Kotaro Inoue

Information Authentication of Three-Dimensional Photon Counting Double Random Phase Encryption Using Nonlinear Maximum Average Correlation Height Filter

In this paper, we propose a nonlinear maximum average correlation height (MACH) filter for information authentication of photon counting double random phase encryption (DRPE). To enhance the security of DRPE, photon counting imaging can be applied because of its sparseness. However, under severely photon-starved conditions, information authentication of DRPE may not be implemented successfully. To visualize the photon counting DRPE, a three-dimensional imaging technique such as integral imaging can be used. In addition, a nonlinear MACH filter can be utilized for helping the information authentication. Therefore, in this paper, we use integral imaging and nonlinear MACH filter to implement the information authentication of photon counting DRPE. To verify our method, we implement optical experiments and computer simulation.

Depth Extraction of Partially Occluded 3D Objects Using Axially Distributed Stereo Image Sensing

There are several methods to record three dimensional (3D) information of objects such as lens array based integral imaging, synthetic aperture integral imaging (SAII), computer synthesized integral imaging (CSII), axially distributed image sensing (ADS), and axially distributed stereo image sensing (ADSS). ADSS method is capable of recording partially occluded 3D objects and reconstructing high-resolution slice plane images. In this paper, we present a computational method for depth extraction of partially occluded 3D objects using ADSS. In the proposed method, the high resolution elemental stereo image pairs are recorded by simply moving the stereo camera along the optical axis and the recorded elemental image pairs are used to reconstruct 3D slice images using the computational reconstruction algorithm. To extract depth information of partially occluded 3D object, we utilize the edge enhancement and simple block matching algorithm between two reconstructed slice image pair. To demonstrate the proposed method, we carry out the preliminary experiments and the results are presented.

Computational reconstruction technique in integral imaging with enhanced visual quality

In this paper, we propose a visual quality enhancement of 3D reconstruction algorithm in integral imaging. Conventional integral imaging has a critical problem that attenuates the visual quality of 3D objects when low-resolution elemental images are used. Although, PERT is one of the solutions, the size of 3D scenes is different from optical reconstruction since it is not considering space between back-projected pixels on reconstruction planes. Therefore, we consider this space and use convolution operator. Especially, convolution operator can be designed by considering aperture shapes. To support our proposed method, we carry out optical experiment and computer simulations.

3D resolution enhancement of integral imaging using resolution priority integral imaging and depth priority integral imaging

In this paper, we propose a new passive image sensing and visualization of 3D objects using concept of both resolution priority integral imaging (RPII) and depth priority integral imaging (DPII) to improve lateral and depth resolutions of 3D images simultaneously. We suppose that elemental images are the most important information for 3D performance of integral imaging, since they include both lateral and depth resolutions of 3D objects. Therefore, all resolutions of the reconstructed 3D images are determined by these elemental images in pickup stage. In this paper, we analyze the lateral and depth resolutions that depend on the basic parameters of camera or lens for pickup. Then, we describe our proposed method. To support our proposed method, we carry out the computer simulation. In addition, we analyze how the surface light of 3D objects placed in arbitrary position can be expressed within the permitted range according to the setting of camera parameters. Finally, to evaluate the performance of our method, peak signal to noise ratio (PSNR) is calculated.

Depth estimation of computational reconstruction in integral imaging by considering the pixel blink rate

In this paper, we propose a new high-resolution depth estimation algorithm in integral imaging which can obtain threedimensional (3D) images by using lenslet array. In conventional studies, a stereo-matching is used for depth estimation. However, it is not the best solution for integral imaging since the 3D images are usually low-resolution images. Therefore, we propose a pixel blink rate based algorithm using pixel of the elemental images rearrangement technique (PERT) in integral imaging. Through our optical experiment, the depth resolution by our technique is dramatically improved compared with a conventional method.

Depth resolution enhancement of computational reconstruction of integral imaging

In this paper, we propose a new computational reconstruction technique of integral imaging for depth resolution enhancement by using integer-valued and non-uniform shifting pixels. In a general integral imaging system, we can record and visualize (or display) 3D object using lenslet array. In previous studies, many reconstruction techniques such as computational volumetric reconstruction and pixel of elemental images rearrangement technique (PERT) have been reported. However, a conventional 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. On the other hand, our proposed method uses non-uniformly distributed shifting pixels for reconstruction instead of uniformly distributed shifting pixels in conventional computational volumetric reconstruction. Thus, 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 Automatic Target Recognition System Based on Optical Integral Imaging Reconstruction

In this paper, we present a three-dimensional (3-D) automatic target recognition system based on optical integral imaging reconstruction. In integral imaging, elemental images of the reference and target 3-D objects are obtained through a lenslet array or a camera array. Then, reconstructed 3-D images at various reconstruction depths can be optically generated on the output plane by back-projecting these elemental images onto a display panel. 3-D automatic target recognition can be implemented using computational integral imaging reconstruction and digital nonlinear correlation filters. However, these methods require non-trivial computation time for reconstruction and recognition. Instead, we implement 3-D automatic target recognition using optical cross-correlation between the reconstructed 3-D reference and target images at the same reconstruction depth. Our method depends on an all-optical structure to realize a real-time 3-D automatic target recognition system. In addition, we use a nonlinear correlation filter to improve recognition performance. To prove our proposed method, we carry out the optical experiments and report recognition results.

Color Compensation of an Underwater Imaging System Using Electromagnetic Wave Propagation

Images can be obtained by collecting rays from objects. The characteristics of electromagnetic wave propagation depend on the medium. In particular, in an underwater imaging system, the interface between air and water must be considered. Further, reflection and transmission coefficients can be found by using electromagnetic theory. Because of the fact that the values of these coefficients differ according to the media, the recorded light intensities will change. A color image sensor has three different color channels. Therefore, the reflection and transmission coefficients have to be calculated individually. Thereafter, by using these coefficients, we can compensate for the color information of underwater objects. In this paper, we present a method to compensate for the color information of underwater objects by using electromagnetic wave propagation theory. To prove our method, we conducted optical experiments and evaluated the quality of the compensated image by a metric known as mean square error.

Development of active state measurements system for the cells in solution

With the objective of developing a system to quantitatively measure the active states of microorganisms and cells by using laser speckle, we performed simulations to determine the optimum system parameters. In this study, the obtained optimum parameters were applied, dynamic speckle was observed by measuring moving objects with an optical device, and the parameters obtained from the dynamic speckle were evaluated.

Improved 3D Resolution Analysis of N-Ocular Imaging Systems with the Defocusing Effect of an Imaging Lens

In this paper, we propose an improved framework to analyze an N-ocular imaging system under fixed constrained resources such as the number of image sensors, the pixel size of image sensors, the distance between adjacent image sensors, the focal length of image sensors, and field of view of image sensors. This proposed framework takes into consideration, for the first time, the defocusing effect of the imaging lenses according to the object distance. Based on the proposed framework, the Nocular imaging system such as integral imaging is analyzed in terms of depth resolution using two-point-source resolution analysis. By taking into consideration the defocusing effect of the imaging lenses using ray projection model, it is shown that an improved depth resolution can be obtained near the central depth plane as the number of cameras increases. To validate the proposed framework, Monte Carlo simulations are carried out and the results are analyzed.