PHOTOGRAPHIC IMAGE QUALITY: PREDICTION AND ANALYSIS

• Document Type: CREST
• Collection: General CIA Records
• Document Number (FOIA) /ESDN (CREST): CIA-RDP78B05167A001800170026-0
• Release Decision: RIFPUB
• Original Classification: K
• Document Page Count: 22
• Document Creation Date: December 27, 2016
• Document Release Date: June 9, 2014
• Sequence Number: 26
• Publication Date: September 19, 1966
• Content Type: REPORT

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PHOTOGRAPHIC IMAGE QUALITY; PREDICTION AND ANALYSIS
Milton D. Rosenau, Jr.
? The Perkin-Elmer Corporation
Norwalk, Connecticut, U. S. A.
Presented at
Symposium of Commission I
International Society of Photogrammetry
London, 19-23 September 1966
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11111
PHOTOGRAPHIC IMAGE QUALITY: PREDICTION AND ANALYSIS
Milton D. Rosenau, Jr.
The Perkin-Elmer Corporation
Norwalk, Connecticut, U.S.A.
ABSTRACT
Optical transfer functions have been applied to both the
prediction and the analysis of image quality in photo-optical systems
with considerable success, but there are limitations. The optical
image quality depends on the wavefront shape, the object's apparent
modulation, and image motion, for which optical transfer functions
can easily be applied. However, the film effect on the optical image
is non-linear and requires treatment by other techniques. Since
aerial photography is obtained in broad wavelength regions, there is
a further requirement to consider chromatic effects, and for this,
polychromatic optical transfer functions are now being applied. The
prediction of photographic image quality requires assumptions and a
probabilistic treatment is needed. The analysis of photographic image
quality for diagnosis of camera performance requires a comparison of
the actual image with the expected image. Examples will illustrate
these points.
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I. INTRODUCTION
There are basically two different problems in considering photo-
graphic image quality. The first problem is prediction, that is, estimating
how well a photo-optical system will perform. The second problem is analysis,
that is, deciding how well it did perform.
In predicting photographic image quality, the concern is to select
among candidate photo-optical systems, to permit intelligent design tradeoff)
or to create specifications for subsequent acceptance testing. In the process
of making predictions, it is necessary to deal with unknowns which require
some assumptions, and it is generally desirable to recognize that the variabil-
ity of some aspects is best handled by a probabilistic treatment.
Analysis of photographic image quality is important for the con-
duct of acceptance tests and to provide diagnostic clues so that residual de-
fects may be located. Analysis for acceptance testing is quite straightforward,
being essentially a "go-no go" process. Analyzing photographic images to pro-
vide a clue to system imperfections is a difficult task but it can be success-
fully accomplished if the analyst has a thorough understanding of the photo-
optical system so that he knows the orientation and plausible magnitude of image
quality losses attributable to each subsystem.
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II. THE PHOTOGRAPHIC PROCESS:
In aerial photography a linear treatment can be applied between
the ground object and the aerial (or exposure) image, but a non-linear treat-
ment is required between the aerial image and the photographic image.1/2
The linear portion of the photographic process is concerned with
the modulation (or contrast) of the object and its subsequent alteration by
non-image forming light received at the focal plane. The linear portion also
involves the optical transfer function of the imaging process, and here we
are concerned with a multiplication of two transfer functions, one due to the
optical wavefront and a second due to an image motion.
Optical wavefronts are rather like a mermaid. Everyone presumably
knows what they are but we have not actually got one that we can exhibit.
For our purposes now, the important characteristics are depicted in Figure 1.
In Figure 1A, a point on the ground is shown emitting a spherical wavefront.
This passes through a homogeneous atmosphere and arrives at a perfect lens
where it is reshaped to a spherical wavefront convergent on the Gaussian image
point. This produces an intensity distribution with Airy rings as illustrated
at the top of the figure. In Figure 1B, the intervening atmospheric turbulence
is shown to distort the perfectly spherical wavefront which then is still some-
what deformed after transmission through the lens, and this produces a somewhat
unsharp image. In Figure 1C, we illustrate a highly artificial atmospheric
distortion putting a notch in the wavefront. In Figure 1D the atmosphere is
perfect but the lens somewhat imperfect and the wavefront which results is
still imperfect. In Figure lE there is illustrated an imperfect wavefront and
imperfect lens with imperfections exactly compensating each other, and this
would produce a perfect wavefront imaging to a diffraction pattern. This
latter situation is highly improbable, of course, but does illustrate the point
that it does not matter where a wavefront becomes distorted, but it is only the
shape of the wavefront in the exit pupil of the lens which determines the in-
tensity distribution in the image.
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In Figure 2 the means by which an optical transfer function is
computed is shown.3 While the equations look imposing, it is a straight-
forward computation and simply means that the shape of the wavefront in the
exit pupil contains all of the information about the optical behavior of the
lens.4
Image motion has been extensively treated elsewhere.5'6 The
points to bear in mind now are that this transfer function is independent of
the wavefront transfer function and that it depends only on the relative
motion of the image and the film.
The linear relation is illustrated in Figure 3. An object is
imaged through the atmosphere, through the boundary layer (which is a locally
disturbed atmosphere near the photographic airplane), through the window in
the photographic airplane, through the camera lens, where there may be some
imperfections in focus and image motion, to produce an aerial image which im-
pinges on the film while the shutter is open. The three independent factors
which influence this imaging process are the reduction of the object's in-
herent modulation (Mo) by scattered and added non-image forming light,1'70,
and the two transfer functions which we have just discussed, the product of
which is T(k).
The film is non-linear so a sinusoidal intensity distribution is
not transmitted as a sinusoid.8
In addition, film is grainy and effectively
adds noise to an otherwise generally smooth intensity distribution.2'9-11
The most satisfactory way to treat this difficulty depends on the consideration
at hand. In predicting resolution, for instance, modulation detectability
curves have proven quite satisfactory.12'13
Other treatments are possible,
but, essentially, the prediction of the transmittance of the film depends on
knowing the aerial image intensity distribution impinging on the film, and
secondly the exposure of this with respect to the characteristic curve. With
this information it is possible to predict the mean transmittance of the film,
and then noise may be added to this statistically.
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III. POLYCHROMATIC OPTICAL TRANSFER FUNCTIONS:
Lenses are generally designed to give a good image in a single
monochromatic line, typically the sodium (nD) line in the middle of the
photographic region. Then, as illustrated in Figure 4, the design is changed
to provide good color correction at two other wavelengths, typically the
mercury (ne) and hydrogen (n e) lines. The designer will very often examine
the modulation transfer functions at each of these wavelengths, and, in effect,
the phase transfer function by examination of the lateral color. But, in
aerial photography, lenses work over a continuous spectral region with a rel-
ative intensity that is determined by the scene illumination, the spectral
reflectance of the scene, the spectral transmittance of the lens-filter com-
bination, and the spectral sensitivity of the film. Because of this it is
desirable to compute an optical transfer function which is descriptive of the
behavior of the optical system in this entire spectral region.
To provide an example, we will report on a study of J. G. Baker's
Geocon lens14 Figure 5. As shown in Figure 6 the modulation transfer function
varies considerably with wavelength. This lens is a severe example of chro-
matic dependence because it has so many glass types to achieve a very wide
field, flat focal plane, large relative aperture and low distortion.
As shown in Figure 7, we use four steps in computing polychromatic
transfer functions. (An alternative technique has also been discussed.15)
The first three are the same steps which are necessary to compute the mono-
chromatic transfer functions while the last step provides an approximate
polychromatic transfer function. To investigate the approximation, we apply
Simpson's Rule at 3, 5, 7, 9, 11 and 13 wavelengths distributed in the oper-
ational spectrum, as shown in Figure 8. For this lens, the on-axis results
are shown in Figure 9, and some off-axis results in Figure 10. In this
particular instance, the polychromatic transfer functions computed with nine
wavelengths would be satisfactory for most purposes.
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ur
Another problem which arises in testing of lenses is that the
laboratory tests are sometimes carried out using spectral regions which are
different than the operational spectrum. For instance; as shown in Figure 111
using a tungsten source and a photoelectric detector produces a relatively
blue spectrum as opposed to the operational spectrum for which the previous
computations were made. In such a laboratory situation the polychromatic
transfer function of the lens may be quite different than the operational
case as shown in Figure 12.
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IV. PREDICTION:
In making a prediction of camera performance, the analytical
technique must be carried out in some straightforward method. The use of
polychromatic transfer functions (Section III) within the framework of the
model of the photographic process (Section II) is a rational way to make
these predictions. However, there are two very important considerations
which must be properly understood. The first is that assumptions have to be
made. Second, performance prediction must be done probabilistically because
of inherent variability in the aerial photographic process, and, for this
large computers appear to be necessary.
In predicting performance to select among candidate systems or
make design choices, assumptions must be made principally about the magnitude
of residual image motion, and the magnitude of focus imperfections. For
instance, in a camera just being designed, the actual performance of final
mechanisms is unknown and must be estimated based on assumed design success.
The basis for probabilistic treatment has been described pre-
viously.16
In recent year, we have done considerable work to further evolve
the prediction technique, but it still requires critical testing before a new
report is justified. What is worth remembering is that the atmosphere, the
magnitude and direction of image motion and the optical transfer function are
all variables from one photograph to the next and in some cases from place
within a photograph. The variable behavior of film can be important.
Figure 13 distinguishes the differing requirements among the
three main prediction purposes. Since acceptance tests may be carried out
under known conditions, assumptions may not be needed, but the real variance
of the measurement technique is crucial.17
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V. ANALYSIS
When photographic images are measured for resolution, there will
be a variation as illustrated in Figure 14. If the analysis is for the
purpose of acceptance testing, standard statistical techniques can be applied
to decide if the system is acceptable with respect to the predicted resolution
spread.
In the case of camera fault diagnosis in flight testing, the
situation is more involved because there is generally a larger variation in
resolution and even more of this variation is not due to the camera. This is
summarized in Figure 15 for both edge gradient analysis18/19 and resolution
targets. Statistical treatment by Student's t-test can be applied to determine
if the actual performance is significantly different from prediction. If the
difference is significant, the cause may be deduced by the clues catalogued
in Figure 16, but it is also important to verify that the magnitude is
plausible. It is not apparent that most diagnostic analysis is done this
carefully today, so we have the prospect of further advances in this area.
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ACKNOWLEDGEMENTS
The method of computing polychromatic optical transfer
functions was proposed by A. Offner and D. V. Lundholm, the
implementation was carried out by D. V. Lundholm with program-
ming assistance from D. Milson and R. A. Rissolo, and
J. Hoogland offered valuable guidance in the required re-
fractive index interpolation. It is a pleasure to acknowledge
these contributions and those from other colleagues.
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REFERENCES
1
Rosenau, M. D., Phot. Sci. and Engr. 265-271 (1962).
2Scott, F.
3Offner, A
Phot..Sci. and Engr. 2.1 248-251 (1965).
.1 Phot. Sci. and Engr. 2j 240-243 (1965).
4Shack, R. V. J. Opt.
5Rosenau, M. D., Phot.
6Shack, R.
Soc. Am. 46 755-757 (1965).
Sci. and Engr. 9 248-251 (1965).
V., Appl. Opt. 1 1171-1181 (1964).
7Rosenau, M. D., Scott, F., and Thiessen, W. F., Photo. Sci. and Engr.
92-95 (1963).
8Lamberts? R. LI J. Opt. Soc. Am. 49 425-428 (1959)
9Se1wyn, E. W. H., J. Phot. Sci. 2, 138-147 (1959).
10
Schade, O. H., J.S.M.P.T.E. 73 81-119 (1964).
11Watt; F. J. B. and Steel, B. G., J. Phot. Sci. 12 34-46 (1964).
12Scott,
13
Scott
14Baker,
R. M., Phot. Sci. and Engr. 2, 261-262 (1965).
F., Phot. Sci. and Engr. 10 49-52 (1966).
J. G., U. S. Patent 3,039,361 (June 19, 1962).
15
Rimmer? M. P. and Shannon, R. R., J. Opt. Soc. Am. 55 1570 (1965).
16Rosenau? M. D., Appl. Opt. 2, 29-34 (1964).
17Waidelich, J. A., J. Opt. Soc. Am. 55 1589 (1965).
18
Scott, F: Scott, R. M., and Shack, R. V., Phot. Sci.
(1963).
and Engr. L 345-349
19Rosenau, M. D., "Image Evaluation by Edge Gradient Analysis", paper
presented at Soc. Phot, Instr. Engrs. 11th Annual Symposium, St. Louis,
22 Aug. 1966.
10
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INTENSITY OF IMAGE (I)
VS
DISTANCE (X) X
IMAGE
LENS
ATMOSPHERE
GROUND
(A)
PERFECT SYSTEM,
HOMOGENEOUS ATMOSPHERE
(B)
PERFECT SYSTEM,
TURBULENT ATMOSPHERE
FIGURE Ia. OPTICAL WAVEFRONTS
INTENSITY OF IMAGE (I)
VS
DISTANCE ( X)
IMAGE
LENS
AT
GROUND
(C)
BAD ATMOSPHERE
GOOD LENS
BAD IMAGE
(D)
GOOD ATMOSPHERE
BAD LENS
BAD IMAGE
FIGURE lb. OPTICAL WAVEFRONTS
?
(E)
BAD ATMOSPHERE
BAD LENS
GOOD IMAGE
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A ( xy')= Wavefront distance at point x',y' in exit pupil
from spherical wavefront of radius f
G (x%Yi)' -2Tri (xI,Y1) s PUPIL FUNCTION
Thf
=
tle
S 5 G (x1-4-x,y1+y) G* (x;YI) dxdy
L-7- OPTICAL TRANSFER FUNCTION
FIGURE 2. DEPENDENCE OF OPTICAL TRANSFER
FUNCTION ON WAVEFRONT
I?a-PHOTOGRAPHIC
IMAGE
NON-LINEAR
4
is-AERIAL
IMAGE
IMAIGE
MOTION
FOCUS
LENS
WINDOW
BOUNDARY
LAYER
ATMOSPHERE
OBJECT
LINEAR
RELATION
IMAGE MODULATION= Milk )= Mo(k) 0Tw(k)Tim(k)
RELATIVE MOTION
OF IMAGE AND FILM
TEMPERATURE AND
OBJECT DISTANCE
FLARE
DIFFRACTION,
OBSCURATION, AND
ABERRATION
STRAIN, INHOMO-
GENEITY, AND
UNFLATNESS
LUMINESCENCE
(X)
TURBULENCE
( A.C.)
SCATTERING
(D.C.)
TURBULENCE
(A.C.)
INHERENT MODULATION = Mo( k)
INFLUENCES if
INDEPENDENT
FACTORS
MODULATION
ALTERATION
CO)
WAVEFRONT
[Tw(k)]
IMAGE MOTION
[Tim(k)]
FIGURE 3. THE AERIAL PHOTOGRAPHIC PROCESS
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RELATIVE
INTENSITY
?
?.I
nD
MONO
(
COLOR
OPI
SI
A
ne
nc
L1 LA
_
_ \AI A
5000A
60004
7000A
FIGURE 4. AERIAL LENS DESIGN AND
A TYPICAL FLIGHT SPECTRUM
BaSF-I0
LaK-17
LAl 1 i
it aSF-5 LaSF-5
LaK-17
1 LaK-17
LaSF-5
LaSF-5
SF-5
BaF-8
LaK-17
SSK-9
CHROMATIC
ESIGN
CORRECTION
RATIONAL
ECTRUM
VELENGTH
FOCAL
PLANE
FIGURE 5. GEOCON LENS
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11,
1.0 1.0
MODULATION
TRANSFER
FUNCTION
6400
6000
5600
MODULATION
TRANSFER?
FUNCTION
0.5
L.
-20 0 [phone] 6600W 7200
( t
FOCAL POSITION
MICRONS) -0,2
WAVELENGTH ( ANGSTROMS)
(A) THROUGH FOCUS VARIATION (B) COLOR VARIATION AT BEST FOCUS
FOR THREE COLORS
FIGURE 6. MONOCHROMATIC MODULATION TRANSFER FUNCTIONS,
ON-AXIS, AT SPATIAL FREQUENCY OF 60 CY/ MM
1. RAY TRACE GIVES WAVEFRONT SHAPE IN EXIT PUPIL.
2. WAVEFRONT SHAPE GIVES PUPIL FUNCTION.
3. AUTOCORRELATION OF PUPIL FUNCTION GIVES REAL AND
IMAGINARY PARTS OF MONOCHROMATIC OPTICAL TRANSFER
FUNCTION.
4. WEIGHTED ADDITION AND NORMALIZATION OF MONOCHROMATIC
REAL AND IMAGINARY PARTS GIVES REAL AND IMAGINARY
PARTS OF POLYCHROMATIC OPTICAL TRANSFER FUNCTION.
FIGURE 7. STEPS IN COMPUTING MONOCHROMATIC
OPTICAL TRANSFER FUNCTIONS
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RELATIVE
INTENSITY
OPERATIONAL
SPECTRUM
5000A
A
A
A A A
AAAA
A AAA
AAAA
6000A'
A
WAVELENGTH
7000A
n=3
A n = 5
A A A n = 7
A A A An= 9
A A A A A fl=II
AAAALtA n=13
WAVELENGTH
INTERVALS FOR
SIMPSON'S RULE
INTEGRATION
FIGURE 8. APPROXIMATION OF CONTINUOUS SPECTRUM
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MODULATION
TRANSFER
FUNCTION
n= 3
-20
FOCAL POSITION ( MICRONS )
FIGURE 9. POLYCHROMATIC MODULATION TRANSFER ?FUNCTIONS,
ON AXIS, AT SPATIAL FREQUENCY OF 60. CY/MM, FOR DIF-
FERENT APPROXIMATIONS OF THE OPERATIONAL SPECTRUM
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.50 .50
MODULATION
TRANSFER ,n = 5
FUNCTION
.40
-20 -10 0 10 20
(A) RADIAL
,n7
II
n=13
"n=9
n=3
MODULATION
TRANSFER
FUNCTION
n = 3
.40
-20 -10 0 IO 20
(13) TANGENTIAL
FIGURE 10. POLYCHROMATIC MODULATION TRANSFER FUNCTIONS,
2P.7 DEGREES OFF-AXIS, AT SPATIAL FREQUENCY OF 20 CY/MM.
FOR DIFFERENT APPROXIMATIONS OF THE OPERATIONAL SPECTRUM
RELATIVE
INTENSITY
A LABORATORY TEST SPECTRUM
TUNGSTEN FILAMENT
WRATTEN 12 FILTER
S-I1 DETECTOR
/OPERATIONAL SPECTPUM
SUNLIGHT ( 6000?K BLACKBODY)
WRATTEN 12 FILTER
PANCHROMATIC FILM
5000 6000 7000
WAVELENGTH ( ANGSTROMS )
FIGURE II. TWO SPECTRA
8000
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?
OPERATIONAL
SPECTRUM
LABORATORY
SPECTRUM
MODULATION
TRANSFER
FUNCTION
1.0
0.5
20 CY/ MM
40 CY/ MM
} 60 CY/MM
-20 0 +20
FOCAL POSITION ( MICRONS)
FIGURE 12. POLYCHROMATIC ( n =13) MODULATION TRANSFER
FUNCTIONS, ON-AXIS, FOR SPECTRA OF FIGURE 3-8
PURPOSE OF
PREDICTION
REQUIREMENTS
SELECT AMONG
CANDIDATE
PHOTO-OPTICAL
SYSTEMS
MAKE
INTELLIGENT
DESIGN
"TRADE- OFFS"
SPECIFY
ACCEPTANCE
CRITERIA
FIXED
SPECIFICATION
TARGET FORM
GENERAL
CAMERA FORM
TARGET FORM
SPECIFIC
CAMERA FORM
TARGET FORM
ACTUAL CAMERA
ZERO IMAGE MOTION
BEST FOCUS
ASSUMPTIONS
NEEDED
IMAGE MOTION
FOCUS
DISTRIBUTION OF
IMAGE LOCATIONS
AND ORIENTATIONS
IMAGE MOTION
FOCUS
NONE
ITEMS WITH
VARIANCE
REQUIRING
PROBABILITYMEASUREMENT
TREATMENT
IMAGE MOTION
FOCUS
ATMOSPHERE
( IF DIFFERENT
FOR CANDIDATES)
FILM
IMAGE MOTION
FOCUS
RESOLUTION
TECHNIQUE
FIGURE 13. PREDICTION CONSIDERATIONS
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OPERATIONAL
SPECTRUM
LABORATORY
SPECTRUM
MODULATION
TRANSFER
FUNCTION
1.0
0.5
20 CY/ MM
40 CY/ MM
160 CY/MM
-20 0 +20
FOCAL POSITION ( MICRONS)
FIGURE 12. POLYCHROMATIC (n = I 3 ) MODULATION TRANSFER
FUNCTIONS, ON-AXIS, FOR SPECTRA OF FIGURE 3-8
PURPOSE OF
PREDICTION
REQUIREMENTS
SELECT AMONG
CANDIDATE
PHOTO-OPTICAL
SYSTEMS
MAKE
INTELLIGENT
DESIGN
"TRADE- OFFS"
SPECIFY
ACCEPTANCE
CRITERIA
FIXED
SPECIFICATION
TARGET FORM
GENERAL
CAMERA FORM
TARGET FORM
SPECIFIC
CAMERA FORM
TARGET FORM
ACTUAL CAMERA
ZERO IMAGE MOTION
BEST FOCUS
ASSUMPTIONS
NEEDED
IMAGE MOTION
FOCUS
DISTRIBUTION OF
IMAGE LOCATIONS
AND ORIENTATIONS
IMAGE MOTION
FOCUS
NONE
ITEMS WITH
VARIANCE
REQUIRING
PROBABILITY
TREATMENT
IMAGE MOTION
FOCUS
ATMOSPHERE
( IF DIFFERENT
FOR CANDIDATES)
FILM
IMAGE MOTION
FOCUS
-
RESOLUTION
MEASUREMENT
TECHNIQUE
FIGURE 13. PREDICTION CONSIDERATIONS
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NUMBER OF
READINGS OF
RESOLUTION
VALUE
RESOLUTION VALUE
FIGURE 14. RESOLUTION VARIATION
TARGET
TYPE
CAUSE
EDGE GRADIENT
US MILITARY STANDARD
TRI-BAR
LACK OF
"GROUND TRUTH"
OBJECT WAS NOT
A SHARP EDGE
CONTRAST
VARIATION
PAINT DETERIORATION OR
ATMOSPHERIC SCATTERING
FILM
VARIATION
TRANSFER FUNCTION
MODULATION DETECTABILITY
MEASUREMENT
MICRODENSITOMETER FOCUS,
SLIT ALIGNMENT AND DRIVE
ACCURACY; TRANSMISSION-
EXPOSURE CONVERSION;
COMPUTATIONAL TECHNIQUE.
DIFFERENT READERS AND
MICROSCOPES; READER
CONSISTENCY.
FIGURE 15. CAUSES OF RESOLUTION VARIATION, OTHER
THAN CAMERA SYSTEM
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(I) SOURCES OF POOR IMAGE QUALITY
NATURE OF IMAGE
PRIME CAUSE
ROTATIONALLY
SYMMETRICAL
RANDOM WAVEFRONT DISTORTION
FOCUS
SPHERICAL ABERRATION
NON-ROTATIONALLY
SYMMETRICAL
IMAGE MOTION
ASTIGMATISM
FOCUS ( NON-ROUND PUPIL)
DOUBLED
VIBRATION
WINDOW SEAM IN PRESSURIZED
CAMERA BAY
BENT MIRROR
(2) IS THE MAGNITUDE OF POSTULATED CAUSE REASONABLE
FOR THE SYSTEM ?
FIGURE 16. FAULT DIAGNOSIS
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