4 - Nonlinear Optical Phase Conjugation

The first quarter of 2007 started as an active season for the real-time image processing community. The annual SPIE Conference on Real-Time Image Processing, which was held in San Jose, CA, highlighted recent developments in this growing branch of image processing. As one of the few regular events with its focus matching that of JRTIP, one witnessed the growth in applications of real-time image and video processing. The conference served as an effective instrument to foster the dissemination of recent results and to leverage the cohesion of the real time image processing community. The preparations for the next SPIE Conference on Real-Time Image Processing in 2008 are already progressing and its call-for-papers is included at the end of this issue. In 2007, JRTIP is going to seek involvement of more scientists in the field of real-time image and video processing. After 1 year of operation, the editors-in-chief have started to recruit additional associate editors to enlarge the expertise base of the editorial board, to solicit manuscript submissions from potential authors, and to make the reviewing process more efficient. Interested colleagues should contact the editors-in-chief expressing their Associate Editor candidature in support of one or more fields within the focus of JRTIP. In addition, they wish to encourage researchers to act as guest editors for special issues on any real-time image or video processing related topics of interest to them. It is possible to schedule two special issues in 2007, provided that a draft call-for-papers is sent to the editors-in-chief as soon as possible. The third issue of JRTIP presents four articles, two are survey papers and two are original research papers. These articles address different real-time image processing perspectives ranging from algorithmic to hardware issues. The first survey paper deals with the real-time filtering in 2D and 3D using ordered statistics that are supported by a rich set of experimental results derived from several algorithmic variations. Performance measurements are used to demonstrate the effect of noise suppression and preservation of fine image structures and to show the DSP implementation of denoising filters. The second survey paper introduces a non-invasive analysis of dynamic processes in living cells using microscopic computer vision techniques for understanding the interactions between cellular and molecular functional units and for providing novel insights into the cell biology. The third article is an original research paper covering a machine vision application involving vision-based position sensing for aerial refueling of unmanned aerial vehicles in real-time. A full image processing chain is investigated and tested in a MATLAB/Simulink soft real-time environment as well as in a Linux/RTAI hard real-time environment. The feasibility of the real-time implementation is shown using off-the-shelf commercial hardware. The fourth article is another original research paper describing a hardware architecture for real-time fractal coding for image compression. The introduced parallel hardware architecture contains units for domain M. F. Carlsohn (&) Ingenieurberatung fur Computer Vision und Bildkommunikation, Am Heiddamm 36g, 28355 Bremen, Germany e-mail: matthias.carlsohn@t-online.de

The application of principal component analysis to simplify the classification of pixels of images obtained with the help of unmanned aerial vehicle for the organization received information processing in real time, and simplify the process of orientation of unmanned aerial vehicle on the ground. We consider the neural network of the principal component analysis. The results of experiments showing the effectiveness of such use of principal component analysis.

In this paper we introduce a new real-time parallel image processing system based on an IEEE1394-based PC-cluster. PC-clusters are becoming standard tools in the field of computer vision, especially for real-time multi-view image processing. To reduce the cost of system construction, we have employed IEEE1394 bus as a base network of the PC-cluster. IEEE1394 bus has the throughput of up to 400Mbps, which is large enough to transfer uncompressed video data, and supports not only asynchronous data transfer but isochronous one, which is inevitable for real-time data transfer. In addition, since we have several products of IEEE1394-based digital cameras, we can easily integrate real-time image processing system including camera systems. This paper presents an overview of our real-time parallel image processing system on an IEEE1394based PC-cluster, referring to basic features of IEEE1394. We also show some experimental results to evaluate the performance of the PC-cluster, comparing it with the performance of our previous Myrinet-based PC-cluster. Then we show a prototypical application of the system, which is real-time volume reconstruction from multi-view images by visual cone intersection method.

It is well known how selective attention biases information processing in real time, but few work investigates its aftereffects. To examine perceptual aftereffect after prolonged attention to a monocular pathway, movie images played normally were presented to normal adult’s one eye (attended eye), while movie images of the same episode but played backwards were presented to the opposite eye (unattended eye). One hour of watching this dichoptic movie caused a shift of perceptual ocular dominance towards the unattended eye. Interestingly, the aftereffect positively correlated with the advantage of neural activity for the attended-eye over unattended-eye signals at the frontal electrodes measured with steady-state visual evoked potentials (SSVEP). Moreover, the aftereffect disappeared when interocular competition was minimized during adaptation. These results suggest that top-down eye-specific attention can induce ocular dominance plasticity through binocular rivalry mechanisms. The ocular-opponency-neuron model for binocular rivalry is then discussed for possible mechanisms underlying the present findings.

It is well known how selective attention biases information processing in real time, but few work investigates the aftereffects of prolonged attention, letalone the underlying neural mechanisms. To examine perceptual aftereffect after prolonged attention to a monocular pathway, movie images played normally were presented to normal adult's one eye (attended eye), while movie images of the same episode but played backwards were presented to the opposite eye (unattended eye). One hour of watching this dichoptic movie caused a shift of perceptual ocular dominance towards the unattended eye. Interestingly, the aftereffect positively correlated with the advantage of neural activity for the attended-eye over unattended-eye signals at the frontal electrodes measured with steady-state visual evoked potentials. Moreover, the aftereffect disappeared when interocular competition was minimized during adaptation. These results suggest that top-down eye-specific attention can induce ocular dominance plasticity through binocular rivalry mechanisms. The present study opens the route to explain at least part of short-term ocular dominance plasticity with the ocular-opponency-neuron model, which may be an interesting complement to the homeostatic compensation theory.

Retina Implants for blind subjects with retinal degenerative disorders require retinal information processing in real time with many individually tunable spatiotemporal filters with antagonistic receptive field properties (RF filters). We describe a RF filter structure, function and tuning mechanism as well as the implementation of tunable RF filters in real time on a digital signal processor (DSP) for currently developed retina implants. Each tunable RF filter is capable of implementing a large variety of spatial and temporal mapping operations from spatiotemporal light patterns at the assigned array of photo sensors onto a corresponding single asynchronous stimulation pulse sequence at the output. For this purpose, our tunable RF filters incorporate a wide range of typical RF properties of primate retinal ganglion cells in their functional space. A number of spatial and temporal parameters in the RF filter algorithm is specified in order to assure smooth maneuvering within the functional space as well as tuning of a given RF filter to the desired RF function of a given contacted ganglion cell in a learning phase. Typical spatial and temporal information processing properties of tunable RF filters are presented.

We embodied networks of cultured biological neurons in simulation and in robotics. This is a new research paradigm to study learning, memory, and information processing in real time: the Neurally-Controlled Animat. Neural activity was subject to detailed electrical and optical observation using multi-electrode arrays and microscopy in order to access the neural correlates of animat behavior. Neurobiology has given inspiration to AI since the advent of the perceptron and consequent artificial neural networks, developed using local properties of individual neurons. We wish to continue this trend by studying the network processing of ensembles of living neurons that lead to higher-level cognition and intelligent behavior.KeywordsNeural ActivityDynamical System TheoryIntelligent BehaviorNeural FiringAverage Firing RateThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

In real-time image processing, the demand for efficient solutions has surged with the proliferation of applications spanning from autonomous vehicles to medical diagnostics. This study addresses the imperative need for accelerated machine learning algorithms to enhance the processing speed of image-related tasks. The research focuses on leveraging Field-Programmable Gate Arrays (FPGAs) to implement hardware acceleration, exploiting their parallel computing capabilities. The advent of machine learning in image processing has revolutionized various industries, yet real-time applications encounter computational bottlenecks. This research delves into hardware acceleration using FPGAs to overcome these constraints, offering a novel approach to expedite machine learning algorithms. Traditional software implementations of machine learning algorithms often fall short in meeting real-time processing requirements. This research aims to bridge this gap by exploring FPGA-based hardware acceleration, addressing the performance limitations hindering the seamless integration of machine learning into real-time image processing systems. While existing literature acknowledges the potential of FPGA-based acceleration, a comprehensive exploration of its application for real-time image processing is lacking. This research fills the void by presenting a detailed method and empirical results, contributing to the limited body of knowledge on FPGA-accelerated machine learning in the of image processing. The study employs a systematic approach, integrating machine learning algorithms onto FPGAs through hardware description languages. The implementation is optimized to exploit parallelism inherent in FPGAs, resulting in a tailored hardware solution for real-time image processing. Comparative analyses against software implementations provide insights into the performance gains achieved. The experimental results demonstrate a significant enhancement in processing speed, validating the efficacy of FPGA-based hardware acceleration for machine learning algorithms in real-time image processing applications.

Control and manipulation of electromagnetic waves has reached a new level with the recent understanding of topological states of matter. These metamaterials have the potential to revolutionize many areas in traditional electromagnetic design, from highly robust cavities to small footprint waveguides. Much of the past literature has been on the cutting edge of condensed matter physics, but there is now ample opportunity to explore their usage for practical microwave and optical devices. To assist the beginner, in this Tutorial we give a basic introduction to the essential concepts of topological phenomena in electromagnetic systems, including geometric phases, topological invariants, pseudospin states, and the integer/valley/spin quantum Hall effects. Our focus is on engineered photonic topological insulators (PTIs) in two-dimensional systems. We highlight methods for characterizing such structures and how they result in unique wave-guiding properties. In addition, we provide recipes on how to realize PTIs using photonic crystals and metasurfaces, examine differences between different types of PTIs, and discuss limitations and advantages of some of the existing enabling platforms.

Metasurfaces, the two-dimensional counterpart of metamaterials, have caught\ngreat attention thanks to their powerful capabilities on manipulation of\nelectromagnetic waves. Recent times have seen the emergence of a variety of\nmetasurfaces exhibiting not only countless functionalities, but also a\nreconfigurable response. Additionally, digital or coding metasurfaces have\nrevolutionized the field by describing the device as a matrix of discrete\nbuilding block states, thus drawing clear parallelisms with information theory\nand opening new ways to model, compose, and (re)program advanced metasurfaces.\nThis paper joins the reconfigurable and digital approaches, and presents a\nmetasurface that leverages the tunability of graphene to perform beam steering\nat terahertz frequencies. A comprehensive design methodology is presented\nencompassing technological, unit cell design, digital metamaterial synthesis,\nand programmability aspects. By setting up and dynamically adjusting a phase\ngradient along the metasurface plane, the resulting device achieves beam\nsteering at all practical directions. The proposed design is studied through\nanalytical models and validated numerically, showing beam widths and steering\nerrors well below 10 degrees and 5% in most cases. Finally, design guidelines\nare extracted through a scalability analysis involving the metasurface size and\nnumber of unit cell states.\n

Chiral and bianisotropic metamaterials, where coupling of magnetic and electric phenomena plays an important role, offer advanced possibilities for the control and manipulation of electromagnetic waves. Such a control is particularly useful in the THz and far-IR region where natural materials do not show strong response and thus they are not offered as components for a direct realization of electromagnetic wave manipulation. Among the most useful and important capabilities of chiral and bianisotropic metamaterials is the advanced control of the wave polarization that they offer, including giant polarization rotation, conversion, filtering, absorption, etc. In this paper we review our recent work demonstrating some of those capabilities, in a variety of structures, both planar and 3D-bulk ones. The structures presented show, among others, large optical activity, tunable/switchable wave ellipticity, and polarization-dependent asymmetric transmission.

Nanophotonic devices, composed of metals, dielectrics, or semiconductors, enable precise and high-spatial-resolution manipulation of electromagnetic waves by leveraging diverse light–matter interaction mechanisms at subwavelength length scales. Their compact size, light weight, versatile functionality and unprecedented performance are rapidly revolutionizing how optical devices and systems are constructed across the infrared, visible, and ultraviolet spectra. Here, we review recent advances and future opportunities of nanophotonic elements operating in the ultraviolet spectral region, which include plasmonic devices, optical metamaterials, and optical metasurfaces. We discuss their working principles, material platforms, fabrication, and characterization techniques, followed by representative device applications across various interdisciplinary areas such as imaging, sensing and spectroscopy. We conclude this review by elaborating on future opportunities and challenges for ultraviolet nanophotonic devices.

The physics of high-index dielectric materials has led to the appearance of all-dielectric meta-photonics, which provides a low-loss platform for effective subwavelength control and manipulation of electromagnetic waves. This paper presents a metasurface that supports ultrahigh-Q chiroptical resonance at the gamma point. We propose a design that manipulates quasi-bound states in the continuum (q-BICs) through in-plane and out-of-plane perturbations to achieve unit chirality in a lossless planar metasurface. By varying the in-plane asymmetry parameter, we enhance the quality factor while keeping the resonant wavelength stable. The design is done in such a way that by changing the in-plane asymmetry parameter to increase the quality factor, the resonant wavelength remains stable. The design achieves a Q-factor of 5.9×106 and a circular dichroism (CD) of 0.99 at visible wavelengths by breaking the out-of-plane symmetry. Our findings open up new possibilities for precise chiral sensing technologies.

Image edge detection significantly preserves the important structural properties in an image, while reducing the amount of data and filters out useless information. Since edge detection is in the forefront of image processing for object detection, it is crucial to have a good understanding of edge detection algorithms. This paper introduces the Canny edge detection algorithm and the implementation of the Canny edge-detection algorithm on an FPGA. Custom implementation in FPGAs, using a combination of hardware and software co-design, allows real-time processing, providing a good trade-off between performance and flexibility. The results show that FPGAs are good alternatives, which can be used to off-load these computationally-intensive and repetitive functions as co-processors and the capability of supporting canny edge detection for real-time image processing.

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