt. Express 22(14), 16945–16955 (2014). [CrossRef] [PubMed]
12. S. Li, Z. Zhang, X. Ma, and J. Zhong, “Shadow-free single-pixel imaging,” Opt. Commun. 403, 257–261 (2017). [CrossRef]
13. Z. Zhang, S. Liu, J. Peng, M. Yao, G. Zheng, and J. Zhong, “Simultaneous spatial, spectral, and 3D compressive imaging via efficient Fourier single-pixel measurements,” Optica 5(3), 315–319 (2018). [CrossRef]
15. M. Tanha, R. Kheradmand, and S. Ahmadi-Kandjani, “Gray-scale and color optical encryption based on computational ghost imaging,” Appl. Phys. Lett. 101(10), 101108 (2012). [CrossRef]
16. W. Chen and X. Chen, “Ghost imaging for three-dimensional optical security,” Appl. Phys. Lett. 103(22), 221106 (2013). [CrossRef]
17. W. Chen and X. Chen, “Ghost imaging using labyrinth-like phase modulation patterns for high-efficiency and high-security optical encryption,” Europhys. Lett. 109(1), 14001 (2015). [CrossRef]
18. S. Zhao, L. Wang, W. Liang, W. Cheng, and L. Gong, “High performance optical encryption based on computational ghost imaging with QR code and compressive sensing technique,” Opt. Commun. 353, 90–95 (2015). [CrossRef]
19. J. Wu, Z. Xie, Z. Liu, W. Liu, Y. Zhang, and S. Liu, “Multiple-image encryption based on computational ghost imaging,” Opt. Commun. 359, 38–43 (2016). [CrossRef]
20. X. Li, X. Meng, X. Yang, Y. Yin, Y. Wang, X. Peng, W. He, G. Dong, and H. Chen, “Multiple-image encryption based on compressive ghost imaging and coordinate sampling,” IEEE Photonics J. 8(4), 1–11 (2016). [CrossRef]
21. Y. Qin and Y. Zhang, “Information encryption in ghost imaging with customized data container and XOR operation,” IEEE Photonics J. 9(2), 1–8 (2017). [CrossRef]
22. Z. Zhang and J. Zhong, “Single-pixel Broadcast Imaging,” in Computational Optical Sensing and Imaging, 2015 OSA Technical Digest Series (Optical Society of America, 2015), paper CT4F–3.
23. B. Javidi, A. Carnicer, M. Yamaguchi, T. Nomura, E. Pérez-Cabré, M. S. Millán, N. K. Nishchal, R. Torroba, J. F. Barrera, W. He, X. Peng, A. Stern, Y. Rivenson, A. Alfalou, C. Brosseau, C. Guo, J. T. Sheridan, G. Situ, M. Naruse, T. Matsumoto, I. Juvells, E. Tajahuerce, J. Lancis, W. Chen, X. Chen, P. W. H. Pinkse, A. P. Mosk, and A. Markman, “Roadmap on optical security,” J. Opt. 18(8), 083001 (2016). [CrossRef]
27. E. Pérez-Cabré, H. C. Abril, M. S. Millán, and B. Javidi, “Photon-counting double-random-phase encoding for secure image verification and retrieval,” J. Opt. 14(9), 094001 (2012). [CrossRef]
28. T. Nomura and B. Javidi, “Optical encryption using a joint transform correlator architecture,” Opt. Eng. 39(8), 2031–2036 (2000). [CrossRef]
30. O. Katz, Y. Bromberg, and Y. Silberberg, “Compressive ghost imaging,” Appl. Phys. Lett. 95(13), 131110 (2009). [CrossRef]
32. S. Jiao, W. Zou, and X. Li, “Noise Removal for Optical Holographic Encryption from Telecommunication Engineering Perspective,” in Digital Holography and Three-Dimensional Imaging, 2017 OSA Technical Digest Series (Optical Society of America, 2017), paper Th2A–5.
33. S. Jiao, Z. Jin, C. Zhou, W. Zou, and X. Li, “Is QR code an optimal data container in optical encryption systems from an error-correction coding perspective?” J. Opt. Soc. Am. A 35(1), A23–A29 (2018). [CrossRef] [PubMed]
34. N. Radwell, K. J. Mitchell, G. M. Gibson, M. P. Edgar, R. Bowman, and M. J. Padgett, “Single-pixel infrared and visible microscope,” Optica 1(5), 285–289 (2014). [CrossRef]
38. J. Yang, L. Gong, X. Xu, P. Hai, Y. Shen, Y. Suzuki, and L. V. Wang, “Motionless volumetric photoacoustic microscopy with spatially invariant resolution,” Nat. Commun. 8(1), 780 (2017). [CrossRef] [PubMed]
41. S. Liu and J. T. Sheridan, “Optical encryption by combining image scrambling techniques in fractional Fourier domains,” Opt. Commun. 287, 73–80 (2013). [CrossRef]
42. X. F. Meng, L. Z. Cai, X. L. Yang, X. X. Shen, and G. Y. Dong, “Information security system by iterative multiple-phase retrieval and pixel random permutation,” Appl. Opt. 45(14), 3289–3297 (2006). [CrossRef] [PubMed]
44. P. W. M. Tsang, T.-C. Poon, and A. S. M. Jiao, “Embedding intensity image in grid-cross down-sampling (GCD) binary holograms based on block truncation coding,” Opt. Commun. 304, 62–70 (2013). [CrossRef]
45. S. Jiao and P. Tsang, “A hologram watermarking scheme based on scrambling embedding and image inpainting,” in Digital Holography and Three-Dimensional Imaging, 2015 OSA Technical Digest Series (Optical Society of America, 2015), paper DT2A–4.
46. Z. Zhuang, S. Jiao, W. Zou, and X. Li, “Embedding intensity image into a binary hologram with strong noise resistant capability,” Opt. Commun. 403, 245–251 (2017). [CrossRef]
47. N. K. Pareek, V. Patidar, and K. K. Sud, “Image encryption using chaotic logistic map,” Image Vis. Comput. 24(9), 926–934 (2006). [CrossRef]
49. Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: From error visibility to structural similarity,” IEEE Trans. Image Process. 13(4), 600–612 (2004). [CrossRef] [PubMed]