Three-Dimensional Single Random Phase Encryption.

In this paper, we propose a new optical encryption technique that uses the single random phase mask. In conventional optical encryptions such as double random phase encryption (DRPE), two different random phase masks are required to encrypt the primary data. For decryption, DRPE requires taking the absolute value of the decrypted data because it is complex-valued. In addition, when key information is revealed, the primary data may be reconstructed by attackers. To reduce the number of random phase masks and enhance the security level, in this paper, we propose single random phase encryption (SRPE) with additive white Gaussian noise (AWGN) and volumetric computational reconstruction (VCR) of integral imaging. In our method, even if key information is known, the primary data may not be reconstructed. To enhance the visual quality of the decrypted data by SRPE, multiple observation is utilized. To reconstruct the primary data, we use VCR of integral imaging because it can remove AWGN by average effect. Thus, since the reconstruction depth can be another key piece of information of SRPE, the security level can be enhanced. In addition, it does not require taking the absolute value of the decrypted data for decryption. To verify the validity of our method, we implement the simulation and calculate performance metrics such as peak sidelobe ratio (PSR) and structural similarity (SSIM). In increasing the number of observations, SSIM for the decrypted data can be improved dramatically. Moreover, even if the number of observations is not enough, three-dimensional (3D) data can be decrypted by SRPE at the correct reconstruction depth.

Three-Dimensional Image Visualization under Photon-Starved Conditions Using N Observations and Statistical Estimation.

In this paper, we propose a method for the three-dimensional (3D) image visualization of objects under photon-starved conditions using multiple observations and statistical estimation. To visualize 3D objects under these conditions, photon counting integral imaging was used, which can extract photons from 3D objects using the Poisson random process. However, this process may not reconstruct 3D images under severely photon-starved conditions due to a lack of photons. Therefore, to solve this problem, in this paper, we propose N-observation photon-counting integral imaging with statistical estimation. Since photons are extracted randomly using the Poisson distribution, increasing the samples of photons can improve the accuracy of photon extraction. In addition, by using a statistical estimation method, such as maximum likelihood estimation, 3D images can be reconstructed. To prove our proposed method, we implemented the optical experiment and calculated its performance metrics, which included the peak signal-to-noise ratio (PSNR), structural similarity (SSIM), peak-to-correlation energy (PCE), and the peak sidelobe ratio (PSR).

Image Processing Techniques for Improving Quality of 3D Profile in Digital Holographic Microscopy Using Deep Learning Algorithm.

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.

Three-Dimensional Digital Zooming of Integral Imaging under Photon-Starved Conditions.

In this paper, we propose new three-dimensional (3D) visualization of objects at long distance under photon-starved conditions. In conventional three-dimensional image visualization techniques, the visual quality of three-dimensional images may be degraded because object images at long distances may have low resolution. Thus, in our proposed method, we utilize digital zooming, which can crop and interpolate the region of interest from the image to improve the visual quality of three-dimensional images at long distances. Under photon-starved conditions, three-dimensional images at long distances may not be visualized due to the lack of the number of photons. Photon counting integral imaging can be used to solve this problem, but objects at long distance may still have a small number of photons. In our method, a three-dimensional image can be reconstructed, since photon counting integral imaging with digital zooming is used. In addition, to estimate a more accurate three-dimensional image at long distance under photon-starved conditions, in this paper, multiple observation photon counting integral imaging (i.e., N observation photon counting integral imaging) is used. To show the feasibility of our proposed method, we implement the optical experiments and calculate performance metrics, such as peak sidelobe ratio. Therefore, our method can improve the visualization of three-dimensional objects at long distances under photon-starved conditions.

Lensless Three-Dimensional Imaging under Photon-Starved Conditions.

In this paper, we propose a lensless three-dimensional (3D) imaging under photon-starved conditions using diffraction grating and computational photon counting method. In conventional 3D imaging with and without the lens, 3D visualization of objects under photon-starved conditions may be difficult due to lack of photons. To solve this problem, our proposed method uses diffraction grating imaging as lensless 3D imaging and computational photon counting method for 3D visualization of objects under these conditions. In addition, to improve the visual quality of 3D images under severely photon-starved conditions, in this paper, multiple observation photon counting method with advanced statistical estimation such as Bayesian estimation is proposed. Multiple observation photon counting method can estimate the more accurate 3D images by remedying the random errors of photon occurrence because it can increase the samples of photons. To prove the ability of our proposed method, we implement the optical experiments and calculate the peak sidelobe ratio as the performance metric.

Three-Dimensional Image Transmission of Integral Imaging through Wireless MIMO Channel.

For the reconstruction of high-resolution 3D digital content in integral imaging, an efficient wireless 3D image transmission system is required to convey a large number of elemental images without a communication bottleneck. To support a high transmission rate, we herein propose a novel wireless three-dimensional (3D) image transmission and reception strategy based on the multiple-input multiple-output (MIMO) technique. By exploiting the spatial multiplexing capability, multiple elemental images are transmitted simultaneously through the wireless MIMO channel, and recovered with a linear receiver such as matched filter, zero forcing, or minimum mean squared error combiners. Using the recovered elemental images, a 3D image can be reconstructed using volumetric computational reconstruction (VCR) with non-uniform shifting pixels. Although the received elemental images are corrupted by the wireless channel and inter-stream interference, the averaging effect of the VCR can improve the visual quality of the reconstructed 3D images. The numerical results validate that the proposed system can achieve excellent 3D reconstruction performance in terms of the visual quality and peak sidelobe ratio though a large number of elemental images are transmitted simultaneously over the wireless MIMO channel.

Three-Dimensional (3D) Visualization under Extremely Low Light Conditions Using Kalman Filter.

In recent years, research on three-dimensional (3D) reconstruction under low illumination environment has been reported. Photon-counting integral imaging is one of the techniques for visualizing 3D images under low light conditions. However, conventional photon-counting integral imaging has the problem that results are random because Poisson random numbers are temporally and spatially independent. Therefore, in this paper, we apply a technique called Kalman filter to photon-counting integral imaging, which corrects data groups with errors, to improve the visual quality of results. The purpose of this paper is to reduce randomness and improve the accuracy of visualization for results by incorporating the Kalman filter into 3D reconstruction images under extremely low light conditions. Since the proposed method has better structure similarity (SSIM), peak signal-to-noise ratio (PSNR) and cross-correlation values than the conventional method, it can be said that the visualization of low illuminated images can be accurate. In addition, the proposed method is expected to accelerate the development of autonomous driving technology and security camera technology.

3D photon counting integral imaging by using multi-level decomposition.

In this paper, we propose three-dimensional (3D) photon counting integral imaging by using multi-level decomposition such as discrete wavelet transform to improve the visual quality and measurement accuracy under photon-starved conditions. Conventional 3D integral imaging can visualize 3D objects and acquire their depth information. However, the amount of irradiated light on the object causes the degradation of visual quality for 3D images under photon-starved conditions. To visualize 3D objects, photon counting integral imaging has been utilized. It can detect photons from 3D scenes by using a computational photon counting model, which is modelled by the Poisson random process. However, photons occur not only from objects but also in areas where objects do not exist. Moreover, photon fluctuation may occur in the scene through shot noise. Since these noise photons are measurement errors, it may decrease the image quality and accuracy. In contrast, our proposed method uses 2D discrete wavelet transform, which can emphasize the object photons effectively. Finally, our proposed method can enhance the visual quality of 3D images and provide more accurate depth information under photon-starved conditions. To prove the feasibility of our proposed method, we implement the optical experiment and calculate various image quality metrics.

Enhancement of three-dimensional image visualization under photon-starved conditions.

In this paper, we propose enhancement of three-dimensional (3D) image visualization under photon-starved conditions using preprocessing such as contrast-limited adaptive histogram equalization (CLAHE) and histogram matching. In conventional imaging techniques, photon-counting integral imaging can be utilized for 3D visualization. However, due to a lack of photons, it is challenging to enhance the visual quality of 3D images under severely photon-starved conditions. To improve the visual quality and accuracy of 3D images under these conditions, in this paper, we apply CLAHE and histogram matching to a scene before photon-counting integral imaging is used. To prove the feasibility of our proposed method, we implement the optical experiment and show the performance metric such as peak sidelobe ratio.

Three-Dimensional Integral Imaging with Enhanced Lateral and Longitudinal Resolutions Using Multiple Pickup Positions.

In this paper, we propose an enhancement of three-dimensional (3D) image visualization techniques by using different pickup plane reconstructions. In conventional 3D visualization techniques, synthetic aperture integral imaging (SAII) and volumetric computational reconstruction (VCR) can be utilized. However, due to the lack of image information and shifting pixels, it may be difficult to obtain better lateral and longitudinal resolutions of 3D images. Thus, we propose a new elemental image acquisition and computational reconstruction to improve both the lateral and longitudinal resolutions of 3D objects. To prove the feasibility of our proposed method, we present the performance metrics, such as mean squared error (MSE), peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and peak-to-sidelobe ratio (PSR). Therefore, our method can improve both the lateral and longitudinal resolutions of 3D objects more than the conventional technique.

Noise Filtering Method of Digital Holographic Microscopy for Obtaining an Accurate Three-Dimensional Profile of Object Using a Windowed Sideband Array (WiSA).

In the image processing method of digital holographic microscopy (DHM), we can obtain a phase information of an object by windowing a sideband in Fourier domain and taking inverse Fourier transform. In this method, it is necessary to window a wide sideband to obtain detailed information on the object. However, since the information of the DC spectrum is widely distributed over the entire range from the center of Fourier domain, the window sideband includes not only phase information but also DC information. For this reason, research on acquiring only the phase information of an object without noise in digital holography is a challenging issue for many researchers. Therefore, in this paper, we propose the use of a windowed sideband array (WiSA) as an image processing method to obtain an accurate three-dimensional (3D) profile of an object without noise in DHM. The proposed method does not affect the neighbor pixels of the filtered pixel but removes noise while maintaining the detail of the object. Thus, a more accurate 3D profile can be obtained compared with the conventional filter. In this paper, we create an ideal comparison target i.e., microspheres for comparison, and verify the effect of the filter through additional experiments using red blood cells.

Angular spectrum matching for digital holographic microscopy under extremely low light conditions.

Digital holographic microscopy (DHM) is a future three-dimensional (3D) microscopy due to its high-resolution and high-precision 3D images. Thus, it is getting attention in bioinformatics, semiconductor defect detection, etc. However, some limitations still exist. Especially, high-speed holographic imaging requires high-power lasers, which are difficult to image on highly absorbent or light-sensitive samples. To overcome these issues, we propose a new, to the best of our knowledge, digital hologram recovery algorithm called angular spectrum matching (ASM), which achieves hologram imitation to recover holograms in digital holography at low light intensities. The hologram used for the background phase comparison is recorded without objects; thus, no power limitation is required. The ASM utilizes this background hologram to recover dark holograms. We present experimental results showing improved DHM numerical reconstructions and recovered holograms under extremely low light conditions.

Three-dimensional imaging system with both improved lateral resolution and depth of field considering non-uniform system parameters.

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.

Peplography-a passive 3D photon counting imaging through scattering media.

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 πε'πλo (péplo; "veiled") and γραϕη'ς (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.

Three-Dimensional Photon Counting Imaging with Axially Distributed Sensing.

In this paper, we review three-dimensional (3D) photon counting imaging with axially distributed sensing. Under severely photon-starved conditions, we have proposed various imaging and algorithmic approaches to reconstruct a scene in 3D, which are not possible by using conventional imaging system due to lack of sufficient number of photons. In this paper, we present an overview of optical sensing and imaging system along with dedicated algorithms for reconstructing 3D scenes by photon counting axially distributed sensing, which may be implemented by moving a single image sensor along its optical axis. To visualize the 3D image, statistical estimation methods and computational reconstruction of axially distributed sensing is applied.

Three-dimensional image transmission and reconstruction for multisensor imaging system using interleaver division multiple access.

In this paper, we present an optical image transmission and reconstruction system of 3D objects using a multisensor imaging system and interleaver division multiple access (IDMA) channel. When the 3D image data from the multisensor imaging system are transmitted over a wireless channel, loss or distortion of original data may occur by wireless channel environment, such as multiple access interference and channel noise. To solve this problem, an optical 3D image reconstruction scheme and IDMA technique can be used. Reconstructed 3D image data at the receiver is clear enough to distinguish the depth of 3D objects. To prove our proposed scheme, we carry out an optical experiment for sensing 3D information and simulation for data transmission of a multisensor imaging system via IDMA with channel noise.

Orthoscopic real image reconstruction in integral imaging by rotating an elemental image based on the reference point of object space.

We propose a new approach for depth conversion of three-dimensional (3D) reconstruction from pseudoscopic to orthoscopic real images in resolution priority integral imaging. In integral imaging, depth of field is recorded in an elemental image array. In the proposed method, the depth information is converted by a 180° rotation of each elemental image in an elemental image array based on a reference point of conversion, which is caused by a reference point of object space. Orthoscopic real images can be reconstructed in 3D space by using the depth conversion of an elemental image array. The feasibility of the proposed method has been confirmed through preliminary experiments as well as ray optical analysis.

Three-dimensional photon counting double-random-phase encryption.

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.

A Novel Image Processing Method for Obtaining an Accurate Three-Dimensional Profile of Red Blood Cells in Digital Holographic Microscopy.

Recently, research on disease diagnosis using red blood cells (RBCs) has been active due to the advantage that it is possible to diagnose many diseases with a drop of blood in a short time. Representatively, there are disease diagnosis technologies that utilize deep learning techniques and digital holographic microscope (DHM) techniques. However, three-dimensional (3D) profile obtained by DHM has a problem of random noise caused by the overlapping DC spectrum and sideband in the Fourier domain, which has the probability of misjudging diseases in deep learning technology. To reduce random noise and obtain a more accurate 3D profile, in this paper, we propose a novel image processing method which randomly selects the center of the high-frequency sideband (RaCoHS) in the Fourier domain. This proposed algorithm has the advantage of filtering while using only recorded hologram information to maintain high-frequency information. We compared and analyzed the conventional filtering method and the general image processing method to verify the effectiveness of the proposed method. In addition, the proposed image processing algorithm can be applied to all digital holography technologies including DHM, and in particular, it is expected to have a great effect on the accuracy of disease diagnosis technologies using DHM.

3D passive integral imaging using compressive sensing.

Passive 3D sensing using integral imaging techniques has been well studied in the literature. It has been shown that a scene can be reconstructed at various depths using several 2D elemental images. This provides the ability to reconstruct objects in the presence of occlusions, and passively estimate their 3D profile. However, high resolution 2D elemental images are required for high quality 3D reconstruction. Compressive Sensing (CS) provides a way to dramatically reduce the amount of data that needs to be collected to form the elemental images, which in turn can reduce the storage and bandwidth requirements. In this paper, we explore the effects of CS in acquisition of the elemental images, and ultimately on passive 3D scene reconstruction and object recognition. Our experiments show that the performance of passive 3D sensing systems remains robust even when elemental images are recovered from very few compressive measurements.