Speckle visual cryptography for credential authentication.

Based on the high random distribution characteristic of the natural speckle image, a new method of speckle visual cryptography, to the best of our knowledge, is designed by combining the natural speckle image with the secret key in visual cryptography. Specifically, we designed an authentication system for user credentials by combining speckle visual cryptography and the QR code. By using the speckle visual cryptography method, the image of the QR code carrying user authentication information is hidden in the speckle image, and the speckle image is printed on the paper credentials. Through a simulation and analog experiments, we verify the possibility of applying speckle visual cryptography to a user credentials authentication system, compare the improved grayscale reordering algorithm and grayscale reordering algorithm, and prove that the improved grayscale reordering algorithm has more advantages in this system by comparing the PSNR and SSIM. Finally, the y-interference ability and the uniqueness of the virtual secret key in the system are analyzed to prove that the secret key has high anti-interference ability and security.

Optical information hiding for different surface images.

Optical hiding often requires the selection of specific artificial optical components as carriers, which results in poor versatility of the carriers and high costs for the hiding system. To conceal secret information on different surfaces such as metal, wood, and paper, we propose an optical information hiding method. In this method, we use images of surfaces, whose grayscale histograms have the characteristic of symmetric distribution. Based on this characteristic, we first scramble the surface image, and then adjust part of the gray value of the surface image to the complementary value to embed the secret information into a scrambled surface image to generate a key image. In the extraction process, a projector is used to reproduce the scrambled surface image and the key image, which are then incoherently superimposed to extract the secret information using the human visual system. The extraction process does not require complex optical knowledge and is simple and feasible. Simulation experiments and optical experiments indicate that this method is applicable in practice and possesses good security and imperceptibility. Furthermore, we prove the reliability of this method by embedding secret information in different surface images, demonstrating the potential application of more surface images in the field of optical information hiding. Finally, we discuss the applicability of surface information images and analyze the imperceptibility of key images.

SLAM medical imaging enabled by pre-chirp and gain jointly managed Yb-fiber laser.

We demonstrate a pre-chirp and gain jointly managed Yb-fiber laser that drives simultaneous label-free autofluorescence-multiharmonic (SLAM) medical imaging. We show that a gain managed Yb-fiber amplifier produces high-quality compressed pulses when the seeding pulses exhibit proper negative pre-chirp. The resulting laser source can generate 43-MHz, 34-fs pulses centered at 1110 nm with more than 90-nJ energy. We apply this ultrafast source to SLAM imaging of cellular and extracellular components in various human tissues of intestinal adenocarcinoma, lung adenocarcinoma, and liver.

Simulation-driven machine learning approach for high-speed correction of slope-dependent error in coherence scanning interferometry.

Slope-dependent error often occurs in the coherence scanning interferometry (CSI) measurement of functional engineering surfaces with complex geometries. Previous studies have shown that these errors can be corrected through the characterization and phase inversion of the instrument's three-dimensional (3D) surface transfer function. However, since CSI instrument is usually not completely shift-invariant, the 3D surface transfer function characterization and correction must be repeated for different regions of the full field of view, resulting in a long computational process and a reduction of measurement efficiency. In this work, we introduce a machine learning approach based on a deep neural network that is trainable for slope-dependent error correction in CSI. Our method leverages a deep neural network to directly learn errors characteristics from simulated surface measurements provided by a previously validated physics-based virtual CSI method. The experimental results demonstrate that the trained network is capable of correcting the surface height map with 1024 × 1024 sampling points within 0.1 seconds, covering a 178 µm field of view. The accuracy is comparable to the previous phase inversion approach while the new method is two orders of magnitude faster under the same computational condition.

Low-dose imaging denoising with one pair of noisy images.

Low-dose imaging techniques have many important applications in diverse fields, from biological engineering to materials science. Samples can be protected from phototoxicity or radiation-induced damage using low-dose illumination. However, imaging under a low-dose condition is dominated by Poisson noise and additive Gaussian noise, which seriously affects the imaging quality, such as signal-to-noise ratio, contrast, and resolution. In this work, we demonstrate a low-dose imaging denoising method that incorporates the noise statistical model into a deep neural network. One pair of noisy images is used instead of clear target labels and the parameters of the network are optimized by the noise statistical model. The proposed method is evaluated using simulation data of the optical microscope, and scanning transmission electron microscope under different low-dose illumination conditions. In order to capture two noisy measurements of the same information in a dynamic process, we built an optical microscope that is capable of capturing a pair of images with independent and identically distributed noises in one shot. A biological dynamic process under low-dose condition imaging is performed and reconstructed with the proposed method. We experimentally demonstrate that the proposed method is effective on an optical microscope, fluorescence microscope, and scanning transmission electron microscope, and show that the reconstructed images are improved in terms of signal-to-noise ratio and spatial resolution. We believe that the proposed method could be applied to a wide range of low-dose imaging systems from biological to material science.

Three-dimensional phase and intensity reconstruction from coherent modulation imaging measurements.

Coherent modulation imaging is a lensless imaging technique, where a complex-valued image can be recovered from a single diffraction pattern using the iterative algorithm. Although mostly applied in two dimensions, it can be tomographically combined to produce three-dimensional (3D) images. Here we present a 3D reconstruction procedure for the sample's phase and intensity from coherent modulation imaging measurements. Pre-processing methods to remove illumination probe, inherent ambiguities in phase reconstruction results, and intensity fluctuation are given. With the projections extracted by our method, standard tomographic reconstruction frameworks can be used to recover accurate quantitative 3D phase and intensity images. Numerical simulations and optical experiments validate our method.

Cryptanalysis of an optical cryptosystem with uncertainty quantification in a probabilistic model.

In this paper, a modified probabilistic deep learning method is proposed to attack the double random phase encryption by modeling the conditional distribution of plaintext. The well-trained probabilistic model gives both predictions of plaintext and uncertainty quantification, the latter of which is first introduced to optical cryptanalysis. Predictions of the model are close to real plaintexts, showing the success of the proposed model. Uncertainty quantification reveals the level of reliability of each pixel in the prediction of plaintext without ground truth. Subsequent simulation experiments demonstrate that uncertainty quantification can effectively identify poor-quality predictions to avoid the risk of unreliability from deep learning models.

Coherent modulation imaging using a physics-driven neural network.

Coherent modulation imaging (CMI) is a lessness diffraction imaging technique, which uses an iterative algorithm to reconstruct a complex field from a single intensity diffraction pattern. Deep learning as a powerful optimization method can be used to solve highly ill-conditioned problems, including complex field phase retrieval. In this study, a physics-driven neural network for CMI is developed, termed CMINet, to reconstruct the complex-valued object from a single diffraction pattern. The developed approach optimizes the network's weights by a customized physical-model-based loss function, instead of using any ground truth of the reconstructed object for training beforehand. Simulation experiment results show that the developed CMINet has a high reconstruction quality with less noise and robustness to physical parameters. Besides, a trained CMINet can be used to reconstruct a dynamic process with a fast speed instead of iterations frame-by-frame. The biological experiment results show that CMINet can reconstruct high-quality amplitude and phase images with more sharp details, which is practical for biological imaging applications.

Resolution-enhanced ptychography framework with an equivalent upsampling and precise position.

As a lensless imaging technique, ptychography provides a new way to resolve the conflict between the spatial resolution and the field of view. However, due to the pixel size limit of the sensor, a compromise has to be reached between the spatial resolution and the signal-to-noise ratio. Here, we propose a resolution-enhanced ptychography framework with equivalent upsampling and subpixel accuracy in position to further improve the resolution of ptychography. According to the theory of pixel superresolved techniques, the inherent shift illumination scheme in ptychography can additionally enhance the resolution with the redundant data. An additional layer of pooling is used to simulate the downsampling of a digital record, and the pixel superresolved problem is transformed into an automatic optimization problem. The proposed framework is verified by optical experiments, both in biological samples and the resolution targets. Compared to the traditional algorithm, the spatial lateral resolution is twice as large using the same data set.

Enlarged range of measurement method with strong noise resistance for dual-wavelength digital holography: erratum.

We present an erratum to our Letter [Opt. Lett.46, 4694 (2021)10.1364/OL.432135]. This erratum corrects an unintended error in the labels of Figs. 5(h)-5(j). The corrections have no influence on the results and conclusions of the original Letter.

Spatiotemporal coherent modulation imaging for dynamic quantitative phase and amplitude microscopy.

The single-shot capability of coherent modulation imaging (CMI) makes it have great potential in the investigation of dynamic processes. Its main disadvantage is the relatively low signal-to-noise ratio (SNR) which affects the spatial resolution and reconstruction accuracy. Here, we propose the improvement of a general spatiotemporal CMI method for imaging of dynamic processes. By making use of the redundant information in time-series reconstructions, the spatiotemporal CMI can achieve robust and fast reconstruction with higher SNR and spatial resolution. The method is validated by numerical simulations and optical experiments. We combine the CMI module with an optical microscope to achieve quantitative phase and amplitude reconstruction of dynamic biological processes. With the reconstructed complex field, we also demonstrate the 3D digital refocusing ability of the CMI microscope. With further development, we expect the spatiotemporal CMI method can be applied to study a range of dynamic phenomena.

Dynamic coherent diffractive imaging with a physics-driven untrained learning method.

Reconstruction of a complex field from one single diffraction measurement remains a challenging task among the community of coherent diffraction imaging (CDI). Conventional iterative algorithms are time-consuming and struggle to converge to a feasible solution because of the inherent ambiguities. Recently, deep-learning-based methods have shown considerable success in computational imaging, but they require large amounts of training data that in many cases are difficult to obtain. Here, we introduce a physics-driven untrained learning method, termed Deep CDI, which addresses the above problem and can image a dynamic process with high confidence and fast reconstruction. Without any labeled data for pretraining, the Deep CDI can reconstruct a complex-valued object from a single diffraction pattern by combining a conventional artificial neural network with a real-world physical imaging model. To our knowledge, we are the first to demonstrate that the support region constraint, which is widely used in the iteration-algorithm-based method, can be utilized for loss calculation. The loss calculated from support constraint and free propagation constraint are summed up to optimize the network's weights. As a proof of principle, numerical simulations and optical experiments on a static sample are carried out to demonstrate the feasibility of our method. We then continuously collect 3600 diffraction patterns and demonstrate that our method can predict the dynamic process with an average reconstruction speed of 228 frames per second (FPS) using only a fraction of the diffraction data to train the weights.

Enlarged range of measurement method with strong noise resistance for dual-wavelength digital holography.

A concise and powerful method for dual-wavelength digital holography (DWDH) is proposed. By designing a new algorithm, this proposed method bypasses the phase synthesis process and directly obtains the thickness distribution of the object. This method can enlarge the range of measurement with strong noise resistance. For example, noise analysis results show that the proposed method reduces the reconstruction error from 101 nm to 9 nm when the signal-to-noise ratio is equal to 30. Therefore, this method would prove useful for DWDH, and its effectiveness is verified by both numerical simulations and experimental results.

Optical image hiding based on spectrum encoding with structured illumination.

Structured illumination microscopy (SIM) is combined with optical image hiding for the first time, to the best of our knowledge. In a linear phase encoding system, secret information might be divulged with the input related to the correct image. In this paper, we propose an optical hiding method in which the concept of SIM is used to create reconstructed host images with an extended spectrum. This method not only improves the security of the image hiding system, but also creates a new perspective for optical image hiding and makes solutions for the defect of the linear phase encoding system.

Natural speckle-based watermarking with random-like illuminated decoding.

We propose an optical watermarking method based on a natural speckle pattern. In the watermarking process, the watermark information is embedded into the natural speckle pattern. Then the random-like watermarked image is generated with the proposed grayscale reordering algorithm. During the extraction procedure, the watermarked image is projected to the natural speckle pattern as illumination. Subsequently, they are incoherently superimposed to extract the watermark information directly by human vision. Optical experiments and a hypothesis test are conducted to demonstrate the proposed method with high reliability, imperceptibility and robustness. The proposed method is the first watermarking method utilizing the natural diffuser as the core element in encoding and decoding.

Visual-cryptographic image hiding with holographic optical elements.

In this paper, we propose a visual-cryptographic image hiding method based on visual cryptography (VC) and volume grating optics. The secret image is converted to the encrypted visual keys (VKs) according to the normal VC algorithm. Then the VKs are further embellished to the QR-code-like appearance and hidden as the holographic optical elements (HOEs), which are fabricated by the holographic exposure of photopolymer. In the decryption process, the fabricated HOE-VKs are illuminated with the laser beam. The reconstructed VKs are overlaid to extract the hidden information directly in the optical facility, without additional computation. Optical experiments verify that the VKs in HOE mode improves the system with high security and good robustness on the random noise attack. Besides, the volume grating nature also enlarges the system bandwidth by using the multiplexing technique. The proposed method may provide a promising potential for the practical image hiding systems.

Optical watermarking based on single-shot-ptychography encoding.

A novel optical encoding method based on single-shot ptychography is proposed for the application of optical watermarking. For the inherent properties of single-shot ptychography, the watermark is encoded into a series of tiny diffraction spots just in one exposure. Those tiny spots have high imperceptibility and compressibility, which are quite suitable for the optical watermarking application. The security of the proposed watermarking is mainly supported by the strong imperceptibility, as well as the introduction of compression encoding and scrambling encoding. In addition, the diversity of the multi-pinhole array and the structural parameters can also be served as security keys. Both numerical simulation and optical experiment demonstrate the high security and the easy implementation of the single-shot-ptychography-based optical watermarking. Further, the compression encoding can largely improve the embedding capacity that enables the multiple-watermarking for more transmitted information and higher security.

Security risk of diffractive-imaging-based optical cryptosystem.

The well-known diffractive-imaging-based optical cryptosystem is breached in the paper. The decryption key of the system can be easily accessed by the opponent by using a new type of powerful phase retrieval method. Our result, to our best knowledge, is the first work to show the security risk of the diffractive-image cryptosystem. Meanwhile, we provide a set of numerical simulations to demonstrate the feasibility and robustness of the presented method.

Analytic known-plaintext attack on a phase-shifting interferometry-based cryptosystem.

We demonstrate a new analytic approach to a known-plaintext attack (KPA) on an optical cryptosystem based on the phase-shifting interferometry (PSI) technique. With the proposed analytic attack method, an opponent can access the exact decryption keys and obtain perfect attack results. This demonstration, to the best of our knowledge, shows for the first time that the optical cryptosystem based on the PSI technique is vulnerable to KPA.

Optical encryption of unlimited-size images based on ptychographic scanning digital holography.

The ptychographic scanning operation is introduced into digital holography to expand the field-of-view (FOV). An optical image encryption method based on this technique is further proposed and analyzed. The plaintext is moved sequentially in the way of ptychographic scanning and corresponding pairs of phase-shifted interferograms are recorded as ciphertexts. Then the holographic processing and the ptychographic iterative reconstruction are both employed to retrieve the plaintext. Numerical experiments demonstrate that the proposed system possesses high security level and wide FOV. The proposed method might also be used for other potential applications, such as three-dimensional information encryption and image hiding.