We demonstrate a multispectral double-image-based cryptosystem that exploits only a tiny number of random white noise samples for proper decryption. Primarily, one of the two downsampled images is converted into the phase function after being shuffled by Arnold transform (AT), while the other image is modulated as an amplitude-based image after AT. Consecutively, a full double-image encryption can be achieved by employing classical double random phase encryption (DRPE) technique in the fractional Fourier transform domain with corresponding fractional orders. In this study, the encrypted complex data is randomly sampled via compressive sensing (CS) framework by which only 25% of the sparse white noise samples are being reserved to realize decryption with zero or small errors. As a consequence, together with correct phase keys, fractional orders and proper inverse AT operators, $l_p$ minimization must be utilized to decrypt the original information. Thus, in addition to the perfect image reconstruction, the proposed cryptosystem provides an additional layer of security to the conventional DRPE system. Both the mathematical and numerical simulations were carried out to verify the feasibility as well as the robustness of the proposed system. The simulation results are presented in order to demonstrate the effectiveness of the proposed system. To the best of our knowledge, this is the first report on integrating CS with encrypted complex samples for information security.

This paper presents a new method for three-dimensional (3D) scene acquisition via reconstruction with multispectral information and its Fourier-based encryption using computational integral imaging, by which the field of view, resolution, and information security are increased, respectively. The color imaging sensors covered with a Bayer color filter array captures elemental images (EI) at different spectral bands (400 and 700 nm intervals in the visible spectrum). Subsequently, double random phase encryption (DRPE) in the Fourier domain is employed on Bayer formatted EI to encrypt the captured 3D scene. Proper 3D object reconstruction only can be achieved by applying inverse decryption and a geometric ray backpropagation algorithm on the encrypted EI. Further, the high-resolution multispectral 3D scene can be visualized by using various adaptive interpolation algorithms. To objectively evaluate our proposed method, we carried out computational experiments for 3D object sensing, reconstruction, and digital simulations for DRPE. Experiment results validate the feasibility and robustness of our proposed approach, even under severe degradation.

The hybrid input–output algorithm, error reduction algorithm, and combinations of both phase retrieval algorithms are applied to perform ciphertext-only attacks on linear canonical transform (LCT)-based amplitude encoding double-random-phase encryption (DRPE) systems. Special cases of LCT-based DRPE systems, i.e., Fourier-transform-based, fractional-Fourier-transform-based, and Fresnel-transform-based DRPE, can also be successfully attacked using the method proposed. Numerical simulations are performed to demonstrate the efficacy of the proposed attacking method.

We present a method of securing multispectral 3D photon-counted integral imaging (PCII) using classical Hartley Transform (HT) based encryption by employing optical interferometry. This method has the simultaneous advantages of minimizing complexity by eliminating the need for holography recording and addresses the phase sensitivity problem encountered when using digital cameras. These together with single-channel multispectral 3D data compactness, the inherent properties of the classical photon counting detection model, i.e. sparse sensing and the capability for nonlinear transformation, permits better authentication of the retrieved 3D scene at various depth cues. Furthermore, the proposed technique works for both spatially and temporally incoherent illumination. To validate the proposed technique simulations were carried out for both the 2D and 3D cases. Experimental data is processed and the results support the feasibility of the encryption method.

Recently, three-dimensional (3D) object reconstruction using photon counting integral imaging (PCII) based on grayscale elemental images has been reported. In this paper, we present a multispectral PCII system using Bayer elemental images for multispectral visualization of photon limited 3D scenes. We measure the accuracy as well as the speed of demosaicing algorithms to estimate multispectral 3D scenes at low light levels. Experimental results demonstrate that the gradient corrected linear interpolation technique achieves better performance in terms of peak-to-SNR (PSNR) and faster computation.

Cholangiocarcinoma is one of the rarest yet most aggressive cancers that has a low 5-year survival rate (2% - 24%) and thus often requires an accurate and timely diagnosis. Hyperspectral Imaging (HSI) is a recently developed, promising spectroscopic-based non-invasive bioimaging technique that records a spatial image (x, y) together with wide spectral (λ) information. In this work, for the first time we propose to use a three-dimensional (3D)U-Net architecture for Hyperspectral microscopic imaging-based cholangiocarcinoma detection and classification. In addition to this architecture, we opted for a few preprocessing steps to achieve higher classification accuracy (CA) with minimal computational cost. Our results are compared with several standard unsupervised and supervised learning approaches to prove the efficacy of the proposed network and the preprocessing steps. For instance, we compared our results with state-of-the-art architectures, such as the Important-Aware Network (IANet), the Context Pyramid Fusion Network (CPFNet), and the semantic pixel-wise segmentation network (SegNet). We showed that our proposed architecture achieves an increased CA of 1.29% with the standard preprocessing step i.e., flat-field correction, and of 4.29% with our opted preprocessing steps.