Apparatus and method for extended depth of field imaging

An extended depth of field is achieved by a computational imaging system that combines a multifocal imaging subsystem for producing a purposefully blurred intermediate image with a digital processing subsystem for producing a recovered image having an extended depth of field. The multifocal imaging system exhibits spherical aberration as the dominant feature of the purposeful blur. In an aspect, a central obscuration of the multifocal imaging subsystem renders point-spread functions of object points more uniform over a range of object distances, however, the system may not include a centrally obscured aperture to achieve intended results. An iterative digital deconvolution algorithm for converting the intermediate image into the recovered image based on maximum entropy involves a metric parameter that speeds convergence, avoids stagnations, and enhances image quality.

Not applicable.

TECHNICAL FIELD

Embodiments of the invention generally relate to optical image acquisition and processing apparatus and methods. More particularly, embodiments of the invention are directed to apparatus and methods for providing an image of an object that records a greater or an extended depth of field than provided by a typical diffraction-limited optical imaging system. Particularly advantageous fields for application of the embodiments of the invention include, but are not limited to, photography, digital imaging, communications and entertainment, pattern recognition, detection, microscopy, machine vision, and optical measurement.

BACKGROUND OF THE INVENTION

The recorded image of an object imaged by conventional imaging system is typically sharply in focus only over a limited object distance range (in front of and behind the object) known as the ‘depth of field’ (DOF). The DOF is inversely proportional to the square of the numerical aperture of the imaging system for diffraction-limited imaging. On the flip-side, the ‘depth of focus’ is the amount by which the image may be shifted in a longitudinal direction and introduce no more than acceptable blur. Present-day cameras have mechanical focusing means (including automatic systems), to provide high quality images of particular object scenes at various object distances. Even with these focusing means it can be difficult to sharply photograph object scenes that span large axial distances (i.e., those exceeding the DOF of the optical system). Cameras with a larger depth of focus will provide better imaging performance over those without with respect to depth of field.

Digital processing of image data on a pixel-by-pixel basis has afforded more opportunity for improving and correcting optically imaged scenes. Some of these improvements have related to increasing the depth of field in the recorded image. For example, digital processing has been used to combine images of the same scene taken at different depths of focus to produce a composite image that has recorded an extended depth of field (or, as may be said, having an extended depth of field). The multiple images take time to collect, are difficult to process, and are generally unsatisfactory for scenes subject to change.

Amplitude attenuation filters have also been used to extend the depth of field. Typically, the attenuation filters are located in the aperture of the imaging system, leaving inner radii clear but attenuating the outer annulus. Moreover, these filters tend to introduce large amounts of light loss, which limits their applications.

More promising attempts have been made that deliberately blur an intermediate image in a systematic way so that at least some information about the imaged object is retained through a range of focus positions and a non-ideal impulse response function remains substantially invariant over the defocus range. Digital processing, which effectively deconvolutes the point spread function, restores the image to a more recognizable likeness of the object through an extended depth of field.

One such example locates a cubic phase mask within the aperture of the imaging system to generate a distance invariant transfer function. Digital processing removes the blur. Although significant improvement in the recorded depth of field is achieved, the cubic phase mask is not rotationally symmetric and has proven to be expensive and difficult to fabricate.

Another such example similarly locates a circularly symmetric, logarithmic asphere lens to extend the depth of field. Although this solution is more economical to manufacture, the impulse response has not been found to be substantially uniform over the full range of operation and, as a result, some degradation is experienced in the image quality of the recovered image.

Reconstruction algorithms for removing the blur of such intermediate images are subject to problems relating to the quality and efficiency of their results. Nonlinear processing algorithms can suffer from slow convergence or stagnation and produce images with reduced contrast at high spatial frequencies.

In view of the aforementioned challenges and shortcomings associated with conventional extended depth of field imaging apparatus and processes, and others appreciated by those skilled in the art, the inventors have recognized the unfilled need for apparatus and methods that are better capable of, and cost effective at, providing better images that exhibit extended depths of field.

SUMMARY OF THE INVENTION

An embodiment of the invention is directed to an imaging system that produces images recorded with an extended depth of field (i.e., exhibiting a depth of field that is greater than, or extended, over that obtainable with a typical diffraction-limited imaging system). In an exemplary aspect, the system is referred to as an integrated computational imaging system. According to an aspect, the imaging system incorporates a multifocal imaging subsystem that provides purposefully blurred intermediate images of the object scene. As used herein, the term ‘multifocal’ refers to a circularly symmetric optical element (e.g., lens) in which the focal length of the lens changes (either increasing or decreasing) as a function of lens radius, without any angular variation of focal length. Imaging lenses with spherical aberration are an example of a component of a multifocal imaging subsystem where the focal length varies with radius.

According to an aspect of the invention, a multifocal imaging subsystem comprising one or more components is designed to provide a controlled amount of spherical aberration to purposefully and uniformly blur the image (i.e., an intermediate image that will be subject to image processing according to an embodiment of the invention described further below) over a range of focal distances. Advantageously, the introduction of the controlled amount of spherical aberration produces a substantially uniform impulse response over the range of focal depths provided by the multifocal imaging subsystem. According to an aspect, third-order spherical aberration is the primary aberration used to purposefully blur the image. According to an aspect, the multifocal imaging subsystem has a centrally obscured aperture that shapes the system's impulse response and eliminates contrast inversion for larger amounts of defocus. Together, the controlled spherical aberration and the centrally obscured aperture provide a sufficiently narrow and invariant impulse response over an extended depth of focus. Diffraction-limited performance over a range of six to 10 times the classical depth of focus limit has been demonstrated. A digital processing subsystem is provided, which recovers the intermediate convoluted images for producing a final image of the object that remains in focus through an extended range of object depths.

In an illustrative aspect, the centrally obscured aperture of the multifocal imaging subsystem is circularly symmetric and has a minimum radius, δR. The radius δR defines both an outer limit of the central obscuration and an inner limit of a transmitting aperture having a maximum radius, R, defining an outer limit of the transmitting aperture. In an advantageous aspect, the ratio of δR/R will be greater than or equal to ⅙. In an exemplary aspect, improved performance was demonstrated for δR/R equal to or greater than 0.3.

However, for use in particular applications (e.g., mobile telephone), the one or more lenses of the multifocal imaging subsystem may be relatively small in diameter, for example, about 3 millimeters (mm) or less. When the lenses are in this size range or the optical design positions the central obscuration on an internal element surface, optical as well as physical challenges make it difficult to satisfy the condition of δR/R greater than or equal to ⅙. In this regime, other factors such as internal scattering, for example, will compromise overall system performance. Thus for values of δR/R less than ⅙, it is advantageous not to incorporate the central obscuration or, in other words, δR equals zero in this alternative aspect.

The overall aberration of the multifocal imaging subsystem can be expressed as a phase delay function in nominal wavelengths of imaging light, and the predominately third-order spherical aberration advantageously contributes a phase delay of between 1.6λ and 6λ

According to an exemplary aspect, at least one lens of the multifocal imaging subsystem can be designed substantially free of spherical aberration, and a phase plate, for example, can be designed to produce the spherical aberration that forms the dominant feature of the purposeful blur. The phase plate may be positioned in an aperture within the multifocal imaging subsystem at the aperture stop (pupil) of the subsystem for optimum field of view. Alternatively, the multifocal imaging subsystem can include at least one lens element having a surface modified to produce some or all of the spherical aberration that forms the dominant feature of the purposeful blur and that is in a proper range for reducing impulse response variation over the extended depth of field. The spherical aberration can also be divided among more than one lens element to increase design flexibility. In a particular aspect, the phase delays produced within the aperture at δR and R may be at least approximately equal for the center of the designated object range.

Thus, in a particular embodiment, an integrated computational imaging system for producing an image of an object over an extended depth of field includes a multifocal imaging subsystem that introduces a controlled amount of predominately third-order spherical aberration to provide a purposefully blurred intermediate image through an extended range of object distances; an intermediate image-detector; and, a computer processing device that calculates a high quality image from the purposefully blurred intermediate image.

An embodiment of the invention is directed to a method of providing a higher-quality image of an object recorded over an extended depth of field than provided by a typical imaging system. The method involves the steps of providing a multifocal imaging subsystem as a part of an integrated computational imaging system; forming an intermediate image of an object that is purposefully, uniformly blurred by a controlled amount of predominately third-order spherical aberration over a range of focal distances, such that the system impulse response is substantially invariant over the focal range of the multifocal imaging subsystem; and, recovering an image from the blurred intermediate image for viewing or further application.

The range of object distances over which the intermediate image is purposefully blurred by the controlled amount of spherical aberration advantageously includes the given object distance; i.e., for object distances between the imaging system and the object and, alternatively, extending beyond the object. This extended depth of field provides depth of focus that may be four times or greater than that provided by a diffraction-limited imaging system.

In an exemplary aspect, an integrated computational imaging system in accordance with the invention includes a multifocal imaging subsystem for producing an intermediate image of an object that includes an ideal imaging component and a prescribed spherical aberration component for purposefully blurring the intermediate image. A central obscuration of the multifocal imaging subsystem renders the point spread function of object points produced with the purposeful blur substantially uniform over a range of object distances. A digital processing subsystem deblurs the intermediate image produced by the multifocal imaging subsystem and calculates a recovered image that exhibits an extended depth of field.

Each of the point spread functions of the object points has a central peak and oscillating ring structures. The central obscuration provides for narrowing the average point spread function either for the close-in points or the distance object points, depending on the design. In particular, the central obscuration renders both the widths of the central peaks and the oscillating ring structures more uniform among the object points.

The purposeful blur of the image provided by the multifocal imaging subsystem is the starting point for rendering the point spread functions of object points more uniform over an extended range of object distances. The central obscuration advantageously removes variant components of the point spread functions produced by the purposeful blur for narrowing the central peak of the average point spread function among the object points, especially the object distance corresponding to the center section of the imaging subsystem. The digital processing subsystem calculates the recovered image based on the average point spread function. Increases in performance associated with the central obscuration are believed mainly due to the similarities of the point spread functions over the design range of object distances rather than from any direct increase of the depth of field that might otherwise accompany the use of a central obscuration in an ideal imaging system. In particular, the associated improvements in the depth of field, particularly for close-in distances, are believed mainly due to both the narrower central peak of the average point spread function and the similar oscillating ring structures of the point spread functions over the designed object range. These two factors lead to point spread functions that vary less with object distance, so that the average point spread function used in the digital processing can provide a significantly improved output.

An exemplary multifocal imaging system having an extended depth of field includes a multiple lens element imaging subsystem that purposefully blurs an intermediate image with a controlled amount of a third-order spherical aberration. The third-order spherical aberration is distributed among a plurality of the lens elements and is the dominant monochromatic aberration of the system. In an aspect, a central obscuration cooperates with the third-order spherical aberration for reducing variation in the purposeful blur over the extended depth of field. The plurality of lens elements can include at least two logarithmic aspheres. As stated herein above, the central obscuration may have a radius equal to zero and thus be absent in view of optical and mechanical considerations (i.e., when δR/R<⅙) that would degrade system performance more than an improvement from the central obscuration.

A method for creating an extended depth of field imaging system includes formulating an imaging system as a combination of an ideal imaging component arranged for diffraction-limited imaging and an aberration component dominated by a third-order spherical aberration. The amount of the third-order spherical aberration is determined so as to reduce variations among impulse responses over the extended depth of field. The amount of the third-order spherical aberration can be determined by adjusting the amount of third-order spherical aberration from one amount to another to identify more uniform combinations of impulse responses over the extended depth of field.

Another method in accordance with the invention for extended depth of field imaging includes providing a multifocal imaging subsystem including an ideal lens modified to incorporate a controlled amount of third-order spherical aberration. The intermediate imaging performance of the modified lens is tested over a range of focus positions. The amount of the third-order spherical aberration is adjusted for producing point spread functions that have a minimal variation over the range of focus positions. A central obscuration is advantageously incorporated into the subsystem to narrow the point-spread functions, e.g., at the close-in distances referred to as the β-design.

According to an aspect of the method, a calculated impulse response departs from an arithmetic average of the impulse responses over the range of focus positions to unevenly weight the impulse responses over the extended depth of field. The calculated impulse response is incorporated into a digital processing algorithm for deblurring the intermediate image produced by the multifocal imaging subsystem and for calculating a recovered image over the extended depth of field.

Thus a circularly symmetric, spherically aberrated, multifocal imaging subsystem with (or, with qualification, without) a centrally obscured aperture can be used in accordance with the embodiments and aspects of the invention for intermediate imaging. The resulting impulse response is sufficiently invariant over a range of object depths to support digital processing for recovering an image that remains in focus over a range of focal depths. Without too much loss in light intensity, this combined system can produce diffraction-limited resolution over an extended depth of field that may be ten times that obtained by a conventional lens system. Prior approaches to extending the depth of field have not had this capability. The embodied imaging system is also economical to manufacture, since it is circularly symmetric.

The embodied invention can also be practiced as a method of recovering an image based on an intermediate image, which includes accessing an intermediate image of a scene and performing an iterative digital deconvolution of the intermediate image using a maximum entropy algorithm. Using the maximum entropy algorithm, a new image is estimated containing a combination of directional images. These directional images are uniquely altered using a metric parameter to speed convergence toward a recovered image while avoiding points of stagnation.

The intermediate image can be produced using a multifocal imaging system, such as an aspheric lens. Typical point spread functions of such lenses have oscillating bases, which reduce image contrast. The metric parameter is adjustable within its preferred range and significantly reduces side lobe oscillation that is seen in the blurred image.

We refer to this new form of nonlinear digital processing as “metric parameter-maximum entropy algorithm” or, the MPME algorithm, which is expected to have wide applicability to digital image processing. The attributes of rapid convergence and the avoidance of stagnation can be generally advantageous for image reconstruction, restoration, filtering, and picture processing.

DETAILED DESCRIPTION OF EMBODIMENTS OF THE INVENTION

Our studies of circularly symmetric multifocal lenses have revealed that a controlled amount of spherical aberration provides a desirable distance-invariant blur that leads to extended depth-of-field, high-quality imaging. An advantageous embodiment of an extended depth of field imaging system includes a multifocal lens subsystem that can be based on any standard imaging arrangement modified to incorporate predominately third-order spherical aberration, as well as higher-order spherical aberrations. Such standard imaging arrangements include Petzval lenses, Cooke lenses, double Gauss lenses, zoom lenses, auto focus lenses, telescope systems, and others known in the art.

In addition, our studies have found that a further improvement in imaging, particularly at diffraction-limited resolution throughout an extended depth of field, can be realized by obscuring a center portion of the aperture of the multifocal imaging subsystem to narrow the impulse response for close-in distances (β-design) or for far distances (referred to herein as the γ-design). The central obscuration increases the range over which the impulse response remains uniform. The radially-central region of the multifocal imaging subsystem can be a major contributor to the variation in the impulse response with distance. The combination of a central obscuration with a properly designed multifocal imaging system can be used to further extend the depth of field, or to support higher resolution imaging through the extended depth of field.

Referring toFIG. 1, an integrated computational imaging system10for extended depth of field imaging includes a multifocal imaging subsystem12, an intermediate image detection device14, a digital processing subsystem16, and a display18.

Other imaging systems contemplated by the invention include multiple lens elements such as for dealing with chromatic aberrations or other imaging requirements. The invention provides increased flexibility within such multiple lens element designs to distribute the desired amount of spherical aberration among a plurality of the lens elements. For example, at least two of the lens elements can be formed as logarithmic aspheres, each incorporating a portion of the desired spherical aberration.

The image detection device14, which collects an intermediate image30of object20that is purposefully blurred, can be fashioned as a pixilated CCD (charge coupled device) or CMOS (complementary metal oxide semiconductor) detector or other light sensitive device. The detector pixels can be arranged as a two-dimensional array, as a one-dimensional array, or even as a single detector pixel. Any pixel combinations short of two dimensions are subject to scanning for collecting enough information to complete a two dimensional intermediate image30. However, one-dimensional imaging can be used for particular applications.

The digital processing subsystem16includes a computer-processing device having a combination of hardware and software for the purpose of image processing. The digital processing subsystem16can be incorporated into a camera system that also includes the multifocal imaging subsystem12, or the digital processing subsystem16can be arranged as a standalone image-processing computer. The primary purpose of the digital processing subsystem16is to sharpen the intermediate image30. An inverse filter or its modifications, e.g., Wiener filter, can be used for this purpose. A nonlinear algorithm, such as an iterative maximum entropy algorithm, can be used to sharpen the intermediate image30. If a maximum entropy algorithm is used, an optional acceleration factor, referred to herein as a metric parameter, can be chosen to optimize the speed and convergence.

The digitally processed image, which is referred to as a recovered image32, is outputted to the display device18, which can be a CRT (cathode ray tube), LCD (liquid crystal display) or other display device appropriate for viewing purposes. Alternatively, the display device18can be omitted and/or the recovered image32can be inputted to other functional hardware/software. For example, the recovered image32can be input to a pattern recognition system or a machine vision system. If the recovered image32is used for these latter purposes, then the digital processing subsystem can be incorporated into the pattern recognition or machine vision system. The digital processing device can become optional depending on the amount of blur in the intermediate image.

The integrated computational imaging system10is applicable to binary or gray scale or color imaging. It is also applicable to a range of different wavelengths including infrared imaging.

An optical diagram of a modified multifocal imaging subsystem12for producing the intermediate image30is illustrated inFIG. 2based on use of a logarithmic asphere34that combines the ideal imaging of the lens22with a predetermined spherical aberration of the phase plate24. A point source S, which is located along an optical axis36at a distance S0away from the logarithmic asphere34at object plane I, is imaged as a blurred intermediate point image P at image plane II at a distance t along the optical axis36on the other side of the logarithmic asphere34. The logarithmic asphere34is mounted within the annular (or ring-type) aperture28, having a radius from δR to R, where δR is the radius of the central obscuration26and R is the radius of lens aperture28, where 0≦δR<R. The center portion of the lens aperture28from the optical axis36to δR is blocked by the central obscuration26in the form of a disk-shaped stop. However, δR=0 is treated as a special case of a full aperture, which is consistent with a particular embodiment of the invention.

In optical system design, the ideal imaging components and the aberrations can be described as follows:
φ/k=W+φideal/k(1)
wherein the phase delay φ is measured in radians, k equals 2π/λ0where λ0is the average wavelength of illumination, and W is the optical path difference (OPD) in micrometers.

For the ideal imaging system, it is well known that
φideal=k(√{square root over (t2+r2)}−t+√{square root over (s02+r2)}−s0)  (1)
in which the phase delay φidealis measured in radians for a perfect diffraction limited lens, r is the radial coordinate in plane I, and s0is the focused object position, and k=2π/λ0.

For the ideal lens, as an example with s0=1500 mm, t=62.5 mm, R=8 mm, and λ0=0.5 μm, from a power series expansion of φideal, one can readily find:

ϕideal=6702.07⁢(rR)2-26.35⁢(rR)4+0.21⁢(rR)6+…(3)
which Equation (3) is valid in the non-paraxial regime.

For the OPD W, of the two types of logarithmic aspheres denoted by subscript β and subscript γ, one can express them as follows:

W=s02+r2-s0+λ02⁢⁢π⁢ϕP(4)
where s0is the center of the depth of field range, λ0is the wavelength of illumination in vacuum, and the expression of φPfor the two types of logarithmic aspheres, φPβand φPγ, can be written as follows.

From a power series expansion of Equations (5) or (7), it can be appreciated for purposes of the invention that spherical aberration is the dominant feature of the purposeful blur that is being introduced. This will be made more evident within a description of some specific embodiments.

For completing a design based on Equations (4)-(8), the desired range for the depth of field s1, s2can be selected along with representative values for t, R, δR, s0, and λ0. Thereafter, the variables aβ, Aβ, and φPβ(or aγ, Aγ, and φPγ) can be computed. From these, Equation (4) can be used to compute the aberration term W.

The logarithmic asphere described above is an example of a multifocal lens that can be constructed in accordance with the invention. From a more general point of view, a multifocal lens useful for extended depth-of-field imaging can be composed of any standard imaging arrangement that is designed to incorporate a predetermined amount of spherical aberration, including third-order spherical aberration as well as higher-order spherical aberrations. For example, such standard imaging and projection arrangements such as Petzval lenses, Cooke lenses, and double Gauss lenses can be used for these purposes.

For describing a multifocal lens in terms of a range of aberrations, it is useful to expand the aberration function φPin a series in terms of (r/R). For example, if the design parameters: s0=1500 mm, s1=1400 mm, s2=1615 mm, t=62.5 mm, R=8 mm, λ0=0.5 μm, and δR=0, from Equations (4), (5), and (6) are used, a power series expansion of the phase delays of a β-type logarithmic asphere or a logarithmic phase plate is found as follows.

In Table I, the first row of data is the whole phase delay function of the multifocal lens from Equation (1), i.e., φβ(r)=6721.22(r/R)2−45.63(r/R)4+0.32(r/R)6+ . . . . The second row of data is the radian phase delay function for an ideal lens arrangement, e.g., Petzval lens, Cooke lens, double Gauss lens, or Cassegrain system. The third row of data is the aberration terms of the phase delay function, which is the difference between the phase delays of the multifocal lens and an ideal lens. The dominant aberration term in the multifocal lens is the third-order spherical aberration (i.e., the fourth-order term of r/R). For diffraction-limited resolution, the largest allowable OPD is generally 0.25λ. To achieve a ten-fold increase in the depth of field, the spherical aberration is around 3 wavelengths (i.e., 19.28λ/2π=3λ), which is a little over ten times the allowable defocus for diffraction-limited imaging. Good performance of our multifocal lens includes spherical aberration in the amount of 1.8 to 6 wavelengths, while the amount of higher-order spherical aberration is largely insignificant. The multifocal lens designed this way will have an extended depth of field from 6 to 10 times that of a conventional ideal lens.

Another example of our multifocal lens has the same parameter values as above, but with δR/R=0.5 to illustrate the effectiveness of a central obscuration26. The phase delay for the different terms is shown in the following Table II.

Although the third-order spherical aberration (r/R)4looks larger than without central obscuration, the effective third-order aberration, i.e., the phase delay difference contributed from spherical aberration between the edge of the lens and the edge of the central obscured block is: 25.76−{25.76×(δR/R)2}=19.32 radians. Thus, the effective third-order aberration amounts are similar for both the full aperture multifocal lens described by Table I and the centrally obscured multifocal lens described by Table II. Accordingly, the good performance centrally obscured multifocal lens has an effective third-order aberration that is still within a range from 1.8 to 6 wavelengths.

From the above description, it is apparent that the multifocal lens having an effective third-order spherical aberration in the range of 1.8 to 6 wavelengths can increase the depth of field six to ten times over that of a conventional lens. This conclusion pertains to any reasonable amount of central obscuration and also is independent of the wavelength of illumination, focal length and best focus object distance.

The second-order term, i.e. (r/R)2of the series expansion is not relevant to the increase of depth of field, but has the function of changing the position of center of the focus range. For the second-order term, we generally pick a value in a way that the aberration W at the inner edge of aperture28and the aberration W at the outer edge of central obscuration26have similar values to facilitate the phase plate or lens fabrication. In the case that no central obscuration is used, i.e., δR=0, a coefficient of the second-order term is selected so that the aberration W at the edge of aperture28is zero.

There are different ways that these controlled aberrations can be incorporated into the well-known imaging lens arrangements, e.g., Petzval lens or Cooke lens. For an existing lens arrangement, a simple way is to fabricate the aberration part of the multifocal lens as the phase plate24, which can be attached to the aperture28of the lens arrangement. This method is most effective if the aperture28of the lens arrangement is outside the last lens element at the image plane (II) side.

Another method of multifocal lens realization is to incorporate the aberration into the lens design of the logarithmic asphere34. By modifying a surface parameter of the logarithmic asphere34, the overall phase delay function can still include the ideal lens part and the aberration part. This method has the advantage that no actual lens element is needed; e.g., the flip-over of the well-known lens arrangement introduces large amount of spherical aberration, which could be used as the starting design point. Two important features of this embodiment are that it contains good angular resolution as well as good color correction. The desired amount of spherical aberration can also be distributed among multiple lens elements of the design to provide more design flexibility.

A substantially distance-invariant impulse response is important to the recovery of images having extended depths of focus. A predetermined amount of spherical aberration can be used to produce a more distance-invariant impulse response for effective performance both with and without central obscuration. In the lens with δR=0.5, an optimum amount of spherical aberration has been found to be about 3 waves. However, fairly good image recovery is obtained for a spherical aberration in the range from 1.8 to 6 waves.FIGS. 3A to 3Fshow the effective range for a distance-invariant impulse response. Of note are: 1) the width of center peak; 2) the similarity of side lobes; and 3) the energy leaked to side lobes.FIGS. 4A to 4Fshow the corresponding impulse responses for a lens with δR=0.

The above discussions apply also to the case of the γ-type logarithmic asphere. For the γ-type logarithmic asphere, the coefficients of the power series for W change signs but are otherwise similar, which is shown below by way of example.

For the γ-type logarithmic asphere, the same design parameters can be used including: s0=1500 mm, s1=1400 mm, s2=1615 mm, t=62.5 mm, R=8 mm, λ0=0.5 μm, and δR=0. From Equations (4), (7), and (8), a power series expansion of the phase delays of a γ-type logarithmic asphere or a logarithmic phase plate is found as shown in Table III.

As another example of the multifocal lens, Table IV is based on the same parameter values as above, but with δR/R=0.5 to illustrate the effectiveness of a central obscuration26. This result can be compared to that in Table II.

A difference between the β-type and γ-type phase plates is the sign change for the second and fourth-order terms. The fourth-order term, which corresponds to third-order spherical aberration, is positive for the γ-type lens and negative for the β-type lens. However, the absolute values of the corresponding third-order spherical aberration terms are similar for the same design range.

To demonstrate the performance of the γ-type lens,FIGS. 5A-5Fdepict the point spread functions for different amounts of third-order spherical aberration in units of OPD in combination with a central obscuration of δR/R=0.5.FIGS. 6A-6Fdepict the point-spread functions for different amounts of third-order spherical aberration in units of OPD with no central obscuration. It is apparent that the width of the point spread function changes from small to large when the object is farther away, which contrasts with the results of the β-type lens. From theFIGS. 5 and 6, it is apparent that the effective spherical aberration is still in the range of between 1.8 and 6 wavelengths, although the range is positive for the γ-type lens and negative for the β-type lens. As a further comparison between lens types, it is apparent that the β-type provides a better long distance performance, while the γ-type is favored when the close-in distances are more critical.

Based on the similarity of the impulse responses over the range of object distances, e.g., s1to s2, digital processing of the intermediate image30can be used to sharpen the images of object points throughout the depth of field. One method of image recovery involves using an inverse filter, such as the Weiner-Helstrom inverse filter. Alternatively, a maximum entropy algorithm can be programmed into the digital processing subsystem. This approach for image recovery according to an embodiment of the invention is set forth below.

For the known, measured, noisy image d, point spread function h, and standard deviation σi,jof noise θ in i,jthpixel of d, and unknown object f, one can write the following relation:
d=h**f+θ(9)
in which the double asterisk (**) is a spatial convolution. For an estimate of the object f, we start with an assumed object f(0)and iterate according toFIG. 7. The maximum entropy criterion is to find an estimate of object f which will maximize S under the constraint of C=Caim, where:

C=∑i,j⁢1σi,j2⁢(d-h**⁢f)i,j2(10)S=-∑i,j⁢fi,j⁡(ln⁢fi,j〈f〉-1)(11)
and Caimis the total number of pixels in the image and <f> is the average of image.

The maximum entropy algorithm is an iterative approach to determining an estimate of object20. A diagram of the algorithm is shown inFIG. 7, where an unknown object is convolved with the actual point spread function of the lens. Then, noise is added in the process of imaging. Starting with the initial estimate of the object, an image of this object is calculated by convolving with the single point spread function. Then, a difference between the measured blurred image and the calculated blurred image is calculated. If the difference is larger statistically than the noise in the experiment or the criterion of entropy maximization is not reached, the new estimation of the object is generated until both noise constraint and entropy maximization criterion are met, i.e., Equations (10) and (11) are satisfied.

The single point spread function used in the convolution can be calculated as an average of the point spread functions observed for the different focal depths. However, individual focal distances can be weighted differently to adjust the single point spread function for favoring certain object distances over others for compensating for other effects. The single point spread function could also be varied experimentally to achieve desired results for particular applications or scenes.

For each iteration, the new estimation of object is calculated from the earlier estimation by adding three (or four) direction-images with appropriate coefficients. i.e.,

f(n+1)→=f(n)→+δ⁢⁢f=f(n)→+∑i⁢xi⁢e→i(12)
where f(n+1)is the (n+1)thestimation of object, f(n)is the earlier nthestimation, and eiis the ithdirection-image.Thus, two key steps of the algorithm are:i) What direction-images eishould be used.ii) How to calculate the corresponding coefficients xiof the direction images.

A new metric parameter γ is introduced as a first step to determining the direction-images ei. The parameter γ adjusts the pixel values of direction-images derived from a steep ascent method. The parameter γ ranges from 0 to 1, although γ>1 is still possible. When this parameter is larger, more emphasis is given to the larger pixel values in the image, and also there exists more deviation of the direction images eifrom direction images derived steepest ascent method.

In the second step, Taylor expansions of S and C relative to variables δf are calculated up to second order terms. Hence, the quadratic approximation models Stand Ctare established. The quadratic models greatly facilitate the constrained maximization process because these quadratic equations are much easier to solve than the original nonlinear equations in Equations (10) and (11). The diagram of how to find the next estimation of the object is shown inFIG. 8.

In order to study the optimum value of metric parameter γ, an extended study has been made of the effect of varying the parameter γ. Three different pictures of varying histograms are used including: binary scene, zebra, and tiger. Each of these pictures has 256×256 pixels with the maximum pixel value scaled to 255. Each picture is blurred using 15 normalized impulse responses with the maximum blur consisting of a 5×5 matrix with 15 non-zero values and 10 zeros in the outer regions. Gaussian noise is added with a standard deviation σ ranging from 0.2 to 1.8 in 9 steps. The metric parameter γ is given 21 values ranging from 0.0 to 1.0. Hence, in these computer simulations there are about 8,000 cases. It is convenient to use an effectiveness parameter for the number of iterations, which is defined by Lσ/D, where L is the number of loops for the maximum entropy calculation to converge, σ is the noise standard deviation, and D is the number of non-zero pixel in the blurring function. InFIG. 9, we show a plot of Lσ/D vs. γ, where γ is the metric convergence parameter. The starting images in the algorithm are uniformly gray pictures with pixel values equal to the mean of the blurred images. The rationale for using the parameter, Lσ/D, is explained as follows.

For the computer simulation, the number of loops L for the maximum entropy recovery is linearly proportional to the area of the point spread function, D, or qualitatively proportional to the severity of the blur. The loop number is also approximately inversely proportional to the standard deviation of the noise, σ.

For a wide variety of pictorial content, it is apparent fromFIG. 9that the choice γ=0.4 provides a much faster convergence than γ=1. For the method of steepest ascent, γ=0, the algorithm does converge but it takes 173 times as many loops for a fixed σ/D as compared to the γ=0.4 case (for the zebra). Another feature not immediately apparent from the curves is that when the metric γ is equal to or close to 1, there is a chance that the algorithm can fail due to stagnation. By the experiment, the recommended value of the metric parameter is from 0.2 to 0.6 and more preferably from 0.3 to 0.5. In summary, it is clear that the use of the metric parameter γ guarantees the convergence and makes the algorithm converge much faster for a wide range of scenes. Stagnation is not observed with 0.3<γ<0.5. This new type of nonlinear digital processing is referred to as a metric parameter-maximum entropy (MPME) algorithm.

A more rigorous mathematical description of the metric parameter-maximum entropy algorithm follows within which control over the metric parameter γ enables the algorithm converge much faster for a wide range of scenes.

Two operators used in this section are defined in the following for convenience:(i) If f is a vector f={fi}, then f.γis a vector with its components defined as:
(f.γ)i□{fiγ}(ii) If f and g are vectors of the same dimension, f={fi} and g={gi}, then f.×g is a vector with its components defined as:
(f.×g)i□{figi}

To find the solution of {fk} according to the Lagrange multiplier method, a new function is defined as:
Q=S−λC(13)
where is λ is a Lagrange multiplier constant. Now the problem becomes to maximize Q under the constraint C=Caim. Since Q is a function of n variables, where n is the number of pixels in the image, which is usually very large, an iterative numerical method can be used to find the solution. In each iteration, the standard way is first to determine the search directions in which the solution is estimated to lie and then to find the step length along these directions.

The choice of directional images is important in determining the convergence and speed of the algorithm. In the steepest ascent method, the search direction for maximizing Q is ∇Q. But in order to adjust the weight of different pixel values fi, the direction can be modified to be:
eA=f.γ.×∇Q(14)

In the above equation, the new metric parameter γ improves the speed and reliability of the metric parameter-maximum entropy algorithm. For image deblurring in photography, the larger pixel values will have larger weight, so γ>0 is chosen to let the algorithm approach the desired larger pixel value faster. Generally, γ is chosen from 0 to 1. When γ=0, eAbecomes the search direction for the steepest ascent method. When γ=1, eAbecomes the search direction used by Burch et al. in a paper entitled “Image restoration by a powerful maximum entropy method,” Comput. Visions Graph. Image Process. 23, 113-128 (1983), which is hereby incorporated by reference. Neither the steepest ascent method nor the method of Burch et al. incorporate the metric parameter γ, which provides a new mathematical construction that can be manipulated to increase the speed of convergence and avoid stagnation.

At the maximum point Q, we have:
∇Q=0  (15)

This implies ∇Q·∇Q needs to be minimized, too. Accordingly, the next search direction should be ½ ∇(∇Q·∇Q), or ∇∇Q·∇Q. Here ∇∇Q is the dyadic gradient whose component is defined as follows:

Again, in order to emphasize the bigger pixel values, the direction is modified to be:
eB=f.γ.×[∇∇Q·(f.γ.×∇Q)]  (16)

Substitution of Equation (13) into Equations (14) and (16) yields the following:

Observing the above expression, we know that the two directions actually are linear combinations of many directions, which can be treated as separate search directions, viz.,

From Equations (10) and (11), we have:

Substitution of Equations (19) and (20) into (18) yields the components of each search direction as follows:

In the algorithm, e5and e6can be disregarded as search directions because when γ=0.5 they decompose to e1and e2, respectively; and also when γ<0.5, for small pixel values, they both involve dividing by small numbers, which can cause numerical accuracy problems. Accordingly, e1, e2, e3, and e4are chosen as four search directions.

To simplify the algorithm, three search directions are enough for this problem, we can pick e1, e2, and e3or we can pick e1, e2, and e4, −e3, as another choice. The algorithm converges at about the same speed, although the latter choice is a little better. Here, λ is a constant chosen by Equations (13) and (15), i.e., λ is given by:

In either case, three directions can be written as e1, e2, and e3for simplicity. In the calculation of search directions, it is apparent that that the directions e2and e3are basically the convolutions related to the real point spread function, h, and object, f, before being shaped to one dimension. Care needs to be taken to make certain that there is not pixel shift or image position shift after the convolution operation.

After three search directions are calculated for the current iteration (n), the next task is to find an estimation of the object for the next iteration f(n+1), which is defined as:
f(n+1)=f(n)+δf(23)
where δf is the change of image for the current iteration. It is defined as linear combinations of search directions, with their coefficients to be determined, i.e.,
δf=x1e1+x2e2+x3e3(24)

Since S and C are functions of f that vary in a complicated way, a Taylor expansion can be used to calculate their values as a function of the search directions. Retaining up to the quadratic terms, Stand Ctcan be written as follows:

Substitution of Equation (24) into Equations (25) and (26) yield the following expressions written in matrix form,

In Equations (27) and (28), the notation is defined as follows:

S0=⁢S⁡(f(n))C0=⁢C⁡(f(n))X=⁢[x1x2x3]TA=⁢[∇S·e1∇S·e2∇S·e3]B=⁢-[e1·∇∇S·e1e1·∇∇S·e2e1·∇∇S·e3e2·∇∇S·e1e2·∇∇S·e2e2·∇∇S·e3e3·∇∇S·e1e3·∇∇S·e2e3·∇∇S·e3]M=⁢[∇C·e1∇C·e2∇C·e3]N=⁢[e1·∇∇C·e1e1·∇∇C·e2e1·∇∇C·e3e2·∇∇C·e1e2·∇∇C·e2e2·∇∇C·e3e3·∇∇C·e1e3·∇∇C·e2e3·∇∇C·e3](29)
where [. . .]Tdenotes the transpose of matrix. Matrices A, B, M, and N can be calculated from Equations (20) and (21).

Equations (27) and (28) can be simplified by introducing new variables to diagonalize B and N. First, the rotation matrix R is found to diagonalize the matrix B, i.e.,
RBRT=diag(λ1,λ2,λ3)  (30)
where diag( . . . ) denotes the diagonal matrix.

A new variable Y is defined as follows
Y=RX  (31)

Substitution of Equations (31) and (30) into Equations (27) and (28) yield the following expressions:

Some eigenvalues of B may be very small, and this is discussed in the following two cases.

Case i) Assume that none of λ1, λ2, and λ3is small.

We introduce Z, such that:
Z=diag(√{square root over (λ1)},√{square root over (λ2)},√{square root over (λ3)})Y(34)

A second rotational matrix V is introduced to diagonalize P, i.e.,
VPVT=diag(μ1,μ2,μ3)  (37)
and also define U as:
U=VZ  (38)

Then, substitution of Equations (37) and (38) into Equations (35) and (36) yield the following expressions:

Combining Equations (31), (34), and (38), yields the identity:

In this case, λ3≈0, y3=0 in Equation (31), and also:

A second rotation matrix V is introduced, such that

A new variable U is defined as:

The other case, when two values of λ1, λ2, and λ3are small, can be treated in a similar way. In general, the following expressions of Stand Ctcan be written for all cases with the quadratic matrices diagnolized as follows:

The relation between uiand xi(i=1, 2, 3 or i=1, 2, or i=1) can be found in the identities Equations (41) or (49).

Now the maximum entropy problem becomes one to maximize Stin Equation (50) under the constraint of Ct=Caimin Equation (51). Ctin Equation (51) has minimum value of:

Clearly, Cmin, could be larger than Caim. If this happens, then the maximization of S under the constraint of Ct=Caimwill not have any solution. Accordingly, a new constraint is defined that can always be reached as follows:

The Lagrange multiplier method can be used to solve the maximization in Equation (50) by introducing a new variable Qtas follows:
Qt=αSt−Ct(α>0)  (54)
where Qtcorresponds to the Q wave variable at the left side of Equation (54), (a>0) guarantees that the solution found will maximize the entropy instead of minimizing it.

Substituting Equations (50) and (51) into (54), yields the values of uithat maximize Qtas follows:

ui=α⁢⁢si-ciμi+α(55)
where α is determined by solving the following equation, which is derived by the substitution of Equation (55) into Equation (51) and by the use of the constraint in Equation (53):

C0+∑ci⁢α⁢⁢si-ciμi+α+12⁢∑μi⁡(α⁢⁢si-ciμi+α)2=⁢(α>0)(56)
After α is known, coefficients of x1, x2, and x3are found by Equations (55) and (41) or (49), and the next assumed object for the next iteration can be calculated by Equations (23) and (24). At each iteration, the negative values in the assumed object are reset to zero.

At each iteration if the constraint of C=Caimis satisfied, whether the entropy is maximized is checked by determining if ∇Q is zero, or whether ∇S and ∇C are parallel by calculating the following value:

test=∇S·∇C∇S·∇S⁢∇C·∇C(57)
The algorithm stops if |test|<0.1.

There is a special case when coefficients of x1, x2, and x3are too large such that the expressions in Equations (25) and (26) are not accurate. The Burch et al. paper deals with this by introducing a distance penalty parameter. However, if the starting estimation of the object is a uniformly gray picture or the blurred picture, then this complexity can generally be avoided. Only when the starting image is random should the extra parameter be introduced in the algorithm but only through the first several loops. A further description of the metric parameter-maximum entropy algorithm is found in a paper authored by the co-inventors entitled “Computational imaging with the logarithmic asphere: theory” J. Opt. Soc. Am. A, Vol. 20, No. 12, December 2003, which is hereby incorporated by reference.

In addition to increasing the speed of convergence and avoiding stagnation improved deblurring and image recovery are possible. The metric parameter-maximum entropy algorithm or MPME algorithm improves image quality by increasing the contrast of the recovered image. Adjustments to the metric parameter .gamma., particularly to within the range of 0.2 to 0.6 result in a modulation transfer function having a more rectangular form, which preserves contrast of higher spatial frequency components. The effect of the metric parameter γ is also evident on the point-spread function as a reduction in side lobe oscillations apparent in the intermediate image. The final point images are closer to true points with little or no ringing. Disappearance of the oscillating rings also increases contrast.

The MPME algorithm provides an iterative digital deconvolution method capable of starting with any image. An estimate of the next new image can contain a linear combination of directional images. The metric parameter γ modifies the directional images from those provided by conventional maximum entropy algorithms, while reconciling the directional images of the conventional algorithms as integer instances of the metric parameter γ. Preferably, a quadratic Taylor expansion is used to calculate the values of the entropy S and the statistical noise constraint C as functions of the search directions. The modified statistical noise constraint assures an iterative solution of the new image estimate.

The metric parameter-maximum entropy algorithm (MPME) has an important range of applications due to the “box-like” form of the resulting overall modulation transfer function, as shown inFIG. 15. Hence, at high spatial frequencies the contrast of any digital image will be higher than is typical of the classical fall-off of photographic images. As is well known, the incoherent image optical transfer function falls off in a triangular-like manner as the spatial frequency ranges from zero to the Nyquist limit, also known as the cutoff frequency. The MPME algorithm provides sharper, crisper, high contrast output. While there are earlier algorithms that provide some form of high-frequency or edge sharpening, these earlier algorithms amplify the high-frequency noise as well. For example one such algorithm consists of a two-dimensional FFT, followed by high-frequency emphasis, followed by an inverse FFT. However, as is well known, these earlier methods of providing emphasis or higher contrast at the higher spatial frequencies amplify the noise in the image. From experiments using the MPME algorithm, as is apparent from a study of the operation as shown inFIGS. 7 and 8, the MPME algorithm does not have this drawback. The MPME algorithm provides sharper contrast at the higher spatial frequencies without undue amplification of the noise. Therefore, this contributes to its general applicability in digital image processing.

The optional central obscuration26of the aperture28as apparent inFIG. 2has been introduced to improve the system performance. By incorporating the central obscuration26into the multifocal imaging system12, the processed image can have higher resolution and higher contrast, especially for the close in object distance. The details of this comparison are described in the following paragraphs.

To illustrate the improved performance introduced by the centrally obscured logarithmic asphere,FIGS. 10 and 11compare the images produced through a full aperture logarithmic asphere with the images produced through a centrally obscured logarithmic asphere. The imaging of a two-point object simulation is shown inFIG. 10, based on a full aperture β-design logarithmic asphere. The design parameters are: s1=1400 mm close distance, s2=1615 mm far distance, and s0=1500 mm center distance with an f/# of the lens at 4. The diffraction-limited depth of field is ±8 mm. The two object points are separated by the diffraction limit distance, viz., 2.27 μm. The images of the object blurred by the logarithmic asphere are shown inFIGS. 10(b)-(d) for the object distances s=1450 mm, 1500 mm, and 1580 mm, respectively.FIGS. 10(e)-(h) show the maximum entropy recovery results for the images inFIGS. 10(a)-(d), respectively. For comparison, the performance of an idealized lens is shown inFIGS. 10(a) and10(e). Immediately apparent is the excellent recovery due to the deconvolution inherent in the maximum entropy algorithm. A standard deviation of the noise used in these recoveries is σ=1.5. The point spread function used forFIG. 10(b)-(d) is the average point spread function of the logarithmic asphere over the design range, and the point spread function used forFIG. 10(a) is the actual diffraction blur of the ideal lens. In the maximum entropy recovery using any single member of the impulse responses will lead to near perfect recovery at that distance. However, for extended depth-of-field, it is more advantageous to use the averaged point spread function. Still, as seen inFIGS. 10(b)-(d), by the elimination of most of the oscillation rings and the narrowing of the blurs, nearly diffraction-limited resolution can be achieved over the entire range. Faint rings are apparent in the recoveries shown in FIGS. (g) and (h).

A similar simulation using the centrally obscured logarithmic asphere26is shown inFIGS. 11(a)-(o). The same close (s1=1400 mm), far (s2=1615 mm) and center (s0=1500 mm) distances are used. The center obscuration δR=0.5. i.e., by area 25% of the aperture is not used. This obscuration causes an approximately 25% loss of light intensity, which needs to be considered in a final design trade-off. InFIG. 11, the rows are for object distances 1450 mm, 1492 mm, 1500 mm, 1508 mm, and 1580 mm, respectively, and the columns are for ideal images, intermediate (blurred) images, and recovered images, respectively. The conventional depth of field (for a full aperture f/4.0) ranges from 1492 mm to 1508 mm. A single average impulse response over the design range is used for all five recoveries. The similarity of blur for the logarithmic asphere is clearly seen from the center column of the intermediate (blurred) images, all have two bright peaks at the center accompanied by low intensity oscillating rings. The center bright peaks also have similar sizes. The oscillating rings do not pose a problem since excellent recoveries are achieved for all five images. The two-point recovery (right column) shows excellent diffraction-limited resolution from 1450 mm to 1580 mm. At s=1420 mm (not shown), the two points are still easily resolvable, but not as clearly as those in the figures.

The performance improvements, particularly for close-in distances (β-design), is believed due to both the narrower central peak of the average point spread function and the similar oscillating ring structure of the point spread function over the designed object range. These two factors lead to a point spread function that varies less with object distance s, so that the average point spread function used in the digital processing can provide a significantly improved output. Thus, in comparingFIGS. 10 and 11, a significantly improved result can be achieved by using the centrally obscured logarithmic asphere. The rings are more suppressed and the two points are more clearly resolvable over a range of object distances. In addition, the central obscuration can avoid conditions such as contrast inversion for larger amounts of defocus.

To further demonstrate the improved performance of centrally obscured logarithmic asphere, we show inFIGS. 12 and 13intensity distributions associated with imaging the same two-point object using three logarithmic aspheres having different central obscuration values of δR=0, δR=0.3, and δR=0.5, respectively. When the object is at a distance of s=1450 mm, or 50 mm closer than the best focus plane, the recovered one dimensional pixel values in the two point direction are plotted inFIG. 12along with the pixel values of the blurred image by an ideal lens. For producing the ideal lens curve, the full aperture of the lens is used, i.e., δR=0. A fully resolvable two point recovery is produced with center obscuration of δR=0.5. However, when the obscuration is reduced to δR=0.3, the recovery points are barely resolvable; and when there is no central obscuration, δR=0, the recovery points are unresolvable. These curves clearly show the performance improvement made by a correctly sized central obscuration. The increase in performance is believed mainly due to the similarities of the point spread functions over the design range of object distances instead of from the increase of the depth of field by central obscuration, since we have seen from the earlier section that the increase in depth of field by central obscuration of aperture is, by itself, much more limited.FIG. 13illustrates a set of similar curves for an object is at a distance of s=1500 mm, the best focus position for the logarithmic aspheres and the ideal lens. Again, advantages of the logarithmic asphere with central obscuration for both the resolving power and contrast are apparent.

The improved performance made possible by the invention has particular benefits for photography, which can be observed by comparison from the pictures ofFIGS. 14(a)-(d). In this simulation, the tiger picture is at a distance of 1580 mm (i.e., 80 mm beyond the best focus position).FIG. 14(a) is the recovered image for the logarithmic asphere without center obscuration.FIGS. 14(b) and14(d) are the recovered images for the logarithmic asphere with central obscuration of δR=0.3 and δR=0.5, respectively.FIG. 14(c) shows the tiger image reproduced by an ideal lens with full aperture for comparison purposes. The logarithmic aspheres both with and without obscuration are capable of extending the depth of field. However, the recovered images for logarithmic aspheres with obscuration are better because there are fewer artifacts. The artifacts of the recovery are believed to appear because of differences between the point spread functions through the range of object distances, while the average point spread function over the design range is used for all the recoveries. In inspecting these pictures, it is of note that the width of tiger whiskers in the simulation is about 0.7 μm, which is smaller than the diffraction-limited spot size.

In intensity imaging systems, it is common to characterize their performance by an optical transfer function. Extending this notion to a computational imaging system, in principle, the overall frequency response can be found by dividing the spectrum of the recovered image by that of the input object. Thus, to find the overall frequency response, the images of a point source can be calculated at various object distances, and the maximum entropy algorithm can be applied to these intermediate images to recover the point object. The recoveries can be considered as the combined impulse response of the integrated computational imaging system. A Fourier transform of the recoveries is plotted inFIG. 15. The curves are the combined transfer functions of the system over a range of object distances. The transfer function of the system is circularly symmetric, andFIG. 15shows its values along the radial direction over a range of focal depths. The relative spatial frequency 1.0 corresponds to a cutoff frequency of the diffraction-limited lens for the same imaging settings. The amplitude of the overall transfer function of the new system is increased to the diffraction limit over an extended object range. In addition, the phase of the overall transfer function is zero due to the circular symmetry of the impulse response. The diffraction-limited performance for the integrated computational imaging system over an extended depth of field is clearly seen from these curves.

However, the concept of overall transfer function is only an approximate index of the system performance because of the nonlinear digital processing involved. In other words, different overall transfer functions can be expected for various objects. Nonetheless, the transfer function shown inFIG. 15is a good indication of performance of the integrated imaging system.FIG. 16references the overall transfer function for objects at a distance of s=1580 mm, where transfer functions for both a point and an edge object are plotted. The recoveries from the blur images by the logarithmic lens provide the overall point spread function and overall edge response function, respectively. FromFIG. 16, it is apparent that the overall transfer functions are similar for these two cases.

From the foregoing description, it will be apparent that an improved system, method and apparatus for imaging are provided using a multifocal imaging system in which spherical aberration is the predominate form of blur for intermediate imaging and in which a central obscuration can be used for making more intermediate images more susceptible to correction by digital processing for increasing both resolution and depth of field. Variations and modifications in the herein described system, method, and apparatus will undoubtedly become apparent to those skilled in the art within the overall spirit and scope of the invention.