Computational ghost imaging with key-patterns for image encryption

Xu, Cheng; Li, Dekui; Guo, Kai; Yin, Zhiping; Guo, Zhongyi

doi:10.1016/j.optcom.2022.129190

Cited by 17 publications

(3 citation statements)

References 48 publications

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“…Singh et al [10] proposed a grayscale image encryption scheme based on jigsaw transform and chaos, in which the random intensity mask was generated from the chaotic Logistic map. In addition to the DRPE-based and CRPE-based encryption schemes, researchers have also proposed a series of optical image encryption schemes based on digital holography [11,12], computer-generated holography [13,14], phase retrieval algorithm [15], interference [16], joint transform correlator [17], diffractive imaging [18], computational ghost imaging [19], single pixel imaging [20], ptychography [21], integral imaging [22] and deep learning [23].…”

Section: Introductionmentioning

confidence: 99%

Optical voice encryption based on speckle-illuminated fourier ptychography and plaintext-related chaotic random phase mask

Li,

Xu,

Wang

et al. 2024

Phys. Scr.

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In this paper, we propose an optical voice encryption scheme based on speckle-illuminated Fourier ptychography (FP) and plaintext-related chaotic random phase mask (CRPM). In this proposed encryption scheme, the plaintext-related CRPMs are generated by chaotic Lozi map and secure hash algorithm (SHA-256). During the encryption process, the voice signal to be encrypted is first converted into a two-dimensional (2D) voice map. Then, with the help of CRPMs and speckle-illuminated FP, the voice map is encrypted into a series of noise-like low-resolution images. During the decryption process, the original voice signal can be recovered from the series of noise-like low-resolution images via Fourier ptychographic phase retrieval algorithm and the CRPMs. To the best of our knowledge, it is the first time to use the Fourier ptychography and chaotic random phase mask to implement the optical encryption of the voice signal. In addition, in this proposed encryption scheme, the chaotic parameters can replace the whole random phase masks as the secret keys, which makes the management and transmission of the secret keys become more convenient. Moreover, since the plaintext-related chaotic parameter keys can be updated dynamically, the security of the proposed encryption scheme can be further improved. The feasibility, security and robustness of the proposed encryption scheme are further analyzed by numerical simulations.

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Section: Introductionmentioning

confidence: 99%

Optical voice encryption based on speckle-illuminated fourier ptychography and plaintext-related chaotic random phase mask

Li,

Xu,

Wang

et al. 2024

Phys. Scr.

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“…When light passes through a scattering medium, the ballistic light decays rapidly, and target information will be severely corrupted. In order to get imaging results that are as clear as possible, many typical imaging techniques have been proposed, including transmission matrices [8,9], wavefront shaping [10], light storage effects [11,12], and ghost imaging [13][14][15][16]. However, these methods have certain limitations and so do not work better in complex scattering media situations.…”

Section: Introductionmentioning

confidence: 99%

High-Performance Polarization Imaging Reconstruction in Scattering System under Natural Light Conditions with an Improved U-Net

Lin

Fan

et al. 2023

Photonics

Self Cite

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Imaging through scattering media faces great challenges. Object information will be seriously degraded by scattering media, and the final imaging quality will be poor. In order to improve imaging quality, we propose using the transmitting characteristics of an object’s polarization information, to achieve imaging through scattering media under natural light using an improved U-net. In this paper, we choose ground glass as the scattering medium and capture the polarization images of targets through the scattering medium by a polarization camera. Experimental results show that the proposed model can reconstruct target information from highly damaged images, and for the same material object, the trained network model has a superior generalization without considering its structural shapes. Meanwhile, we have also investigated the effect of the distance between the target and the ground glass on the reconstructing performance, in which, and although the mismatch distance between the training set and the testing sample expands to 1 cm, the modified U-net can also efficaciously reconstruct the targets.

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“…In this paper, we introduce a random illuminated pattern into the Improved U-net (IU-net) based PCGI scheme, and the inputs of IU-net are the values obtained by bucket detector after orthonormalization [12,41,42], which can be called as the random-pattern orthonormalization for the IU-net based PCGI (RPO-IU-PCGI). It is worth noting that the random pattern indicates irregular distribution of the pattern, which is generated by a digital micromirror device (DMD) instead of a thermal or pseudo-thermal light.…”

Section: Introductionmentioning

confidence: 99%

High-performance deep-learning based polarization computational ghost imaging with random patterns and orthonormalization

Fan

et al. 2023

Phys. Scr.

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Polarization computational ghost imaging (PCGI) often requires a large number of samples to reconstruct the targets, which can be optimized by reducing sampling rates with the aids of deep-learning technology. In this paper, the random patterns and successive orthonormalization instead of common Hadamard patterns, has been introduced into the deep-learning based PCGI system to recover high-quality images at lower sampling rates. Firstly, we use a polarized light to illuminate the target with random patterns for sampling. Then we can obtain a vector of bucket detector values containing the reflective information of the target. Secondly, we orthonormalize the vector according to the random patterns. Subsequently, the orthonormalized data can be input into the Improved U-net (IU-net) for reconstructing the targets. We demonstrate that higher-quality image of the testing sample can be obtained at a lower sampling rate of 1.5%, and superior-generalization ability for the untrained complex targets can be also achieved at a lower sampling rate of 6%. Meanwhile, we have also investigated the generalization ability of the system for the untrained targets with different materials that have different depolarization properties, and the system still demonstrates superior performances. The proposed method may pave a way towards the real applications of the PCGI.

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Computational ghost imaging with key-patterns for image encryption

Cited by 17 publications

References 48 publications

Optical voice encryption based on speckle-illuminated fourier ptychography and plaintext-related chaotic random phase mask

Optical voice encryption based on speckle-illuminated fourier ptychography and plaintext-related chaotic random phase mask

High-Performance Polarization Imaging Reconstruction in Scattering System under Natural Light Conditions with an Improved U-Net

High-performance deep-learning based polarization computational ghost imaging with random patterns and orthonormalization

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