Among the various methods for imaging through scattering media, the speckle autocorrelation imaging method based on optical memory effect (OME) has gained significant attention for its non-invasive single-shot imaging capability through scatter media. However, the imaging range of this method is limited by OME. This paper presents a physics-informed deep learning strategy that establishes relationships among different linearly-shift-invariant subsystems based on OME to explore the range of OME range. By leveraging the feature extraction capabilities of deep learning, the proposed approach recovers sidelobes from the speckle autocorrelation patterns of objects in several OME regions. Then, a phase retrieval algorithm is employed to achieve object reconstruction. In the future, the approach can be extended to integrate different small regions of the object plane into a big one which can be linear shift invariant.
Optical imaging through complex, inhomogeneous media is a long-standing challenge due to the presence of scattered light, which causes a degradation of resolution. Thanks to the discovery of the optical memory effect (OME), a variety of novel and breakthrough approaches have been proposed to find the hidden information behind the scattering media. However, those ME-based methods usually suffer from a limited field-of-view (FOV). An object of size beyond the ME region cannot be reconstructed completely and will produce artifacts. Here, we propose a non-invasive method that exploits a moving object behind the scattering media to extend the FOV
Optical cryptosystem based on phase-truncated-Fourier-transforms (PTFT) is one of the most interesting optical cryptographic schemes due to its unique mechanism of encryption/decryption. Conventional learning-based attack method need a large number of plaintext-ciphertext pairs to train a neural network and then predict the plaintexts from subsequent ciphertexts. In this work, we propose an alternative method of attack on PTFT-based optical asymmetric cryptosystem by using an untrained neural network. We optimize the parameters of a neural network with the help of the encryption model of PTFT-based cryptosystem, hoping to get the ability of retrieving any plaintext from the corresponding unknown ciphertext but without help of the decryption keys. The proposed untrained-neural-network-based attack approach eliminates the requirement of tens of thousands of training images and might open up a new avenue for optical cryptanalysis.
Scattering light imaging technique has attracted extensive research because of its huge potential in the fields of biomedical microscopy, remote-sensing mapping etc. For most methods now available to reconstruct an object hidden behind scattering media, the main focus is on reconstructing the shape of the object without considering its spectral information. While imaging a color object, it is often necessary to measure a series of Point Spread Functions (PSFs) or Wavelength-Dependent Speckle Patterns (WDSPs) under various wavelengths of illumination. It’s obvious that these methods are either invasive to the object or require multiple exposures. Here, by taking advantage of the Wavelength- Dependent Response Characteristics (WDRC) of the Liquid Crystal Spatial Light Modulator (LC-SLM), we propose an alternative way to reconstruct a hidden color object with noninvasive and single-exposure strategy. A monochromatic camera is adopted to capture the wavelength-multiplexing gray-scale speckle pattern, which can be then demultiplexed into a number of WDSPs by utilizing of a designed Multi-modal Phase Retrieval Algorithm (MM-PRA). Then, a typical speckle correlation technique (SCT) is applied to reconstruct each component of the hidden color object. The feasibility and effectiveness of the proposed method are demonstrated by numerical results in this work while the optical experiments are on the way.
Deconvolution-based techniques have been widely used for imaging through scattering medium due to the optical memory effect (OME) in speckles. Once the point spread function (PSF) of a scattering system is measured, a smallscale object within the OME region can be easily recovered. However, an extended object larger than the OME region can only be partially reconstructed due to the limited field of view (FOV). Here, we find a way to get an integrated PSF by exploiting a point source with different locations in object plane. Thereafter, an extended object, within the FOV but exceeding the OME region, could be recovered by the integrated PSF without knowing any other system parameters even the locations of the point source.
Many methods have been demonstrated that it is possible to reconstruct an object hidden scattering layers. However, it is still a big challenge when suffer from dynamic and/or time-variant scattering media. Speckle correlation is a breakthrough technique which can noninvasively retrieve the image of object from a single-shot captured pattern but it does not allow for imaging in real time as the complicated iteration process. Recently, deep learning has attracted great attention in scattering imaging but they usually employ end-to-end mode so that the scattering medium must be fixed during the training and testing process. Here, we develop a two-step deep learning strategy for imaging through moving scattering layers. In our proposed scheme, speckle autocorrelation de-noising and object image reconstruction from autocorrelation are trained respectively by using two convolution neural network. Optical experiments show that our proposed scheme has outstanding performance for real-time imaging through moving scattering layers.
An approach for constructing optical hash function has been proposed based on the interaction between multiple scattering media and coherent radiation. Unlike the traditional Hash function via mathematical transformations or complex logic operations, the proposed method employs a multiple scattering media and Sobel filters for data scrambling and features extraction. An arbitrary length input data can be compressed into a fixed length (256-bit) Hash value after a cascade iterative processing. Its safety relies on the unpredicted and non-duplicated disorder multiple scattering media, in other word, there is tremendous difficulty of knowing the multiple scatting media with a specific internal state or efficiently simulating the light interaction effect between the multiple scattering media. Simulation results are presented to demonstrate the avalanche effect and collision resistance performance of the proposed designing strategy of the optical Hash function.
Random-phase-based optical image encryption techniques have drawn a lot of attention in recent years. However, in this contribution those schemes have been demonstrated to be vulnerable to chosen-plaintext attack (CPA) by employing the deep learning strategy. Specifically, by optimizing the parameters, the chosen deep neural network (DNN) can be trained to learn the sensing of an optical cryptosystem and thus get the ability to reconstruct any plaintext image from its corresponding ciphertext. A set of numerical simulation results have been further provided to shown its ability on cracking not only the classical double random phase encryption (DRPE), but also the tripe random-phase encryption (TRPE).
The depth-of-field (DOF) characteristic of the imaging system with scattering medium is analyzed based on the analytical model of ambiguity function as a polar display of the optical transfer function (OTF) in this paper. It is indicated that the scattering medium can help re-collect more high spatial frequencies, which are normally lost with defocusing in traditional imaging systems. Therefore, the scattering medium can be considered not as an obstacle for imaging but as a useful tool to extend the DOF of the imaging system. To test the imaging properties and limitations, we performed optical experiments in a single-lens imaging system.
We address a non-invasive imaging method to observe dynamic objects hidden behind a turbid medium. An initial image of the objects is first recovered by speckle correlation technique (SCT) with a single shot speckle pattern. The scattered point spread function (PSF) is then extracted by taking a deconvolution process between the initial image and its corresponding speckle pattern. Consequently, the images of the dynamic objects, within the optical memory effect (OME) range, can then be reconstructed directly with the same deconvolution process between the sequential speckle patterns and the estimated PSF. In addition, a further calibration operation is employed to enhance the robustness of the PSF, ensuring sharp images can still be observed when objects are close to or even cross the edge of OME. Experimental demonstration is presented to verify the feasibility of our proposed method.
The speckle correlation technique is applied to ciphertext-only attack (COA) on optical cryptosystem based on double random phase encoding. According to the inherent merits of speckle correlation, we have revealed a fact that the ciphertext’s autocorrelation is essentially identical to the plaintext’s own autocorrelation. Then, a plaintext image can be directly reconstructed from the autocorrelation of its corresponding ciphertext by employing a iterate phase-retrieval algorithm. This could then lead to a potential security flaw because an unauthorized user could directly retrieve the plaintext from an intercepted ciphertext by performing proposed COA approach. Meanwhile, a series of numerical simulations will also be provided to verify the validity and feasibility of our proposed COA method.
We propose a novel method to achieve the purpose of hierarchical authentication based on two beams interference. In this method, different target images indicating different authentication levels are analytically encoded into corresponding phase-only masks (phase keys) and amplitude-only masks (amplitude keys) with the help of a random phase mask, which is created in advance and acts as the fixed lock of this authentication system. For the authentication process, a legal user can obtain a specified target image at the output plane if his/her phase key, and amplitude key, which should be settled close against the fixed internal phase lock, are respectively illuminated by two coherent beams. By comparing the target image with all the standard certification images in the database, the system can thus verify the user’s identity. In simple terms, this system can not only confirm the legality of a user but also distinguish his/her identity level. Moreover, in despite of the internal phase lock of this system being fixed, the crosstalk between different pairs of keys hold by different users is low. Theoretical analysis and numerical simulation are both provided to demonstrate the validity of this method.
We present an optical image encryption method based on a modified radial shearing interferometer. In our encryption
process, a plaintext image is first encoded into a phase-only mask (POM), and then modulated by a random phase mask
(RPM), the result is regarded as the input of the radial shearing interferometer and divided into two coherent lights, one
of which will be further modulated by a random amplitude mask (RAM). After all, these two coherent lights will
interfere with each other leading to an interferogram, i.e., ciphertext. And the ciphertext can be used to retrieve the
plaintext image with the help of a recursive algorithm and all correct keys. The aforementioned encryption procedure can
be achieved digitally or optically while the decryption process can be analytically accomplished. Numerical simulation is
provided to demonstrate the validity of this method.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.