Adrian Stern

Three-Dimensional Image Sensing, Visualization, and Processing Using Integral Imaging

Three dimensional (3-D) imaging and display have been subjects of much research due to their diverse benefits and applications. However, due to the necessity to capture, record, process, and display an enormous amount of optical data for producing high-quality 3-D images, the developed 3-D imaging techniques were forced to compromise their performances (e.g., gave up the continuous parallax, restricting to a fixed viewing point) or to use special devices and technology (such as coherent illuminations, special spectacles) which is inconvenient for most practical implementation. Todays rapid progress of digital capture and display technology opened the possibility to proceed toward noncompromising, easy-to-use 3-D imaging techniques. This technology progress prompted the revival of the integral imaging (II)technique based on a technique proposed almost one century ago. II is a type of multiview 3-D imaging system that uses an array of diffractive or refractive elements to capture the 3-D optical data. It has attracted great attention recently, since it produces autostereoscopic images without special illumination requirements. However, with a conventional II system it is not possible to produce 3-D images that have both high resolution, large depth-of-field, and large viewing angle. This paper provides an overview of the approaches and techniques developed during the last decade to overcome these limitations. By combining these techniques with upcoming technology it is to be expected that II-based 3-D imaging systems will reach practical applicability in various fields.

Advances in three-dimensional integral imaging: sensing, display, and applications [Invited]

Three-dimensional (3D) sensing and imaging technologies have been extensively researched for many applications in the fields of entertainment, medicine, robotics, manufacturing, industrial inspection, security, surveillance, and defense due to their diverse and significant benefits. Integral imaging is a passive multiperspective imaging technique, which records multiple two-dimensional images of a scene from different perspectives. Unlike holography, it can capture a scene such as outdoor events with incoherent or ambient light. Integral imaging can display a true 3D color image with full parallax and continuous viewing angles by incoherent light; thus it does not suffer from speckle degradation. Because of its unique properties, integral imaging has been revived over the past decade or so as a promising approach for massive 3D commercialization. A series of key articles on this topic have appeared in the OSA journals, including Applied Optics. Thus, it is fitting that this Commemorative Review presents an overview of literature on physical principles and applications of integral imaging. Several data capture configurations, reconstruction, and display methods are overviewed. In addition, applications including 3D underwater imaging, 3D imaging in photon-starved environments, 3D tracking of occluded objects, 3D optical microscopy, and 3D polarimetric imaging are reviewed.

Sampling of linear canonical transformed signals

Linear canonical transforms play an important role in many fields of optics and signal processing. Well-known transforms such as the Fourier transform, the fractional Fourier transform, and the Fresnel transform can be seen as special cases of the linear canonical transform. In this paper we develop a sampling theorem for linear canonical transformed signals. The well-known Shannon sampling theorem and previously developed sampling criteria for Fresnel and fractional Fourier transformed signals are shown to be a special cases of the theorem developed here.

Compressive Fresnel Holography

Compressive sensing is a relatively new measurement paradigm which seeks to capture the “essential” aspects of a high-dimensional object using as few measurements as possible. In this work we demonstrate successful application of compressive sensing framework to digital Fresnel holography. It is shown that when applying compressive sensing approach to Fresnel fields a special sampling scheme should be adopted for improved results.

Compressed Imaging With a Separable Sensing Operator

Compressive imaging (CI) is a natural branch of compressed sensing (CS). Although a number of CI implementations have started to appear, the design of efficient CI system still remains a challenging problem. One of the main difficulties in implementing CI is that it involves huge amounts of data, which has far-reaching implications for the complexity of the optical design, calibration, data storage and computational burden. In this paper, we solve these problems by using a two-dimensional separable sensing operator. By so doing, we reduce the complexity by factor of 106 for megapixel images. We show that applying this method requires only a reasonable amount of additional samples.

Uncertainty principles in linear canonical transform domains and some of their implications in optics

The linear canonical transform (LCT) is the name of a parameterized continuum of transforms that include, as particular cases, many widely used transforms in optics such as the Fourier transform, fractional Fourier transform, and Fresnel transform. It provides a generalized mathematical tool for representing the response of any first-order optical system in a simple and insightful way. In this work we present four uncertainty relations between LCT pairs and discuss their implications in some common optical systems.

Compressive hyperspectral imaging by random separable projections in both the spatial and the spectral domains

An efficient method and system for compressive sensing of hyperspectral data is presented. Compression efficiency is achieved by randomly encoding both the spatial and the spectral domains of the hyperspectral datacube. Separable sensing architecture is used to reduce the computational complexity associated with the compressive sensing of a large volume of data, which is typical of hyperspectral imaging. The system enables optimizing the ratio between the spatial and the spectral compression sensing ratios. The method is demonstrated by simulations performed on real hyperspectral data.

Roadmap on optical security

Information security and authentication are important challenges facing society. Recent attacks by hackers on the databases of large commercial and financial companies have demonstrated that more research and development of advanced approaches are necessary to deny unauthorized access to critical data. Free space optical technology has been investigated by many researchers in information security, encryption, and authentication. The main motivation for using optics and photonics for information security is that optical waveforms possess many complex degrees of freedom such as amplitude, phase, polarization, large bandwidth, nonlinear transformations, quantum properties of photons, and multiplexing that can be combined in many ways to make information encryption more secure and more difficult to attack. This roadmap article presents an overview of the potential, recent advances, and challenges of optical security and encryption using free space optics. The roadmap on optical security is comprised of six categories that together include 16 short sections written by authors who have made relevant contributions in this field. The first category of this roadmap describes novel encryption approaches, including secure optical sensing which summarizes double random phase encryption applications and flaws [Yamaguchi], the digital holographic encryption in free space optical technique which describes encryption using multidimensional digital holography [Nomura], simultaneous encryption of multiple signals [Perez-Cabre], asymmetric methods based on information truncation [Nishchal], and dynamic encryption of video sequences [Torroba]. Asymmetric and one-way cryptosystems are analyzed by Peng. The second category is on compression for encryption. In their respective contributions, Alfalou and Stern propose similar goals involving compressed data and compressive sensing encryption. The very important area of cryptanalysis is the topic of the third category with two sections: Sheridan reviews phase retrieval algorithms to perform different attacks, whereas Situ discusses nonlinear optical encryption techniques and the development of a rigorous optical information security theory. The fourth category with two contributions reports how encryption could be implemented at the nano- or micro-scale. Naruse discusses the use of nanostructures in security applications and Carnicer proposes encoding information in a tightly focused beam. In the fifth category, encryption based on ghost imaging using single-pixel detectors is also considered. In particular, the authors [Chen, Tajahuerce] emphasize the need for more specialized hardware and image processing algorithms. Finally, in the sixth category, Mosk and Javidi analyze in their corresponding papers how quantum imaging can benefit optical encryption systems. Sources that use few photons make encryption systems much more difficult to attack, providing a secure method for authentication.

Phase-Modulated Optical System With Sparse Representation for Information Encoding and Authentication

We develop a phase-modulated optical system with sparse representation for information encoding and authentication. The optical cryptosystem is developed with cascaded phase-only masks, and the plaintext is encoded into the cascaded phase-only masks based on an iterative phase retrieval algorithm during the encryption. Two simple strategies are developed to generate sparse data: The sparse data are randomly generated from the extracted phase-only masks, and sparse data are randomly generated from the plaintext. These two sparsity strategies are respectively used in the proposed optical security system, and the decrypted images cannot visually render information about the plaintext. Optical authentication method is further applied to verify the decrypted images. It is illustrated that the optical authentication operation with sparsity strategy can provide an additional security layer for the optical security system.

Overview of compressive sensing techniques applied in holography [Invited].

In recent years compressive sensing (CS) has been successfully introduced in digital holography (DH). Depending on the ability to sparsely represent an object, the CS paradigm provides an accurate object reconstruction framework from a relatively small number of encoded signal samples. DH has proven to be an efficient and physically realizable sensing modality that can exploit the benefits of CS. In this paper, we provide an overview of the theoretical guidelines for application of CS in DH and demonstrate the benefits of compressive digital holographic sensing.