Computational volumetric reconstruction of integral imaging with improved depth resolution considering continuously non-uniform shifting pixels

In this paper, we propose a new computational volumetric reconstruction technique of three-dimensional (3D) integral imaging for depth resolution enhancement by using non-uniform and integer-valued shifting pixels. In a typical integral imaging system, 3D images can be recorded and visualized using a lenslet array. In previous studies, many computational reconstruction techniques such as computational volumetric reconstruction and pixel of elemental images rearrangement technique (PERT) have been reported. However, a computational volumetric reconstruction technique has low visual quality and depth resolution because low-resolution elemental images and uniformly distributed shifting pixels are used for reconstruction. Although PERT can enhanced the visual quality of the 3D images, the size of the reconstructed 3D images is different from the original scene. On the other hand, our proposed method uses non-uniformly distributed shifting pixels for reconstruction instead of uniformly distributed shifting pixels. Therefore, the visual quality and depth resolution may be enhanced. Finally, our experimental results show the improvement of depth resolution and visual quality of the reconstructed 3D images

Three-dimensional photon counting optical encryption with enhanced visual quality and security level

In this paper, we propose three-dimensional (3D) photon counting double random phase encryption (DRPE) with enhanced visual quality and security level. Conventional 3D photon counting DRPE can quickly encrypt the data by using the 4f optical system and random phase masks with enhanced security because the 3D photon counting technique is used. 3D photon counting DRPE extracts photons from the encrypted data by using statistical methods such as the Poisson random process, and the visual quality of the decrypted data can be enhanced through the 3D reconstruction process. However, it still has a problem to visualize the data when we extract extremely a few photons. To improve the security and the visual quality of the decrypted data, we propose the random amplitude reconstruction process in the encryption stage. Our proposed method reconstructs the amplitude of the encrypted data at a random depth. Thus, the shifting pixel value and depth information can be another important key for decryption through the random process. Therefore, it can effectively decrypt data securely, and the visual quality of the decrypted data can be enhanced. Finally, through the random reconstruction process in the decryption stage, our proposed method can simultaneously enhance the security and visual quality. To verify our proposed method, we carry out the simulation and optical experiment

3D Visualization of objects in heavy scattering media by using wavelet peplography

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 optical experiments and compare results with the conventional haze removal method and peplography by using various image quality metrics such as correlation, structural similarity, and peak signal-to-noise ratio

3D visualization technique for occluded objects in integral imaging using modified smart pixel mapping

In this paper, we propose a modified smart pixel mapping (SPM) to visualize occluded three-dimensional (3D) objects in real image fields. In integral imaging, orthoscopic real 3D images cannot be displayed because of lenslets and the converging light field from elemental images. Thus, pseudoscopic-to-orthoscopic conversion which rotates each elemental image by 180 degree, has been proposed so that the orthoscopic virtual 3D image can be displayed. However, the orthoscopic real 3D image cannot be displayed. Hence, a conventional SPM that recaptures elemental images for the orthoscopic real 3D image using virtual pinhole array has been reported. However, it has a critical limitation in that the number of pixels for each elemental image is equal to the number of elemental images. Therefore, in this paper, we propose a modified SPM that can solve this critical limitation in a conventional SPM and can also visualize the occluded objects efficiently

3D Visualization for Extremely Dark Scenes Using Merging Reconstruction and Maximum Likelihood Estimation

In this paper, we propose a new three-dimensional (3D) photon-counting integral imaging reconstruction method using a merging reconstruction process and maximum likelihood estimation (MLE). The conventional 3D photon-counting reconstruction method extracts photons from elemental images using a Poisson random process and estimates the scene using statistical methods such as MLE. However, it can reduce the photon levels because of an average overlapping calculation. Thus, it may not visualize 3D objects in severely low light environments. In addition, it may not generate high-quality reconstructed 3D images when the number of elemental images is insufficient. To solve these problems, we propose a new 3D photon-counting merging reconstruction method using MLE. It can visualize 3D objects without photon-level loss through a proposed overlapping calculation during the reconstruction process. We confirmed the image quality of our proposed method by performing optical experiments

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

Noise reduction method using a variance map of the phase differences in digital holographic microscopy

The phase reconstruction process in digital holographic microscopy involves a trade-off between the phase error and the high-spatial-frequency components. In this reconstruction process, if the narrow region of the sideband is windowed in the Fourier domain, the phase error from the DC component will be reduced, but the high-spatial-frequency components will be lost. However, if the wide region is windowed, the 3D profile will include the high-spatial-frequency components, but the phase error will increase. To solve this trade-off, we propose the high-variance pixel averaging method, which uses the variance map of the reconstructed depth profiles of the windowed sidebands of different sizes in the Fourier domain to classify the phase error and the high-spatial-frequency components. Our proposed method calculates the average of the high-variance pixels because they include the noise from the DC component. In addition, for the nonaveraged pixels, the reconstructed phase data created by the spatial frequency components of the widest window are used to include the high-spatial-frequency components. We explain the mathematical algorithm of our proposed method and compare it with conventional methods to verify its advantages

Improved 3D integral imaging reconstruction with elemental image pixel rearrangement

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

Three-Dimensional Photon Counting Integral Imaging Reconstruction using Merging Reconstruction Method

In this paper, we propose a new three-dimensional (3D) photon counting integral imaging reconstruction method using elemental image merging process for the effective use of the extracted photons under severely photon starved conditions. In previous methods, the photons are extracted from elemental images using Poisson random process and 3D images are reconstructed by Maximum Likelihood Estimation (MLE). However, 3D integral imaging with photon counting do not use the photons effectively in reconstruction. Thus, it may not visualize 3D objects in severely low light level conditions. In addition, it may not generate high-quality reconstructed 3D images when the number of the recorded elemental images is insufficient. Therefore, to solve these problems, we propose a new 3D reconstruction method for photon counting integral imaging, which can generate high-quality 3D images without photon loss in the reconstruction process. To prove our method, we carry out the simulation experiments.2nd International Conference on Artificial Intelligence in Information and Communication (ICAIIC 2020), February 19-21, 2020, Fukuoka, Japa

3D Visualization of Objects under scattering media conditions using Integral Imaging

In this paper, we propose a new three-dimensional (3D) reconstruction technique in scattering media using integral imaging. In conventional integral imaging, it can visualize 3D images well when high-resolution elemental images are used. However, when low-resolution images are used, the visual quality of the 3D images is reduced. In addition, it is difficult to recognize the objects under scattering media conditions. Therefore, to visualize 3D images in scattering media, we create a mask filter in frequency domain to remove the scattering media. In addition, we calculate the density of the scattered media to adjust the contrast and histogram equalization automatically. To prove our method, we implemented optical experiments.The 10th International Conference on Information and Communication Technology Convergence (ICTC 2019), 16-18 October 2019, Jeju Island, Kore