A self-scanned 1024 element photodiode array and minicomputer are used to measure the phase (wavefront) in the interference pattern of an interferometer to lambda/100. The photodiode array samples intensities over a 32 x 32 matrix in the interference pattern as the length of the reference arm is varied piezoelectrically. Using these data the minicomputer synchronously detects the phase at each of the 1024 points by a Fourier series method and displays the wavefront in contour and perspective plot on a storage oscilloscope in less than 1 min (Bruning et al. Paper WE16, OSA Annual Meeting, Oct. 1972). The array of intensities is sampled and averaged many times in a random fashion so that the effects of air turbulence, vibrations, and thermal drifts are minimized. Very significant is the fact that wavefront errors in the interferometer are easily determined and may be automatically subtracted from current or subsequent wavefrots. Various programs supporting the measurement system include software for determining the aperture boundary, sum and difference of wavefronts, removal or insertion of tilt and focus errors, and routines for spatial manipulation of wavefronts. FFT programs transform wavefront data into point spread function and modulus and phase of the optical transfer function of lenses. Display programs plot these functions in contour and perspective. The system has been designed to optimize the collection of data to give higher than usual accuracy in measuring the individual elements and final performance of assembled diffraction limited optical systems, and furthermore, the short loop time of a few minutes makes the system an attractive alternative to constraints imposed by test glasses in the optical shop.

Digital wavefront measuring interferometer for testing optical surfaces and lenses

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.

http://diposit.ub.edu/dspace/bitstream/2445/118388/1/664334.pdf

Roadmap on optical security

This paper presents an overview of the potential of free space optical technology in information security, encryption, and authentication. Optical waveform posses many degrees of freedom such as amplitude, phase, polarization, spectral content, and multiplexing which can be combined in different ways to make the information encoding more secure. This paper reviews optical techniques for encryption and security of two-dimensional and three-dimensional data. Interferometric methods are used to record and retrieve data by either optical or digital holography for security applications. Digital holograms are widely used in recording and processing three dimensional data, and are attractive for securing three dimensional data. Also, we review optical authentication techniques applied to ID tags with visible and near infrared imaging. A variety of images and signatures, including biometrics, random codes, and primary images can be combined in an optical ID tag for security and authentication.

Optical Techniques for Information Security

Digitally sampled in-line holograms may be linearly filtered to reconstruct a representation of the original object distribution, thereby decoding the information contained in the hologram. The decoding process is performed by digital computation rather than optically. Substitution of digital for optical decoding has several advantages, including selective suppression of the twin-image artifact, elimination of the far-field requirement, and automation of the data reduction and analysis process. The proposed filter is a truncated series expansion of the inverse of that operator that maps object opacity function to hologram intensity. The first term of the expansion is shown to be equivalent to conventional (optical) reconstruction, with successive terms increasingly sup-pressing the twin image. The algorithm is computationally efficient, requiring only a single fast Fourier transform pair.

Digital decoding of in-line holograms

Particle tracking is a fundamental technique for investigating a variety of biophysical processes, from intracellular dynamics to the characterization of cell motility and migration. However, observing three-dimensional (3D) trajectories of particles is in general a challenging task in classical microscopy owing to the limited imaging depth of field of commercial optical microscopes, which represents a serious drawback for the analysis of time-lapse microscopy image data. Therefore, numerous automated particle-tracking approaches have been developed by many research groups around the world. Recently, digital holography (DH) in microscopy has rapidly gained credit as one of the elective techniques for these applications, mainly due to the uniqueness of the DH to provide a posteriori quantitative multiple refocusing capability and phase-contrast imaging. Starting from this paradigm, a huge amount of 3D holographic tracking approaches have been conceived and investigated for applications in various branches of science, including optofluids, microfluidics, biomedical microscopy, cell mechano-trasduction, and cell migration. Since a wider community of readers could be interested in such a review, i.e., not only scientists working in the fields of optics and photonics but also users of particle-tracking tools, it should be very beneficial to provide a complete review of state-of-the-art holographic 3D particle-tracking methods and their applications in bio-microfluidics.

Recent advances in holographic 3D particle tracking

Digital holography is a technique that permits digital capture of
holograms and subsequent processing on a digital computer. This
paper reviews various applications of this technique. The presented
applications cover three-dimensional (3-D) imaging as well as several
associated problems. For the case of 3-D imaging, optical and
digital methods to reconstruct and visualize the recorded objects
are described. In addition, techniques to compress and encrypt 3-D
information in the form of digital holograms are presented. Lastly,
3-D pattern recognition applications of digital holography are discussed.
The described techniques constitute a comprehensive approach
to 3-D imaging and processing.

Three-Dimensional Imaging and ProcessingUsing Computational Holographic Imaging

We present a technique to estimate the pose of a three-dimensional object from a two-dimensional view. We first compute the correlation between the unknown image and several synthetic-discriminant-function filters constructed with known views of the object. We consider both linear and nonlinear correlations. The filters are constructed in such a way that the obtained correlation values depend on the pose parameters. We show that this dependence is not perfectly linear, in particular for nonlinear correlation. Therefore we use a two-layer neural network to retrieve the pose parameters from the correlation values. We demonstrate the technique by simultaneously estimating the in-plane and out-of-plane orientations of an airplane within an 8-deg portion. We show that a nonlinear correlation is necessary to identify the object and also to estimate its pose. On the other hand, linear correlation is more accurate and more robust. A combination of linear and nonlinear correlations gives the best results.

Pose estimation from a two-dimensional view by use of composite correlation filters and neural networks

The depth of field of optical systems can be extended with a phase mask placed in the pupil. We propose to design this
phase mask through a heuristic numerical optimization. We describe two different criteria to illustrate the technique. We
present the obtained phase profiles and we show that they have good properties regarding the extension of the depth of
field.

Increasing the depth of field of imaging systems with numerically optimized phase masks

We propose to use integral images to reconstruct and recognize three-dimensional (3D) scenes in a computer. A stereo-matching algorithm is applied to integral images in order to extract the depth information. This information is used to digitally reconstruct the 3D scenes. A numerical 3D correlation is then computed between various reconstructed scenes. We demonstrate the reconstruction and correlation results from experimental integral images. We propose to use a nonlinear correlation for better discrimination and we present the successful recognition and 3D localization of an object in a 3D scene. We finally compare the discrimination of two- and three-dimensional correlations.

Three-dimensional object visualization and recognition based on computational integral imaging

The two-dimensional view, obtained with a camera, of a three-dimensional (3-D) object varies with the 3-D orientation of this object, complicating the recognition task. In this work we address the problem of estimating the pose of a 3-D object knowing only a 2-D projection. The proposed technique is based on a combination of synthetic-discriminant-function filters and neural networks. We succeed in estimating two orientations: in-plane and out-of-plane rotations within a 8 degree square range.

Yann Frauel

Papers

Three-Dimensional Imaging and ProcessingUsing Computational Holographic Imaging

Pose estimation from a two-dimensional view by use of composite correlation filters and neural networks

Increasing the depth of field of imaging systems with numerically optimized phase masks

Three-dimensional object visualization and recognition based on computational integral imaging

Pose estimation combining synthetic-discriminant-function filters and neural networks