In this Letter, we present a three-dimensional (3D) photon counting double-random-phase encryption (DRPE) technique using passive integral imaging. A 3D photon counting DRPE can encrypt a 3D scene and provides more security and authentications due to photon counting Poisson nonlinear transformation on the encrypted image. In addition, 3D imaging allows verification of the 3D object at different depths. Preliminary results and performance evaluation have been presented.

Dynamic imaging through scattering media under natural light is a significant challenge in many applications. To overcome this challenge, we propose a new passive imaging technique in scattering media using statistical estimation and photon counting modeling. We directly detect the ballistic photons from objects in scattering media based on statistical optics and then show experimental results to support our proposed method. We have named the proposed technique “peplography.” The word comes from Greek words $\pi \stackrel{\prime }{\epsilon }\pi \lambda o$ (péplo; “veiled”) and $\gamma \rho \alpha \varphi \stackrel{\prime }{\eta }\varsigma$ (grafís; “writing”). The peplography system directly detects ballistic photons associated with the objects from a single peplogram (“veiled image”) based on statistical optics, and reconstructs the three-dimensional (3D) peplogram using integral imaging.

Computational reconstruction of integral imaging requires much more computational loads than optical reconstruction because of the adding and averaging of many elemental images digitally. Thus, to reduce the computational loads, the pixels of elemental images rearrangement technique (PERT) has been proposed. It can reconstruct a three-dimensional (3D) image very fast, but the size of the reconstructed 3D image is different from the conventional computational reconstruction due to no consideration of the empty space between pixels on the reconstruction plane. Therefore, in this paper, we propose PERT considering the projected empty space to correct the size of the reconstructed 3D images. To verify and support our proposed method, we carry out preliminary experiments and calculate the structural similarity index.

In this paper, we propose a new 3D passive image sensing and visualization technique to improve lateral resolution and depth of field (DoF) of integral imaging simultaneously. There is a resolution trade-off between lateral resolution and DoF in integral imaging. To overcome this issue, a large aperture and a small aperture can be used to record the elemental images to reduce the diffraction effect and extend the DoF, respectively. Therefore, in this paper, we utilize these two pickup concepts with a non-uniform camera array. To show the feasibility of our proposed method, we implement an optical experiment. For comparison in details, we calculate the peak signal-to-noise ratio (PSNR) as the performance metric.

Digital Holographic Microscopy (DHM) is a 3D imaging technology widely applied in biology, microelectronics, and medical research. However, the noise generated during the 3D imaging process can affect the accuracy of medical diagnoses. To solve this problem, we proposed several frequency domain filtering algorithms. However, the filtering algorithms we proposed have a limitation in that they can only be applied when the distance between the direct current (DC) spectrum and sidebands are sufficiently far. To address these limitations, among the proposed filtering algorithms, the HiVA algorithm and deep learning algorithm, which effectively filter by distinguishing between noise and detailed information of the object, are used to enable filtering regardless of the distance between the DC spectrum and sidebands. In this paper, a combination of deep learning technology and traditional image processing methods is proposed, aiming to reduce noise in 3D profile imaging using the Improved Denoising Diffusion Probabilistic Models (IDDPM) algorithm.

In this paper, we propose three-dimensional(3D) visualization of objects in heavy scattering media by using peplography and wavelet transform. Conventional haze removal techniques can remove the light haze in the image by using various image processing algorithms or machine learning techniques. However, they may not provide a clear image under heavy scattering media. On the other hand, peplography can visualize the object by detecting ballistic object photons from heavy scattering media. Then, 3D image can be generated by integral imaging. However, it may not visualize 3D object information accurately because of the noise photons from scattering media. Therefore, the image quality for 3D object visualization may be degraded. To solve this problem, we use the discrete wavelet transform in peplography. It can detect the object photon signals from the scattering media and enhance 3D image contrast ratio by using a specific coefficient threshold technique. To prove our method, we carry out the optical experiments and compare with conventional haze removal method and peplography by using various image quality metrics such as correlation, structural similarity, and peak signal to noise ratio.