Myungjin Cho

Information authentication using photon-counting double-random-phase encrypted images

Photon-counting imaging is integrated with optical encryption for information authentication. An image is double-random-phase encrypted, and a photon-limited encrypted image is obtained. The photon-counting encrypted image is generated with few photons and appears sparse; however, we show that it has sufficient information for decryption and authentication. The decrypted image cannot be easily visualized so that an additional layer of information protection is achieved. The authentication is carried out by recognition algorithms. This approach may make the verification process more robust against attacks. To the best of our knowledge, this is the first report on integrating photon-counting imaging and encryption for authentication.

Three-Dimensional Optical Sensing and Visualization Using Integral Imaging

Three-dimensional (3-D) optical image sensing and visualization technologies have been researched extensively for different applications in fields as diverse as entertainment, medical sciences, robotics, manufacturing, and defense. In many instances, the capabilities of 3-D imaging and display systems have revolutionized the progress of these disciplines, enabling new detection/display abilities that would not have been otherwise possible. As one of the promising methods in the area of 3-D sensing and display, integral imaging offers passive and relatively inexpensive way to capture 3-D information and to visualize it optically or computationally. The integral imaging technique belongs to the broader class of multiview imaging techniques and is based on a century old principle which has only been resurrected in the past decade owing to advancement of optoelectronic image sensors as well as the exponential increase in computing power. In this paper, historic and physical foundations of integral imaging are overviewed; different optical pickup and display schemes are discussed and system parameters and performance metrics are described. In addition, computational methods for reconstruction and range estimation are presented and several applications including 3-D underwater imaging, near infra red passive sensing, imaging in photon-starved environments, and 3-D optical microscopy are discussed among others.

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.

Three-Dimensional Visualization of Objects in Turbid Water Using Integral Imaging

In this paper, we demonstrate three-dimensional (3D) visualization of objects in turbid water. The sensing is performed by integral imaging. Multi-perspectives images are degraded due to light scattering and are treated by statistical image processing and computational 3D reconstruction algorithms to remedy the effects of scattering and to visualize the 3D scene. Reconstruction distances and underwater elemental images are calculated according to Snells law. To the best of our knowledge, this is the first report on 3D reconstruction of objects in turbid water using integral imaging. Experimental results are presented.

Improved Viewing Zones for Projection Type Integral Imaging 3D Display Using Adaptive Liquid Crystal Prism Array

In this paper, we present a novel projection-type 3-D integral imaging display with an adaptive liquid crystal (LC) prism array. Comparing with conventional integral imaging display, the proposed system demonstrated that the viewing zones for a projection type integral imaging display was successfully extended by time-multiplexed technique and without any mechanical movement. To the best of our knowledge, this is the first report on combining an adaptive LC prism array with the projection-type integral imaging 3-D display. The proposed display is attractive for future wide viewing zone projection-type integral imaging 3-D displays.

3D passive photon counting automatic target recognition using advanced correlation filters

In this Letter, we present results for detecting and recognizing 3D objects in photon counting images using integral imaging with maximum average correlation height filters. We show that even under photon starved conditions objects may be automatically recognized in passively sensed 3D images using advanced correlation filters. We show that the proposed filter synthesized with ideal training images can detect and recognize a 3D object in photon counting images, even in the presence of occlusions and obscuration.

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.

Tracking of multiple objects in unknown background using Bayesian estimation in 3D space

We present a three-dimensional (3D) object tracking method based on a Bayesian framework for tracking multiple, occluded objects in a complex scene. The 3D passive capture of scene data is based on integral imaging. The statistical characteristics of the objects versus the background are exploited to analyze each frame. The algorithm can work with objects with unknown position, rotation, scale, and illumination. Posterior probabilities of the reconstructed scene background and the 3D objects are calculated by defining their pixel intensities as Gaussian and gamma distributions, respectively, and by assuming appropriate prior distributions for estimated parameters. Multiobject tracking is achieved by maximizing the geodesic distance between the log-posteriors of the background and the objects. Experimental results are presented.

Three-dimensional photon counting integral imaging using Bayesian estimation.

We propose a new estimation method for 3D object reconstruction using photon-counting integral imaging. Earlier studies used maximum likelihood estimation (MLE) as a classical statistical method to reconstruct 3D images from photon-counting elemental images. We use an alternative statistical method known as the Bayesian method, which is more flexible and may perform better than MLE in terms of the mean square error (MSE) metric. The performance of the new reconstruction method is illustrated and compared with MLE by using the MSE. To the best of our knowledge, this is the first report to use the Bayesian method for 3D reconstruction of photon-counting integral imaging.

Optimization of 3D Integral Imaging System Parameters

Integral imaging is a promising technology for 3D imaging and display. A number of parameters affect the performance of 3D integral imaging systems. Improved system performance is obtained by optimization of these parameters with respect to defined performance metrics. In this paper, we present an approach to optimize the performance of 3D integral imaging system in terms of performance metrics under fixed resource constraints. In this analysis, system parameters such as lens numerical aperture, pitch between image sensors, the number of image sensors, the pixel size, and the number of pixels are selected to optimize performance metrics. Wave optics is utilized to describe the imaging process.