Metal nanoparticles are used for different applications in holographic configurations. The metal nanoparticles are placed
close to an object and encode it by a time varying random mask. A decoding mask is computed and used to obtain super-resolution digital hologram and eliminate the twin image and DC from a digital hologram. The method is also shown to be applicable for other optical methods.
In a large plurality of applications, imaging quality is significantly reduced due to existence of static or time-varying random perturbation media. An example of such a medium can be a diffusive window through which we wish to image an object located behind, and not in proximity to, the window. Another example can be localized flow of turbulence (above hot surfaces such as black roads) or of aerosols distorting the imaging resolution of objects positioned behind the perturbation. We present a new deblurring approach for obtaining highly resolved imaging of objects positioned behind static or time-varying random perturbation media. The proposed approach for extraction of the high spatial frequencies is based on iterative computation similar to the well-known Gerchberg-Saxton algorithm for phase retrieval. By focusing our camera onto three planes positioned between the imaging camera and the perturbation, we are able to retrieve the phase distribution of those planes and then reconstruct the intensity of the object by numerical free space propagation of this extracted complex field to the estimated position of the object.
In the last third of the 20<sup>th</sup> century, Fuzzy Logic has risen from a mathematic concept to an applicable approach in soft computing. The approach of optical implementation of fuzzy inferencing was given by the authors in a previous work, giving an extra weight to applications with two dominant inputs. In this work the authors introduce a real time optical rule generator for the dual input fuzzy inference engine. Next, the concept of constructing a set of rules from a given data is discussed. Finally, the authors show ways to implement this procedure optically. The discussion is accompanied by an example illustrating the tranformation from raw data into fuzzy set rules.
The problem of frequency response degradation while stepping out of the focal plane of an optical setup is well known. In this work the authors suggest a novel method for decreasing this effect. The main principle is to assign different weights to different out-of-focus planes and design a phase only filter for each plane, then combining the filters with respect to the weights. The weights of the different planes increase as the distance from the focal plane increases and the exact weights are determined by a fuzzy logic inference engine. The fuzzy logic tool grades the different weighing sets according to the average width of the optical transfer functions and according to the deviation between function's widths.
One of the main problems in imaging systems is the difficulty in placing the output CCD exactly at the imaging plane. This causes the so-called defocus effect in which the optical transfer function in the output plane is worse than expected. This leads to loss of detail and possible contrast conversion. A good solution for solving the defocus problem in a specific plane would be to place a phase-only filter right after (or before) the lens of the imaging setup. This filter can be designed to cancel the defocusing effect in the specific plane. The design of a filter that will yield good images for a range of planes at different distances from the actual imaging plane requires a more complex approach. In this work the authors introduce a novel method for designing the filter using Fuzzy Logic principles. The Fuzzy Logic inference engine accepts as input data a set of filters; each designed for good results in a specific region, and collaborates them to produce a single phase-only filter. The optical transfer function of the combined filter in the various regions is presented to demonstrate the improvement in limiting defocus.
The allocation of CPU time and memory resources, are well known problems in organizations with a large number of users, and a single mainframe. Usually the amount of resources given to a single user is based on its own statistics, not on the entire statistics of the organization therefore patterns are not well identified and the allocation system is prodigal. In this work the authors suggest a fuzzy logic based algorithm to optimize the CPU and memory distribution between the users based on the history of the users. The algorithm works separately on heavy users and light users since they have different patterns to be observed. The result is a set of rules, generated by the fuzzy logic inference engine that will allow the system to use its computing ability in an optimized manner. Test results on data taken from the Faculty of Engineering in Tel Aviv University, demonstrate the abilities of the new algorithm.
The paper reviews the usage of modular optical systems for nonlinear data processing and manipulation. The paper starts with presenting an algorithm for multi stage image processing in which a derivation of instillation having N stages of binary filters is used in order to generate general transformation and then to apply it for pattern recognition. Then, optical configuration that implements second order of Volterra operator is used for non-linear control processes. In the third part of the paper a fuzzy logic based algorithm is used in order to optimize the design of multi stage interconnection network (MIN). The optimization is done in a sense of minimal number of switching modules and minimal number of switching operations per routing stage. In the fourth part we present a technique for optical generation of fuzzy based rules. In the last part of the paper we suggest a fuzzy logic based technique for improving the depth of focus in an imaging system using as single-phase only filter. All the suggested pmcessing configurations are backed up with either simulations or experimental verifications.
State of the art fuzzy-logic based control is mainly implemented using electronic hardware or computer software. This requires interpretation of fuzzy logic concepts such as membership functions and fuzzy based rules, all of which have been thoroughly studied. However, the 2-D light-speed abilities of optical processing enables direct implementation of dual-input fuzzy logic inference engines. The optical equivalent of the membership function is generated in a straightforward manner and the same applies to rule tables and combination rules. Diffractive optical elements allow these optical inference engines to be compact in size and high on efficiency. This is done by binary optics and phase-only elements. Using the 2-D work-plane of optics, the ability of simple control over the wavelength and the polarization of light and the properties of diffractive elements, such an engine can deal with higher order data and lead the way to fast and dynamic fuzzy inferencing.
Image processing in general and optical image processing in particular require very accurate and very complex processors. Such processors are sometimes difficult to manufacture and expensive to purchase. They also might be non flexible in their design. The principle of generating a single processor by use of several simpler processor-modules in cascade (and/or in parallel) is quite familiar. However, in optics this approach is mainly used for filtering in the Fourier or fractional Fourier planes. In this work the authors introduce multi-stage optical processing in the Fresnel plane. Using a small number of binary masks (either amplitude or phase) along the path of the light, one may process the incoming beam in the same manner as using a single high-resolution complex mask. The authors present an algorithm for establishing the binary processors and introduce the results obtained by this approach. An important application of this technique is the field of image recognition. Simulations demonstrate that minor manipulations on the input, affect the output plane significantly. On the other hand, hiding fractions of the input pattern hardly influence the output whereas the obtained effect reveals information regarding the flaw inserted within the input pattern.
A new method for implementing a general linear system using multi-stage architecture was recently published. In this method an iterative procedure determines the architecture to be used to simulate a linear system with a small number of stages and with a small error. Multi-stage IC architectures allow easy switching between different channels. There are many known algorithms for optimizing the interconnection net in terms of minimizing the number of alterations required to switch form one set of channels to a similar set. In optics, however, there exists the probe of implementation. Not all the routing architectures can be easily constructed optically and there is an advantage in implementing routing architectures with a symmetric structure. Therefore an optical setup might be limited to the use of non-blocking setup is known. Thus, the optimization that can be made refers to a specific set of input-output connections and results in finding the smallest number of routing stages required to achieve the full permutation set. In this work we present an algorithm for optimizing the routing scheme. We also show an algorithm for minimizing the number of changes required in a given routing scheme while shifting form one input-output connection to a similar connection.