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l A multiple-image encryption method based on compressed coded aperture imaging, compressive sensing and pixel location scrambling is proposed. l It decreases the redundancy of the key and improves the calculation speed of the reconstruction. l Only one main original random matrix key is utilized, thus the encryption efficiency is greatly raised. A multiple-image encryption method based on compressed coded aperture imaging and pixel location scrambling operation is proposed. In the encryption process, each plaintext image is compressively encoded by its corresponding random matrix, which is generated via a row and column displacement transformation operation on an original random matrix (ORM) and some auxiliary keys. With the help of charge coupled device (CCD), the superimposed image are then generated by recording and grouping all the encoded images; finally, the ciphertext image is obtained by pixel location scrambling operation for the superimposed image. During decryption, the participant who possesses his/her correct key-group, can successfully reconstruct the corresponding plaintext image by location index key, auxiliary keys, scrambling operation, compressed coded aperture imaging retrieval algorithm. Theoretical analysis and numerical simulations validate the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

A multiple-image encryption method via lifting wavelet transform (LWT) and XOR operation is proposed, which is based on a row scanning compressive ghost imaging scheme. In the encryption process, the scrambling operation is implemented for the sparse images transformed by LWT, then the XOR operation is performed on the scrambled images, and the resulting XOR images are compressed in the row scanning compressive ghost imaging, through which the ciphertext images can be detected by bucket detector arrays. During decryption, the participant who possesses his/her correct key-group, can successfully reconstruct the corresponding plaintext image by measurement key regeneration, compression algorithm reconstruction, XOR operation, sparse images recovery, and inverse LWT (iLWT). Theoretical analysis and numerical simulations validate the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

• Considering the additional information from the degree of polarization (DoP), the polarimetric dehazing method is improved with the fusion of the intensity and the DoP. • The wavelet transform is exploited to make fusion, and different fusion rules are designed for the low-frequency and the high-frequency, respectively. • The introduction of DoP enriches the detailed information of the dehazed image, especially for the transparent object that is hard to be handled with previous methods. Image dehazing is an indispensable approach to address the image degeneration in hazy environment. In recent years, polarimetric methods have been widely investigated for image dehazing. In the basic algorithm of polarimetric dehazing, in addition to the dehazed intensity, the degree of polarization (DoP) can also be reconstructed with the assistance of Stokes parameters. Considering the additional information from the DoP, the polarimetric dehazing method is improved with the fusion of the intensity and the DoP. The wavelet transform is exploited to make fusion, and different fusion rules are designed for the low-frequency and the high-frequency components, respectively. The proposed method can improve the quality of polarimetric dehazed image, especially for the transparent object that is hard to be handled with previous methods. The validity of the proposed method is demonstrated in experiments. [ABSTRACT FROM AUTHOR]

• A compressive ghost imaging method based on multiple image prior information is proposed. • By introducing two prior image information, which named smoothness and nonlocal self- similarity, the image reconstruction quality is higher at low sampling ratios. • By utilizing singular value decomposition and compressed sensing, high fidelity and high-quality image reconstruction was achieved at low sampling ratios. Ghost imaging, which utilizes structured illumination to reconstruct two-dimensional information of objects from one-dimensional bucket signals, has garnered significant attention. However, before introducing compressed sensing, how to obtain high fidelity images at low sampling ratios has always been a major challenge. Previous work on compressive ghost imaging lacks the incorporation of prior information into the image reconstruction process, leading to significant distortions in the reconstructed images at low sampling ratios. In this paper, to enhance the imaging quality of ghost imaging at low sampling ratios, we introduce two prior image information regarding smoothness and nonlocal self-similarity based on singular value decomposition compressive ghost imaging and propose a novel compressive ghost imaging. The scientific validity and feasibility of the proposed method are demonstrated both theoretically and experimentally. Furthermore, comparative analyses with other classical algorithms reveal significant advantages of our method in handling image smoothness and nonlocal self-similarity. Even at lower sampling ratios, the proposed method demonstrates impressive reconstruction capabilities, resulting in reconstructed images closely resembling the originals. Both global and local details of the images are effectively reconstructed, enhancing the fidelity of the reconstructed images. [ABSTRACT FROM AUTHOR]

• We scrutinize the Fourier-domain shower-curtain effect (FDSE)-related noise model, which makes it possible to simulate adequate training data to learn FDSE correlography problems. • Without knowing the experimental scene, the generated convolutional neural network (CNN) is computationally efficient and extremely robust to various forms of noise, far exceeding the capabilities of existing algorithms. • The targets can be reconstructed from a standard sCMOS detector with a 150 ms exposure. Optical experiments have proved that in dynamic turbid media, the real-time high-resolution imaging with a single lens is possible. Low signal-to-noise ratio (SNR) measurement is conceivable the primary obstruction to real-time, high-resolution through dynamic turbid media optical imaging. To break this restriction, by individualizing and employing these low SNR measurement data, the spectrum estimation theory is procured a noise model for scatter imaging. The noise model proposed is exploited to synthesize data set training to settle the related problems of noise phase without knowing the experimental scenes. We verify the robustness of the resulting deep correlography method to noise, outdistance the capabilities of the existing Fourier-domain shower-curtain effect (FDSE) system in terms of spatial resolution and total acquisition time, in addition, the targets can be reconstructed from a standard sCMOS detector with a 150 ms exposure. [ABSTRACT FROM AUTHOR]

• A ciphertext-only attack method relys on the statistical correlation properties of speckles, revealing that the statistical average of the Fourier transform intensity of the ciphertext sub-blocks is essentially the same as the autocorrelation of the plaintext itself is proposed. • Characterize and use the ciphertext information, and use the spectrum estimation theory to derive the autocorrelation characteristics of the ciphertext information. • With the process of CNN, this scheme has higher robustness and high fidelity than traditional phase retrieval algorithms. Security analysis is crucial and indispensable to authenticate the performance of cryptographic systems. The security risks of the spatial incoherent optical cryptography system are evaluated from the perspective of scattering medium imaging, and it proves that the system is vulnerable to the pure ciphertext attacks. The proposed ciphertext-only attack method relies on the statistical correlation properties of speckles, revealing that the statistical average of the Fourier transform intensity of the ciphertext sub-blocks is essentially the same as the autocorrelation of the plaintext itself. To better model, characterize and utilize the ciphertext information, the autocorrelation is derived by using the spectral estimation theory. Then, using only the synthetic data sampled from the noise model, without knowing the plaintext, a deep convolutional neural network (CNN) is trained to conquer the noisy phase retrieval problem associated with correlography. The resulting deep-inverse correlography approach is exceptionally robust to noise, with only 4% of the ciphertext clue, the plaintext information can still be retrieved. Both the theory analysis and the experiment results validate the feasibility of the proposed ciphertext-only attack method. [ABSTRACT FROM AUTHOR]

• An optical color watermarking scheme based on single-pixel imaging and singular value decomposition in invariant wavelet domain is proposed. • Bayer array is introduced into full color single-pixel imaging and the amount of data needs to be stored is reduced to one third. • The construction of invariant wavelet domain and principle components embedding based on singular value decomposition provide our watermarking scheme higher robustness and security. A blind optical color watermark scheme is proposed with single-pixel imaging (SPI), redistributed invariant lifting wavelet transform (RILWT) and singular value decomposition (SVD), in which, the watermark is first encrypted by the SPI and then embedded into host image with SVD in redistributed invariant wavelet domain. Bayer array is introduced into SPI to complete full color imaging of target scene (watermark) and the amount of data needs to be stored is reduced to one third. The construction of RILWT and principle components embedding based on SVD provide our watermark scheme high robustness to various kinds of attacks. In addition, the watermark scheme has large capacity, good imperceptibility and no original host image is needed when extracting the watermark. The theory analysis and optical experimental results have demonstrated the good performance and high robustness of the proposed method. [ABSTRACT FROM AUTHOR]

Highlights • The optical image watermarking based on singular value decomposition ghost imaging and lifting wavelet transform is proposed. • It realizes good imperceptibility and robustness, and no original host image is needed when extracting the watermark. • Singular value decomposition ghost imaging has made the reconstruction process with less time and higher quality, and thus the reconstruction of the watermark image shows good performance. Abstract An optical image watermarking method based on singular value decomposition ghost imaging (SVDGI) and a blind watermarking algorithm is proposed. The watermark image is first encrypted by an SVDGI system, then the encrypted watermark is embedded in the host image with the help of the watermark algorithm, which uses the block discrete cosine transform (DCT) and integer lifting wavelet transform (LWT). The proposed watermarking algorithm has good imperceptibility and robustness, and no original host image is needed when extracting the watermark. SVDGI provides a new reconstruction method in ghost imaging (GI) by modifying the measurement matrix with the help of singular value decomposition, which carries out the reconstruction process in less time and at higher quality. The measurement matrix composes a secret key, and only with this key can authorized users decrypt and reconstruct the original watermark image. Both optical experiments and numerical simulations validate the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

A multilevel image authentication method is proposed which is based on row scanning compressive ghost imaging and a hyperplane secret sharing algorithm. In the image encoding process, after the wavelet transform and Arnold transform for the certification image, through row scanning compressive ghost imaging, the ciphertext matrix can be first detected by a bucket detector (BD). Based on a hyperplane secret sharing algorithm, the measurement key using in the row scanning compressive ghost imaging, can be decomposed and shared into n subkeys, which are then distributed to n different participants. In the high-level authentication process, based on a hyperplane secret sharing algorithm and a compressive reconstruction algorithm, any t or more participants with the corresponding correct subkeys can be gathered to reconstruct the original meaningful certification image with high correlation coefficient (CC); While in the case of low-level authentication process, only one authenticator who possesses a correct subkey will gain no significant information of certification image, however, it can result in a remarkable peak output in the nonlinear correlation coefficient distribution. Theoretical analysis and numerical simulations both verify the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

A new kind of hierarchical multiple-image encryption method based on the cascaded interference structure and vector stochastic decomposition algorithm is proposed. In this method, by using the nonequal modulus decomposition and vector stochastic decomposition algorithm, the K th-level secret image modulated by a random phase distribution is analytically encoded into a K th-level phase-only mask (POM) key and the ( K −1)th-level complex amplitude field whose real amplitude is the ( K −1)th-level secret image. Then, served as the input condition, the generated complex amplitude field is further encoded into a ( K −1)th-level POM key and the ( K −2)th-level complex amplitude, and so on, until the 1st-level complex amplitude field is decomposed into two POMs: one is the 1st-level key, and the other is the ciphertext. When decryption occurs, only when the high-level users simultaneously obtain all the phase keys, the correct sequential orders of phase keys, and the correct geometrical parameters, all the secret images are retrieved successfully. Both theoretical analysis and numerical simulations verify the feasibility of this method. [ABSTRACT FROM AUTHOR]

A two-level image authentication method is proposed; the method is based on two-step phase-shifting interferometry, double random phase encoding, and compressive sensing (CS) theory, by which the certification image can be encoded into two interferograms. Through discrete wavelet transform (DWT), sparseness processing, Arnold transform, and data compression, two compressed signals can be generated and delivered to two different participants of the authentication system. Only the participant who possesses the first compressed signal attempts to pass the low-level authentication. The application of Orthogonal Match Pursuit CS algorithm reconstruction, inverse Arnold transform, inverse DWT, two-step phase-shifting wavefront reconstruction, and inverse Fresnel transform can result in the output of a remarkable peak in the central location of the nonlinear correlation coefficient distributions of the recovered image and the standard certification image. Then, the other participant, who possesses the second compressed signal, is authorized to carry out the high-level authentication. Therefore, both compressed signals are collected to reconstruct the original meaningful certification image with a high correlation coefficient. Theoretical analysis and numerical simulations verify the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

The highly c-axis oriented La 1.9 Ce 0.1 CuO 4 (LCCO) film was prepared on SrTiO 3 (STO) substrate by pulsed-laser deposition (PLD). The identification and characterization of the LCCO film was determined by X-ray diffraction (XRD), atomic force microscopy (AFM), scanning electron microscope (SEM) and X-ray photoelectron spectroscopy (XPS). Spectroscopic ellipsometry (SE) measurement was performed on the LCCO film in 0.5–4.13 eV range. The complex pseudodielectric function of the LCCO film was reported by Drude and Lorentz oscillators in this work. Critical point analysis on third-derivative spectra of the Lorentz contributions was accomplished to obtain the interband transition energies. The results indicate that the interband transitions are mainly related to the copper-oxygen planes. The metallic behavior of the pseudodielectric function, which is a common feature in all known electron-doped superconductors, can be obviously observed in the LCCO film. [ABSTRACT FROM AUTHOR]

A multiple-image encryption method is proposed that is based on row scanning compressive ghost imaging, ( t , n ) threshold secret sharing, and phase retrieval in the Fresnel domain. In the encryption process, after wavelet transform and Arnold transform of the target image, the ciphertext matrix can be first detected using a bucket detector. Based on a ( t , n ) threshold secret sharing algorithm, the measurement key used in the row scanning compressive ghost imaging can be decomposed and shared into two pairs of sub-keys, which are then reconstructed using two phase-only mask (POM) keys with fixed pixel values, placed in the input plane and transform plane 2 of the phase retrieval scheme, respectively; and the other POM key in the transform plane 1 can be generated and updated by the iterative encoding of each plaintext image. In each iteration, the target image acts as the input amplitude constraint in the input plane. During decryption, each plaintext image possessing all the correct keys can be successfully decrypted by measurement key regeneration, compression algorithm reconstruction, inverse wavelet transformation, and Fresnel transformation. Theoretical analysis and numerical simulations both verify the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

A hierarchical multilevel authentication system for multiple-image based on phase retrieval and basic vector operations in the Fresnel domain is proposed, by which more certification images are iteratively encoded into multiple cascaded phase masks according to different hierarchical levels. Based on the secret sharing algorithm by basic vector decomposition and composition operations, the iterated phase distributions are split into n pairs of shadow images keys (SIKs), and then distributed to n different participants (the authenticators). During each level in the high authentication process, any 2 or more participants can be gathered to reconstruct the original meaningful certification images. While in the case of each level in the low authentication process, only one authenticator who possesses a correct pair of SIKs, will gain no significant information of certification image; however, it can result in a remarkable peak output in the nonlinear correlation coefficient of the recovered image and the standard certification image, which can successfully provide an additional authentication layer for the high-level authentication. Theoretical analysis and numerical simulations both verify the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

• A demosaicing method for compressive color single-pixel imaging based on a generative adversarial network is proposed. • Bayer array is introduced into full color single-pixel imaging and the amount of data needs to be stored is reduced to one third. Then we utilize a generative adversarial network for demosaicing and achieve a better reconstruction result at lower sampling ratios compared with some state-of the-art demosaising mothods. In conventional full color single-pixel imaging (SPI), multiple times measurement or multiple detectors are required to capture all channels' information of a color scene. The introduction of color filter array (CFA) provides a new idea for full color SPI and the sampling ratio is reduced to one third. However, the imaging quality and color fidelity largely depend on the demosaicing algorithm and when the sampling ratio decreases, the demosaicing result gets worse sharply or even totally fails. In this paper, we utilize a generative adversarial network (GAN) to implement color recovery specific for compressively sampled mosaic images reconstructed in SPI. Deep-learning based demosaicing method helps restore color SPI with higher imaging quality and color fidelity at lower sampling ratios compared with some state-of-the-art demosaicing algorithms. Both computer simulation and optical experiment have demonstrated the effectiveness and feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract: We have proposed a full-phase image encryption method based on double random-phase encoding in Fresnel domain and pixel random permutation (PRP) technique with the use of two-step phase-shifting interferometry (PSI) we reported recently, and verified the effectiveness of this method and its robustness against occlusion and noise attacks by a series of numerical simulations. Comparing with other similar methods, this approach can decrease the number of the interferograms to be delivered from at least three needed in ordinary PSI to only two, give much better performance of image reconstruction than amplitude-based encryption, and provide much higher security level. This method is usable for both the gray-level images and binary images, and usually the latter will lead to better results. [Copyright &y& Elsevier]

• An optical multilevel authentication based on singular value decomposition ghost imaging and (t, n) secret sharing scheme is proposed. • It realizes different levels of authentication to the certificated standard image with the same authentication system. • Singular value decomposition ghost imaging has made the reconstruction process with less time and higher quality. An optical multilevel authentication method is proposed that is based on the singular value decomposition ghost imaging (SVDGI) and (t, n) threshold secret sharing scheme. SVDGI provides an efficient reconstruction method in ghost imaging (GI) by modifying the measurement matrix with the help of singular value decomposition, which enables the reconstruction of an N -pixel image utilizing much less than N measurements with less time and a higher quality. On the other hand, the (t, n) threshold secret sharing algorithm guarantees the multilevel authentication system, in which, the modified measurement matrix is grouped into n parts as n keys and distributed to n different participants, any t (t ≤ n) or more of authorized participants with their correct keys can be collected to restore the original meaningful certification image, which is so called the high-level authentication; While in the case of low-level authentication, any t - 1 or fewer will gain no exact information of the certification image, but a remarkable peak output can be acquired in the 3D distribution of the nonlinear correlation coefficient of the standard certification image and the recovered image. So that the method successfully provides a multilevel authentication. Both numerical simulations and optical experiments validate the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

• An optical color image encryption based on Hadamard single-pixel imaging and Arnold transformation is proposed. • Due to the orthogonality of Hadamard matrix, Hadamard single-pixel imaging can achieve high quality imaging even undersampling. • A coefficient matrix key is used to control both the quantity information of the color components and the parameters of the Arnold transformation, which increases the security of the optical cryptosystem. An optical color image encryption method based on Hadamard single-pixel imaging (SPI) and Arnold transformation is proposed, in which, optical full-color image encryption with good imaging quality and high security performance can be accomplished by only one bucket detector (BD). In the encryption process, after controlling and modulating the Hadamard matrix by the coefficient matrix key, three different sets of colored Hadamard patterns are generated to be projected onto the target colored object; Subsequently, the collected experimental data: R, G, and B components are detected by a BD, which are then scrambled by Arnold transformation to further increase the security; Finally the colored ciphertext image can be recombined and obtained to transferring to the receivers. During the decryption, with the colored ciphertext and all the corrected keys, the original secret color image can be successfully decrypted by the extraction of R, G, B components, inverse Arnold transformation, the correlated computation in SPI, and inverse Hadamard transformation. The feasibility of this method and its robustness against some types of attacks are verified by both computer simulations and optical experiments. [ABSTRACT FROM AUTHOR]

• A two-step phase-shifting algorithm dedicated to fringe projection profilometry (FPP) is proposed. • Considering the physical process of FPP, the captured fringe image is formulated with two independent variables. • The phase error due to ambient light is analyzed via a line-circle model. • The wrapped phase is refined based on the estimation of the actual fringe contrast. Improving the time efficiency of fringe projection profilometry (FPP) is an attractive problem. For FPP using phase-shifting, it is desired to improve the efficiency by reducing the step number for phase retrieval. This paper proposes a two-step phase-shifting algorithm dedicated to FPP. Considering the physical process of FPP, the captured fringe image is formulated with two variables, i.e. surface reflectance and phase value. And a phase shift is introduced to get the two equations, which lead to the close-form solution for phase calculation. Then the phase error due to ambient light is analyzed via a line-circle model, and an algorithm of refining the phase calculation is proposed based on the estimation of the actual fringe contrast. The validity of the proposed approach is demonstrated with experiments. [ABSTRACT FROM AUTHOR]