Abstract:
A system and method for real time optical processing of images by simultaneous combining the variable set of images in selected spectral intervals. Spectral images at a plurality of spectral intervals are collected if needed. According to criteria established in a particular application, coefficients for an optimal combination of selected spectral intervals are calculated. Positive and negative images are continuously captured through the optical filter whose transmission spectrum adjusted to positive and negative coefficients of the optimal combination. Negative images are subtracted from positive images and a sequence of resulting optimized images is displayed in real time.

Description:
BACKGROUND OF THE INVENTION 
     1. Field of Invention 
     The present invention is directed to a real-time opto-electronic image processor. In particular, the present invention is directed to a system and method for real-time optical enhancement of imaging object-to-background contrast. 
     2. Description of Related Art 
     Presently, it is difficult to track objects, such as endangered wildlife, against varying backgrounds, such as trees or fields. Additionally, aircraft, target and munitions tracking at, for example, flight test center ranges, is becoming increasingly challenging. As the pollution of the surrounding areas of DoD test ranges increases, more pollution is deposited in the air. This pollution causes an increase of airborne particulate, haze, and other phenomenon that reduce visibility. Additionally, aircraft and munitions are moving at higher velocity and some objects are stealthy, making them difficult to track. 
     A current approach to image enhancement for object tracking involves the use of sensitive infrared cameras and range-gated laser camera systems. Unfortunately, these devices are very expensive. Infrared cameras typically cost tens of thousands of dollars and the cost of range-gated laser camera systems is even more. These approaches are cost prohibitive for intensive use at numerous sites. Another problem with existing systems is that they are active and they “light-up” a target or object. That is, they project light or audio signals onto the object for detection. These projections can inform the object that it is being tracked. 
     FIG.  1 ( a ) illustrates an exemplary observation scenario of a flying target or object  110 . The scenario includes the object  110 , a sun  120 , a cloud  130 , a tracker  140 , reflected light  150  and scattered light  160 . The tracker  140  collects both light scattered  160  in the atmosphere and reflected  150  from an object  110 . Therefore, the visibility of the object  110  visibility depends strongly on atmospheric conditions. 
     The enhancement of object visibility can be achieved by rejecting the signal in those portions of the light spectrum in which the signal from the object  110  is weak but there is a strong signal from the background. Because of strong Rayleigh scattering of blue light, it can be useful to reject the blue portion of the visible spectrum. Although computer algorithms can be used to calculate how these effects influence light propagation in the atmosphere, unfortunately all calculations assume some atmospheric model that can be significantly different from the atmospheric conditions at the point of observation. 
     SUMMARY OF THE INVENTION 
     The present invention provides a method and a system for a real-time opto-electronic image processor that uses an Acousto-Optic Tunable Filter (AOTF). Image processing can include, for example, object contrast enhancement, object identification, object detection, object tracking or the like. For object contrast enhancement, spectral images at a plurality of spectral intervals are collected. Object and background spectral parameters are established. Filter coefficients are calculated based upon the object and background spectral parameters for maximizing contrast between the object and background in real time. An acousto-optic filter transmission spectrum is adjusted based upon the calculated filter coefficients. An image is captured through the optical filter corresponding to the adjusted acousto-optic filter transmission spectrum. 
     This system can enhance the visibility of remote objects under test in adverse weather conditions. When installed on an optical target tracker, this system can automatically adjust its spectral transmission in such a way that the intensity of the background illumination will be reduced significantly while providing minimum reduction of the light reflected from a detected object. 
     An AOTF under computer control is capable of quickly varying the shape of its spectral transmission curve in the entire visible range. The system automatically analyzes the spectral signatures of the background and of the selected object of interest. It calculates a spectrally matched filter for background suppression and object contrast enhancement. This filtered data then goes directly into the optical channel with minimal computer image processing. The resulting image with the enhanced object contrast can be displayed in real time on a common computer monitor and can be recorded by a VCR. 
     The system makes flight testing of military equipment more informative and less expensive. It reduces flight test program dependence on weather conditions and allows for the collecting of more data by providing real time images with enhanced object visibility. 
     The system and method of the present invention is capable of automatic real-time accommodation for particular atmospheric conditions in a point of observation. To make this accommodation, initial spectral information about the object and background may be collected. The proposed system can then track an object and provide real time enhanced images until severe variations in object or background spectra occur. Then, new data may be collected and the cycle of observation can be continued. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS 
     The preferred embodiments of the present invention will be described with reference to the following figures, wherein like numerals designate like elements, and wherein: 
     FIG.  1 ( a ) illustrates an exemplary observation scenario of a flying target or object; 
     FIG.  1 ( b ) illustrates another exemplary observation scenario of a flying target or object; 
     FIG. 2 is an exemplary block diagram of an object enhancer according to one embodiment; 
     FIG. 3 is an exemplary block diagram of an object enhancer according to another embodiment; 
     FIGS. 4 and 5 are exemplary illustrations of operating principles of a TN LC achromatic rotator; 
     FIG. 6 is an exemplary illustration of a system that shows a principle of operation of an AOTF; 
     FIG. 7 is an exemplary wave vector diagram of acousto-optic interaction in an AOTF; 
     FIG. 8 is an illustration of an exemplary flowchart of system performance of an object enhancer according to one embodiment; 
     FIG. 9 is an exemplary illustration of a flowchart for monitoring a background of an image in an object enhancer; 
     FIG. 10 is an illustration of an exemplary flowchart of another embodiment of system performance of an object enhancer; 
     FIG. 11 is an illustration of an exemplary approach to object enhancement based on differences in spectra of light reflected from an object and of scattered light; 
     FIG. 12 is an exemplary illustration of spectrally matched filtering for a non-uniform background; 
     FIG. 13 is an exemplary illustration of the distribution of background intensity along a filter direction; 
     FIG. 14 is an exemplary illustration of a graph of an AOTF transmission curve; and 
     FIG. 15 is an exemplary illustration of a timing diagram of an experimental object enhancer performance. 
    
    
     DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS 
     FIG.  1 ( b ) illustrates another exemplary observation scenario of a flying target or object  110 . The scenario includes the object  110 , a sun  120 , a cloud  130 , a tracker  140 , reflected light  150 , scattered light  160  and an object enhancer  170 . As illustrated in FIG.  1 ( a ), the tracker  140  collects both light scattered  160  in the atmosphere and reflected  150  from an object  110 . The object enhancer  170  in FIG.  1 ( b ) can work in conjunction with or be integrated into the tracker  140  to filter out the scattered light  160  in real time as disclosed below. Additionally, the object enhancer  170  may be used separately from the tracker  140  for purposes such as image processing, object identification, contrast enhancement, or the like. 
     FIG. 2 is an exemplary block diagram of an object enhancer  900 , such as object enhancer  170 , according to one embodiment. The object enhancer  900  can include a telescope lens  905 , a field lens  910 , beam splitters  915  and  955 , and AOTF  920 , matching optics  925  and  950 , CCD cameras  930  and  945 , a computer  935 , a TV monitor  940 , focusing optics  960  and a spectrometer  965 . 
     In operation, the object enhancer  900  can receive a signal, such as an image, through the telescope lens  905  and the field lens  910 . The signal can be split by beam splitters  915  and  955 . The signal can be filtered through AOTF  920  to matching optics  925 . The signal can then be received by a CCD camera  930  and sent to a computer  935 . The signal can also be sent from beam splitter  955  through matching optics  950  to the CCD cameral  945  and then to the TV monitor  940 . The signal can further be sent from beam splitter  955  through focusing optics to a spectrometer  965 . The spectrometer  965  can then send a signal to the computer  935 . 
     FIG. 3 is an exemplary block diagram of an object enhancer  1000 , such as object enhancer  120  according to another embodiment. This embodiment allows the use of light polarization as an additional discriminate to enhance the object contrast. The object enhancer  1000  can include an objective lens  1010 , a liquid crystal achromatic rotator of light polarization (LC ARLP)  1015 , an AOTF  1020 , matching optics  1025 , a photodetector such as a CCD camera  1030 , a LC ARLP driver  1035 , an AOTF driver  1040 , a frame grabber  1045 , a computer  1050  and a monitor  1055 . 
     In operation, the AOTF  1020  can receive a signal such as an image through front imaging optics such as the objective lens  1010  and the LC ARLP  1015 . The AOTF  1020  can transmit a signal such as a filtered image through posterior optics such as the matching optics  1025  to match a filtered image with a photodetector array such as the CCD camera  1030 . The frame grabber  1045  can digitize a captured image. The computer  1050  can control the operation of the drivers and the frame grabber. An example AOTF  1020  is a non-collinear TeO 2  AOTF that may have an angular aperture of 6° and a linear aperture of 8 mm×8 mm. The AOTF  1020  can be designed to provide full compensation for the dispersion of refractive indices in the TeO 2  crystal. Therefore, no image displacement will occur with variation in light transmission wavelength. This object enhancer  1000  can operate efficiently both in the visible light range and in the near-infrared range. 
     The computer  1050  may include a PCI computer board with an array of sixteen oscillators to drive the AOTF  1020 . These oscillators can simultaneously generate a driving signal with a complex spectrum in a frequency range of 100-200 MHz. A switching time of this array of generators can be 2 μs. The amplitude and frequency of each oscillator can be controlled with 14-bit resolution. This exemplary board can be suitable for extension to 32 frequency channels. Therefore, any desired linear combination of transmission intervals in an octave-wide range can be created or changed in the AOTF  1020  within microseconds. 
     For the LC ARLP  1015 , light polarization can be rotated in wide spectral intervals. By combining birefringent materials, it is possible to obtain near-perfect 90 degree rotation, not only at one wavelength, but also in a wide spectral interval. Achromatic rotators with a twisted nematic (TN) LC layer can be used for the LC ARLP  1015  because its performance is independent of the light wavelength. It can be driven by a low voltage, rectangular, electric signal with alternating polarity generated by, for example, an outboard board such as the LC ARLP driver  1035  under computer  1050  control. Without the voltage, the LC ARLP  1015  rotates the polarization of the incident light so the initially vertical polarized light becomes horizontally polarized. This polarization can be used for spectral filtering by the AOTF  1020 . The applied voltage cancels rotation of light polarization so initially horizontal polarized light is used for the spectral filtering. The LC ARLP  1015  response to the applied voltage is about 1 ms, while transition to the state without applied voltage takes about 6 ms. A similar rotator of light polarization at 45 degrees can be added to the system to perform the operation with three components of the Stokes vector. 
     Thus, a complex spectral image of a scene taken at one of two orthogonal polarizations can be captured by a commercial black and white CCD camera  1030  that can operate in an interface mode at 30 frames/second. An analog composite TV signal from the CCD camera  1030  can be digitized by the frame grabber  1045  and then can be used for image processing and displaying. 
     FIGS. 4 and 5 are exemplary illustrations of the operating principles of a TN LC achromatic rotator  400 . The rotator  400  can include liquid crystal (LC)  410  and a voltage source  420 . Such liquid crystal rotators of polarization are currently commercially available. As illustrated in FIG. 4, when no electric field is applied by the voltage source  420 , the LC molecules  410  create a twisted chain that rotates light polarization. As illustrated in FIG. 5, when an electric field is applied by the voltage source  420 , the LC  410  are reoriented following the direction of the applied electrical field, and no rotation of light polarization occurs. 
     FIG. 6 is an exemplary illustration of a system  1100  that shows the principle of operation of an AOTF such as AOTF  1020 . The system  1100  can include incident light  1110 , a first polarizer  1120 , an acoustic absorber  1130 , a piezoelectric transducer  1140 , a second polarizer  1150  and selected light  1160 . In operation, an image or incident light can enter the system through the first polarizer  1120 . The piezoelectric transducer  1140  can create a traveling acoustic wave  1170 . The wavelength of the acoustic wave  1170  is defined by the frequency of the driving signal applied to the piezoelectric transducer  1140 . Because of an elasto-optic effect, the traveling acoustic wave creates a corresponding phase diffractive grating in crystal in the AOTF  1020 . The light portion of incident light with a wavelength defined by the period of the acoustically induced phase grating (i.e. by the frequency of the driving signal applied to the piezoelectric transducer) is diffracted by this grating. The process of light diffraction on the acoustically induced phase grating is accommodated by the rotation of polarization of the diffracted light 90°. The diffracted light  1160  passes through the polarizer  1150 . By applying a driving signal with a complex spectrum, several gratings with different periods an amplitudes can be excited in the crystal simultaneously, thus allowing for the simultaneous light diffraction with several wavelengths. This operation of an AOTF illustrates how an acousto optic manipulation can be performed on an image in real time. 
     FIG. 7 is an exemplary wave vector diagram  1200  of acousto-optic interaction in an AOTF. Vector k i  represents the wave vector of an incident wave. Vector k d  represents the wave vector of a diffracted wave. Vector k a  represents the wave vector of an acoustic wave. To achieve the capability of providing spectral filtering of images, effective light diffraction at the selected light wavelength must be possible for divergent light beams. The possibility is met in the special geometry of light diffraction when tangents to the incident light  1240  (extraordinary beam in the crystal) and for the diffracted beam  1250  (ordinary beam in the crystal) are parallel. This diagram  1200  is a simple illustration of the resulting acousto-optic interaction between the illustrated waves. 
     FIG. 8 is an illustration of an exemplary flowchart  500  of the operation of an object enhancer system, such as, for example, object enhancer  900 . The flowchart  500  can be implemented using, for example, computer  935  for achieving object enhancement. FIG. 8 shows that spectrally matched filtering can be a linear operation. To accelerate system performance and achieve real time spectrally matched filtering, the unique capability of the AOTF can be utilized to transmit light in numerous spectral intervals simultaneously. This mode of AOTF operation can occur when its driving signal spectrum contains the proper combination of radio frequencies. The amplitude of the corresponding frequency in the driving signal spectrum can define the intensity of transmitted light at each wavelength. By choosing the proper set of frequencies and their amplitudes, the AOTF&#39;s transmission of any arbitrary given shape can be approximated. 
     FIG. 8 illustrates how, for example, data can be sent through a filter in real time for real time tracking of an object. Accordingly, the present invention provides a method for real time optical enhancement of imaging. Spectral images at a plurality of spectral intervals are collected. Object and background spectral parameters are established. Filter coefficients are calculated based upon the object and background spectral parameters for maximizing contrast between the object and background in real time. An acousto-optic filter transmission spectrum is adjusted based upon the calculated filter coefficients. An image is captured through the optical filter corresponding to a set of the calculated filter coefficients. 
     A related embodiment of the invention according to FIG. 8 provides a method for real time optical enhancement of imaging contrast. A translation from a real time image into a spectral representation of the image is created by using an acousto optic manipulation. The spectral representation of the image is processed into positive and negative discrete spectral coefficients to create a high contrast image determinant adaptive spectral filter. The spectral representation is translated back into a high contrast image. 
     Initiation of the flowchart begins in step  505 . In step  510 , initial wavelengths are chosen for analysis. Wavelengths λ 1 , λ 2 , . . . λ 3  can represent the number and position of spectral intervals and wavelengths Δλ 1 , Δλ 2 , . . . Δλ 3  can represent widths of each spectral interval. In step  515 , spectral images are collected in every initial wavelength chosen in step  510 . In step  520 , a spectral signature or area on a spectral image is targeted or entered for determination of the target vector in step  525 . In step  525 , the target vector is obtained from the spectral signature or from the area on a spectral image targeted in step  515 . In step  530 , the background vector is obtained. For example, the background vector may be obtained by sampling pixels of a specific area in an image of the background. 
     In step  535 , the filtering vector F can be created. For example, the filtering vector F can be created according to Equation (1) above or by other useful equations for calculating a filter. In step  540 , step the positive filter F +  is created. In step  545 , the image is grabbed through the positive filter F + . Physically, the filtering may be done by applying a driving signal with a complex frequency spectrum to an AOTF. Such a driving signal can be generated by a signal synthesizer or by an array of frequency generators. In step  550 , the negative filter F −  is created. In step  555 , the image is grabbed through the negative filter F − . Steps  540 - 555  may be repeated or implemented in varying order to obtain varying images through the different filters. Thus, the steps  535 ,  540  and  550  can calculate filter coefficients based upon object and background spectral parameters for maximizing contrast between the object and background in real time. 
     In step  560 , image subtraction is performed on the images grabbed at varying wavelengths through the filters. This subtraction may be performed using a processor or computer. In step  565 , the resulting image can be displayed and/or recorded. In step  570 , it is determined if the image has undergone a substantial change. For example, it is determined if the background has dramatically changed or if the object has left the area of view. If the image has not substantially changed, flow of the process returns to step  540  for continued processing and display of the image. If the image has substantially changed, flow of the process can return to step  510  for redetermination or reestablishment of initial parameters. Flow of the process may additionally return to, for example, steps  515 ,  520 ,  525 ,  530  or  540  depending on how much initial processing is desired to be redone. The determination of step  570  may be performed by monitoring of the background according to FIG.  9 . 
     FIG. 9 is an exemplary illustration of a flowchart  600  step for monitoring a background of an image in an object enhancer. Initiation of the flowchart begins in step  605 . In step  610 , step the object enhancer acquires a background spectrum. In step  620 , the object enhancer compares the background spectrum with a background vector B. In step  630 , the object enhancer determines if there are significant changes in the background based on the comparison performed in step  620 . If there are no significant changes, the object enhancer continues monitoring the background by returning to step  610 . If there are significant changes, the object enhancer can recollect the spectral images in step  640  and return to step  610  for additional monitoring. This monitoring process can be done concurrently with the filtering process of FIG. 5 to cause, for example, the recollection of the spectral images in step  520  when the background changes significantly. 
     FIG. 10 is an illustration of an exemplary flowchart  1300  of the operation of another embodiment of an object enhancer, such as, for example, object enhancer  1000  using a Spectrally Polarimetric Matched Filter (SPMF). A SPMF can be used in the object enhancer  1000  by using a combination of, for example, the LC ARLP  1015  and the AOTF  1020 . Initiation of the process begins in step  1305 . In step  1310 , the LC ARLP is turned off. In step  1015 , J spectral images are captured. In step  1320 , the LC ARLP is turned on. In step  1325 , another J spectral images are captured. In step  1330 , a target area is pinpointed. In step  1335 , background, target parameters, mean vector B, covariance matrix M and target vector T are calculated. These values can be calculated in accordance with the procedures outlined in FIGS. 11 and 12 and procedures further described below. In step  1340 , the SPMF is calculated by using F=M −1 T, F=F +   HP −F −   HP +F +   VP −F −   VP . The SPMF filter F consists of four portions F +   HP , F −   HP , F +   VP , and F −   VP , where HP means horizontal polarization, VP means vertical polarization, and + and − stand for positive and negative coefficients, respectively. Therefore, 4 frames are used for grabbing images through these four portions of the filter, F. By using a commercial CCD camera  1030  with the common speed of 30 frames per second, this system is capable of generating images with an enhanced contrast at the speed of about 7.5 frames per second. Employing the faster camera, frame grabber, and more powerful video adapter creates an opportunity to develop a spectro-polarimetric imaging system that operates at a speed close to 30 frames/s. 
     In step  1345 , step filter F +   VP  is created. In step  1350 , an image is captured through the filter F +   VP . In step  1355 , filter F −   VP  is created. In step  1360 , an image is captured through the filter F −   VP . In step  1365 , the LC ARLP is turned off. In step  1370 , filter F +   HP  is created. In step  1375 , an image is captured through the filter F +   HP . In step  1380 , filter F −   HP  is created. In step  1385 , an image is captured through the filter F −   HP . Flow may then proceed in parallel to steps  1390  and  1387 . In step  1387 , the LC ARLP is turned on and the flowchart advances to step  1345 . In step  1390 , the captured images are summed and subtracted. In step  1395 , the resulting image is displayed. Thus, while the image is being displayed, further images are being processed for further display. Flowchart  1300  provides for the enhancement of target visibility over a cluttered background which can be achieved through the employment of differences both in spectrum and in polarization of light reflected from a background B, and light reflected from a target T. 
     FIG. 11 is an illustration of an exemplary approach to object enhancement based on differences in the spectra of the light reflected from an object and of the scattered light. This approach is useful for creating filtering vector F from a background vector B and a target vector V for an object enhancer such as, for example, object enhancers  170 ,  900  and  1000 . step FIG. 11 illustrates a multidimensional space  200  where each axis λ 1 , λ 2  and λ 3  represents a light intensity in a given spectral interval. The vector B in this space can describe the light from a homogenous background. The vector T corresponds to the light reflected from a target or object such as object  110 . The vector B′ represents light scattered in an atmosphere between the object and the telescope and the vector T′ represents the object with the additional scattering of B′. A light path through a spectral filter can be described as T 1  which is a projection of the light vector on the direction of the filter vector F. 
     Object contrast enhancement can be achieved if the only light reflected from the object will illuminate a photodetector array while a spectral filter will reject the scattered light. FIG. 11 illustrates that for such a situation, the filter vector F should be perpendicular to the background vector B and should be located in the plane P where both vectors B and T are located. 
     Similar considerations are applicable for a larger number of spectral intervals used for observation. The filter vector F can be calculated by the formula              F   =       T   ′     -       (     B   ·     T   ′       )           (     B   ·   B     )       1   /   2              (       T   ′     ·     T   ′       )       1   /   2                     (   1   )                                
     Where                B        (     λ   i     )       =       ∑     m   =   1     k            ∑     n   =   1     l            I   mn          (     λ   i     )                   (   2   )                                
     which is a k×1 grid of pixels in an entire frame used for the calculation of the background spectral signature B, and                  T   ′          (     λ   i     )       =       ∑     m   =   1     p            ∑     n   =   1     q            I   mn          (     λ   i     )                   (   3   )                                
     which is a p×q grid of pixels in a small rectangle that is pointed at the object at any spectral frame which is used for calculating the object spectral signature or vector T. 
     A light intensity can be only a positive number. Therefore, spectral vectors B and T always lay in a sector in the multidimensional spectral space that has only positive coordinates. This means that a vector F that is perpendicular to B must have at least one negative projection on spectral axis (F·B =0). Of course, no optical spectral filter can produce negative light intensity. To accomplish an optimal spectral filtering for object contrast enhancement, a computer image subtraction is used. In this case, two images must be taken. The first of them is taken through a spectral filter, such as AOTF  920  or AOTF  1020 , with a transmission curve that corresponds to the full set of vector F components that have a positive value (F + ). The second is taken through the spectral filter, such as AOTF  920  or AOTF  1020 , with a transmission curve that corresponds to the full set of vector F components that have a negative value (F − ). Because the technology of the spectrally adaptive light filtering with an AOTF allows for very fast variations in filter transmission (few tens of microseconds), each next image frame can be taken through a different spectral filter. Computer subtraction of the two images is also a relatively fast operation. This allows for almost real time (half of the CCD frame rate) imaging with enhanced object contrast. 
     The description of the background spectral signature by vector B is correct only for uniform backgrounds. In some cases, several different objects can be seen simultaneously in the field of view of the optical tracker. For example, the background may be a sky and a terrain or clear sky and a cloud, etc. In this case, each pixel in the background has its own vector in multi-dimensional spectral space. FIG. 12 is an exemplary illustration of spectrally matched filtering for a non-uniform background. FIG. 12 illustrates how the ends of these vectors create a scatter plot in spectral space. 
     FIG. 12 shows a projection of a scatter-plot  310  and object vector T on a plane  320  perpendicular to the first principal component P 1  and containing the second principle component P 2  and the third principle component P 3 . The direction of the filter F can be found as a line passing through the intersection of P 2  and P 3  and a point that is the projection of the target vector T. FIG. 13 is an exemplary illustration of the distribution of the background BG intensity along the filter direction Y and a target intensity T′ that is separated from the background in this projection. In most cases, the filter direction may be a Gaussian curve. 
     The enhancement of object visibility can be achieved in this case by spectrally matched filtering. Spectrally matched filtering can be viewed as an appropriate weighted combination of principal spectral components. The axes of the principal spectral components coincide with the axes of the ellipse of the data probability distribution; they are the eigen vectors of the covariance matrix M. The coefficients of this symmetrical matrix are second order moments of the probability distribution in the background data, for zero-mean distribution, for example, for zero first-order moments. In other words, the coefficient M ij  is the measure of correlation between the data and the i-th and j-th spectral bands:                M   ij     =       1   /   N            ∑     k   =   1     N            x   ik          x   jk                   (   4   )                                
     where x pk  is the value of the p-spectral component in the k-pixel&#39;s spectral data vector after mean removal. 
     The spectrally matched filter can be described as a vector in multidimensional color space. Its performance, i.e. multiplication of each spectral image on the weight coefficient F i , is the dot vector product operation that projects a spectral vector for each pixel to the direction of the filter vector. 
     In the highly correlated backgrounds, the scatter plot ellipsoid BG is strongly elongated and there is a small probability of improving object contrast by projecting the image in a direction coinciding with the direction of the long axis P′ 2  of the ellipsoid BG. The significantly higher probability of finding an object occurs when filter vector Y lies in the plane perpendicular to this axis. As illustrated in FIG. 3, the direction of the filter vector Y must be close to the direction of projection of object vector T′ to this plane. When the direction of the filter vector Y is chosen in the system coordinate associated with principal axes, it is easy to find this vector in the original coordinates of light intensities at various spectral intervals. 
     Calculation of the filter vector F can require knowledge of background and object spectral signatures. This information can be collected at the initial stage of observation by grabbing and storing in a computer memory several spectral images taken through the same filter. The object spectral signature may be obtained by analyzing a set of object image information in an initial spectral image collection in an initial phase of observation. This information about the position of the object can be provided either from another sensor, such as radar, or by an operator by clicking a computer mouse at the object area. 
     Once the object spectral signature is obtained, the system can enhance the object&#39;s contrast automatically until the significant changes in the background occur. For example, an object passing through a cloud can cause such changes. To obtain an indication of the background variation, the system design can include a small spectrometer illuminated by a portion of the light collected by the optical telescope. The spectrometer can compare the background spectrum with the initial data and can raise an alarm signal in the case of significant variations in the spectrum shape. In this case, either the background spectrum from the spectrometer can be used for calculation of a new filter vector or the new cycle of collecting spectral information can be initiated. It is important to notice that, for example, sixteen spectral frames can be captured in about 0.5 s if a CCD camera with common 30 frames per second operational speed is used. 
     FIG. 14 is an exemplary illustration of a graph of an AOTF transmission curve. The curve  700  represents the AOTF spectral transmission curve and its approximation by a set of AOTF spectral responses  710 - 730  to a single frequency signal. As illustrated, the curve  700  is plotted along a spectral transmission T as a function of wavelength λ. By applying a complex driving signal to an AOTF, any desirable shape of the AOTF spectral transmission curve can be reproduced in an octave-wide spectral range. Such complex driving signals for AOTFs can be generated by a signal synthesizer or by an array of signal generators. For example, a signal synthesizer can be used for the AOTF driver  1040  of FIG.  3 . 
     FIG. 15 is an exemplary illustration of a timing diagram  800  of an experimental object enhancer performance. FIG. 15 illustrates how relatively fast object enhancement can be achieved by the disclosed invention. The diagram  800  includes the timing  810  of the grabbing of the positive and negative image signals, the timing  820  of the image subtraction signal, the timing  830  of the displaying of the resulting image signal, the timing  840  of a waiting end of field signal, the timing  850  of the launching AOTF. 
     The method of this invention is preferably implemented on a programmed processor. However, the method may also be implemented on a general purpose or special purpose computer, a programmed microprocessor or microcontroller and peripheral integrated circuit elements, an ASIC or other integrated circuit, a hardware electronic or logic circuit such as a discrete element circuit, a programmable logic device such as a PLD, PLA, FPGA or PAL, or the like. In general, any device on which resides a finite state machine capable of implementing the flowcharts shown in the FIGS. may be used to implement the processor functions of this invention. 
     While this invention has been described with specific embodiments thereof, it is evident that many alternatives, modifications, and variations will be apparent to those skilled in the art. Accordingly, the preferred embodiments of the invention as set forth herein are intended to be illustrative, not limiting. Various changes may be made without departing from the spirit and scope of the invention.