Restoration of faded images

A method of restoring a color image comprised of one or more colorants and which image may have faded over time, which image is represented by an image signal. The method uses both a restoration model and provides a means which enable a user to readily interact with the restoration process to obtain a final restoration. In a second aspect, the restoration model is used in conjunction with a lightness distribution mapping to provide a high quality restoration. The restoration model is a function of a variable time, and is the inverse of a model representing the fade rate of at least one of the colorants as a function of at least a variable time (and preferably also as a function of the other colorants). An apparatus for performing the method is provided. A computer readable medium has computer readable code means which can execute the method in a suitable computer.

FIELD OF THE INVENTION 
The invention relates generally to the field of imaging, and in particular 
to restoring hardcopy images composed of one or more colorants, which 
images may have faded over time. 
BACKGROUND OF THE INVENTION 
In the photographic or printing arts, color images are recorded in the form 
of one or more colorants on a base (such as paper or transparent plastic). 
For example, in the photographic arts the colorants in a developed image 
are typically cyan, magenta and yellow dyes arranged in layers on a 
support. In printing arts an additional black colorant is often used. A 
difficulty with such hardcopy images is that they tend to fade over time 
at rates which depend upon the chemical compounds of the colorants 
themselves and their environment. That is, they decompose or react to form 
other compounds which have absorption spectra different from the original 
colorants. For example, typical cyan, magenta and yellow dyes will 
decompose into mostly colorless compounds particularly under room light 
and more so under sunlight. 
Fading of an original hardcopy is clearly undesirable. Not only does the 
image become fainter, but since different ones of the component colorants 
will fade at different rates, undesirable color shifts occur. For example, 
an original image may take on an overall green tint in addition to 
becoming fainter. It is possible to attempt to enhance a faded hardcopy 
image by scanning it to obtain a digital image signal. The image is then 
displayed on a computer and a user can use known image processing software 
to increase color saturation and alter color balance on a trial and error 
basis until a visually pleasing image is obtained. Such a procedure is 
highly time consuming particularly when a large number of hardcopy images 
need to be processed. Also, as is the case with "enhancement" techniques, 
while the image is made more visually pleasing, such a method does not 
attempt to actually restore the faded image to how it actually appeared 
(that is, the resulting enhanced image may look little like the unfaded 
image). 
One method which attempts to provide a better rendition of color in images 
copied by digital copiers or the like, is histogram stretching. This is 
described, for example, by Gschwind et al. in The Journal of Photographic 
Science V. 38 (1990), Proceedings Issue, p. 99. This procedure simply 
assumes that any given image should have both black and white points and 
stretches the histogram for each color channel of an image to ensure that 
it does. For each color channel, all locations of the image having a given 
lightness value of a color channel have their values moved the same amount 
regardless of their environment in the image (that is, regardless of what 
other colorants may have been present at the same location in the original 
image). This technique does not attempt to restore faded images. 
Methods which can restore, at least in part, a faded color image, have been 
previously described by Gschwind et al. in Journal of Imaging Science and 
Technology V. 38, No. 6 (Nov./Dec. 1994) p. 513; Journal of Imaging 
Science and Technology V. 38, No. 6 (Nov./Dec 1994) p. 520; Journal of 
Photographic Science V. 38 (1990) p. 193; Journal of Photographic Science 
V. 41 (1993) p. 76; and Journal of Imaging Science and Technology V. 38, 
No. 6 (Nov./Dec. 1994) p. 513. In the foregoing articles a method is 
described wherein a linear bleach model of the dye is generated. The 
described methods require that numerical coefficients of the matrix 
bleaching equation be developed for each time of interest, from 
accelerated fading tests of the colorants. For rapid and simple processing 
of faded images, this would require a considerable amount of time and 
operator skill and familiarity with the software to process each faded 
image of interest. A considerable amount of subjective judgment is also 
required to obtain a final acceptable image. Further, such methods require 
the use of narrow band interference filters (which are not used on typical 
scanners) to minimize incorrectly interpreting a side band absorption of 
one colorant (which overlaps the main absorption of another colorant) as 
absorption of the other colorant. This is discussed in further detail by 
Gschwind et al. in Journal of Imaging Science and Technology V. 38 No. 6 
(Nov./Dec. 1994) p. 520 at p. 522-524. 
It would be desirable then to provide a method and apparatus which can 
provide good restoration of a faded image with no, or a relatively low 
level of, operator skill and intervention required. It would also be 
desirable that where operator intervention is required, that it can be 
readily provided by even completely inexperienced users. It would further 
be desirable that conventional scanners, which use wide band filters, 
could be used without the need to add special narrow band filters to them. 
SUMMARY OF THE INVENTION 
The present invention realizes that a more automated reconstruction of a 
faded image can be obtained by generating a fading equation with 
coefficients which are a function of time, and using the inverse of such 
equation (which still has coefficients as a function of time). To restore 
a faded image a user can then simply enter the time of fading and obtain 
one or more preliminary restorations. Furthermore, the present invention 
realizes that histogram stretching is in itself inherently very poor in 
attempting to restore a faded image and in fact, can cause further color 
distortions. This is so because histogram stretching alone changes the 
lightness of all locations in an image of a given color channel lightness 
value an equal amount, regardless of what other colorants may have been 
present in the original faded image at the same location. The present 
invention realizes though that a lightness distribution mapping following 
application of an inverse fading equation, can produce an enhanced 
restoration of a faded image in the form of what is referenced herein as a 
potential restoration. The present invention further realizes that a 
typical user who wishes to restore a faded image such as a photograph, may 
not know the actual time over which the image has faded. Therefore, the 
present invention further provides a very simple and convenient user 
interface which generates and displays a limited number of preliminary or 
potential restorations, but then allows the user to rapidly move through a 
much larger number for selected fade times based only on visual cues. 
The present invention then, provides in one aspect a method of restoring a 
color image. The color image is one which is comprised of one or more 
colorants, and which image may have faded over time. The image is 
represented by an image signal (such as may be obtained, for example, by 
scanning the original image). The method comprises: 
obtaining a restoration model which is a function of a variable time, which 
is the inverse of a model representing the fade rate of at least one of 
the colorants as a function of at least a variable time; 
selecting a time over which the image may have faded; 
applying the restoration model to the image signal to obtain a first 
preliminary restoration; 
selecting at least one further time over which the image may have faded; 
applying the restoration model to the image signal for each of the further 
times to obtain one or more further preliminary restorations; 
displaying the preliminary restorations; and 
upon a user selecting one of the displayed restorations which corresponds 
to a currently selected time, t.sub.c, in a first direction from another 
time, t.sub.i, to which another of the displayed restorations corresponds, 
then applying the restoration model to the image signal using an 
additional time, t.sub.a, to obtain an additional preliminary restoration, 
and displaying the additional preliminary restoration, wherein: 
EQU t.sub.a &gt;t.sub.c if t.sub.c &gt;t.sub.i, 
or 
EQU t.sub.a &lt;t.sub.c if t.sub.c &lt;t.sub.i. 
In a second aspect of the present invention a lightness distribution 
mapping is applied subsequent to application of the restoration model, to 
obtain what is referenced as a potential restoration. This aspect of the 
method comprises: 
obtaining a restoration model which is a function of a variable time, which 
is the inverse of a model representing the fade rate of at least one of 
the colorants as a function of at least a variable time; 
selecting a time over which the image may have faded; 
applying the restoration model to the image signal to obtain a first 
preliminary restoration; and 
then determining characteristics of a lightness distribution of at least 
one color channel for the first preliminary restoration, and when the 
characteristics do not meet preselected characteristics then modifying the 
first preliminary restoration so that the resulting first potential 
restoration meets the preselected characteristics. 
The method may particularly be applied to an image signal which is a 
representation of the image in a source independent space (such as logE 
space). 
A means for performing the method of the present invention are also 
provided. These means can be hardware or software implemented. 
The present invention can provide good restoration of a faded image with 
no, or a relatively low level of, operator skill and intervention 
required. Where operator intervention is required, the present invention 
provides a user interface which can be readily followed by even completely 
inexperienced users based on displayed restorations. The present 
invention, particularly where the image signal represents the image in a 
source independent space does not require the use of multiple restoration 
models which otherwise might be required when the images to be restored 
were obtained from various image sources (such as from different 
photographic paper types or other different media, or different input 
scanners). These and other aspects, objects, features and advantages of 
the present invention will be more clearly understood and appreciated from 
a review of the following detailed description of the preferred 
embodiments and appended claims, and by reference to the accompanying 
drawings.

EMBODIMENTS OF THE INVENTION 
Referring first to FIG. 1A, there is diagramatically shown a vertical 
cross-section through a typical image carrying element 2. Element 2 may, 
for example, represent a processed color photographic film. For 
illustrative purposes different locations in the two-dimensional image are 
shown as vertical columns 40a through 40g. Element 2 has three layers, 
namely a cyan colorant carrying layer C, magenta colorant carrying layer 
M, and yellow colorant carrying layer Y. For convenience, specific 
locations within the three dimensional element will be referenced by the 
colorant layer type followed by a column location (a column location alone 
being a location in the two-dimensional image). For example, the location 
of the yellow layer at column 40g will be referenced as Y40g. The location 
of the cyan layer in column 40g would be C40g. Lighter or fewer lines for 
a given colorant indicate less of that colorant while no shading at all 
indicates no colorant at that location. For example, there is no cyan 
colorant at locations C40b, C40f and C40g. Whether any one of those 
colorants is present at a particular location and their amounts will 
depend on the image being carried by element 2. For example, some 
locations of the image, such as column 40g, will carry only yellow 
colorant (and hence appear yellow in color to a viewer), while other 
locations, such as column 40a, will carry all three colorants (and hence 
will appear black to a viewer). In typical transparent photographic 
elements, cyan layer C would rest on a transparent base while in typical 
reflective photographic elements, yellow layer 30 would rest on a 
reflective base (such as paper). For the purposes of the discussion below, 
it will be assumed that the yellow layer Y rests on a reflective base (not 
shown) such that element 2 is normally viewed by reflected light coming 
from the direction above cyan layer C. However, it will be understood that 
no particular configuration is essential for the present invention. 
Referring now to FIG. 1B, the results of fading of the image which may 
occur particularly after prolonged exposure of the image to light, can be 
seen. The light which causes fading (mostly, but not only, ultraviolet 
light) will be absorbed by each dye in turn, as well as the carrier 
material of each layer (for example, the gelatin gel in a photographic 
film). This means that the same colorant will tend to experience less 
fading if it has other dyes above it, and further that as between two dyes 
which have no other colorants above them those lower in the film (that is 
toward yellow layer Y) will tend to experience less fading. Thus, yellow 
dye at Y40a will tend to experience less fading than yellow dye at Y40b or 
Y40c, which in turn will both have less fading than yellow dye at Y40g. 
Similarly, magenta dye at M40a and M40d will tend to fade less than 
magenta dye at M40b and M40f. Thus, the fading of a particular image 
colorant in a multilayer element, such as photographic film or paper, is 
not simply a function of the particular colorant in question, the light 
intensity and time, but is also a function of the amount of other dyes 
above it at each location in the two-dimensional image. 
Because of the dependence on fading of a given colorant on the amounts of 
other colorants present at the same image location in a multilayer 
element, a histogram stretch of each color channel necessarily fails when 
used to attempt to restore a faded image. This can be seen from FIGS. 2A 
and 2C. For the purposes of discussion, these figures show a histogram of 
a yellow channel. Such histograms can be obtained in a well known manner. 
For example, the image in element 2 may be scanned to produce a digital 
image of x by y pixels with each color channel having a range of color 
values (typically 255 values in each of three color channels in a 24-bit 
color system). Then, for the color channel in question the frequency of 
occurrence of each digital value is plotted against that value. Basically, 
a simple histogram stretch fails because it treats all given color channel 
values equally, without regard to the location in the image from which 
each pixel having that value came (that is, it fails to account for the 
presence, absence, or amount of other colorants above the one of 
interest). 
For example, in FIG. 2A the histogram shows the yellow color channel for an 
image with higher values representing higher color saturation. Value 150 
has been arbitrarily assigned to represent a yellow lightness value of 
locations Y40a Y40b and Y40g in element 2. Following prolonged exposure to 
light all yellow values will fade to some extent such that the histogram 
is shifted to the left (that is, toward lighter colors). However, as 
explained above, region Y40g will fade more Y40b, which in turn will fade 
more than Y40a. Thus, the foregoing regions will have their respective 
lightness values after fading as shown in FIG. 2B. Note that they are no 
longer the same lightness values. A simple histogram stretch of the faded 
image represented by FIG. 2B merely attempts to shift the overall curve 
shape back to a more symmetric distribution such as shown in FIG. 2C. 
However, such a simple histogram stretch fails to account for the fact 
that the values for Y40g, Y40b and Y40a in FIG. 2B need to be mapped 
according to different functions which take account of the other dyes 
present at their respective locations. 
The present invention uses a model to account for the presence of other 
colorants at the same location while realizing that the model alone will 
not yield a relatively good restoration. Numerous models could be chosen, 
with exponential models being most likely based on the photochemical 
processes involved in fading. However, the preferred model is a linear 
fading model as the model needs to be descriptive of the actual changes in 
dye density observed in controlled fading experiments and in most cases 
the fading data is too noisy to take advantage of more sophisticated 
models. 
The preferred linear fading model has the form: 
EQU C(t)=f.sub.1,1 (t)C.sub.0 +f.sub.1,2 (t)M.sub.0 +f.sub.1,3 (t)Y.sub.0 
EQU M(t)=f.sub.2,1 (t)C.sub.0 +f.sub.2,2 (t)M.sub.0 +f.sub.2,3 (t)Y.sub.0 1! 
EQU Y(t)=f.sub.3,1 (t)C.sub.0 +f.sub.3,2 (t)M.sub.0 +f.sub.3,3 (t)Y.sub.0 
where: C(t), M(t) and Y(t) are the analytical densities of the cyan, 
magenta and yellow colorants of the image after fading; C.sub.0, M.sub.0 
and Y.sub.0 are the initial analytical densities of the three colorants 
before any fading occurred; and f.sub.x,y (t) are coefficients which are 
functions of t. The extent of fading is parameterized in the variable t 
which can be likened to an exposure variable, being the product of the 
average intensity of fading light and the time which the image has been 
exposed to the fading light. The units of t in this implementation are 
room-years. This is the number of years that the image has been exposed to 
fading light equal in intensity to an average room. Note that each of the 
coefficients f.sub.x,y is a function of a variable time and therefore the 
model as a whole is a function of a variable time. 
Equation 1! can be expressed in matrix notation as: 
##EQU1## 
In the preferred embodiment, because of limitations in the fading data, 
the matrix coefficients were implemented as simple linear functions. 
Therefore the fading model becomes 
##EQU2## 
where the a.sub.i,j are constants that are derived from an analysis of the 
fading data. 
To obtain the values for a.sub.x,y (t) it is necessary to artificially fade 
colored patches of various known initial lightness values in each of the 
three color channels. The patches should include appropriate colorant 
mixtures in addition to the colorants alone. The change in lightness value 
of each of the color channels is recorded as a function of the total 
exposure to fading light which corresponds to the product of the intensity 
of the fading light and exposure time. The relationship between exposure 
to fading light and the change in lightness value in each of the color 
channels is then modeled using a suitable function, in this case the 
linear model described above. 
Following the foregoing procedure, the fading matrix was developed from 
data based on a kinetic analysis of KODAK EKTACOLOR PLUS daylight fading 
characteristics. This led to the following model: 
##EQU3## 
The original analytical densities of the three colorants in the image can 
now be calculated from the inverse of the matrix in Equation (4), namely: 
##EQU4## 
Note that the restoration model of equation (5) is still a function of t 
(room-years). Thus, once Equation (5) has been obtained, then for any 
faded image it is only necessary to obtain t under daylight conditions to 
calculate the original dye densities C.sub.0, M.sub.0 and Y.sub.0. It is 
not necessary to re-evaluate different co-efficients for different values 
of t. 
It is apparent from the equation above that singularities in the 
restoration matrix exist at points where the fade matrix becomes singular. 
These points can be found from the roots of the cubic equation for the 
determinant. The roots in this case are approximately 63.6, 114.4 and 
2831.7 room years. As the restoration time, t, approaches any of these 
roots, numerical precision limitations in the computer result in 
fluctuating and inaccurate restoration matrices being generated, with gain 
factors (a.sub.11, a.sub.22, a.sub.33) that are excessively large. This 
leads to a poor restoration. In order to prevent this problem the t 
parameter is transformed by means of the following equation: 
EQU t.sub.c =60(1-e.sup.-t/60) 5A! 
Equation (5A) introduces a soft clip into the t parameter, causing it to 
asymptotically approach 60, but never reach it. 
The restoration equation in the preferred embodiment then becomes: 
##EQU5## 
where Det=1-0.024821 t.sub.c +1.46128H10.sup.-4 t.sub.c.sup.2 
-4.85529H10.sup.-8 t.sub.c.sup.3 
b.sub.1,1 =1-0.022196 t.sub.c +1.01576H10.sup.-4 t.sub.c.sup.2 
b.sub.1,2 =-0.001421 t.sub.c +2.32142H10.sup.-5 t.sub.c.sup.2 
b.sub.1,3 =-0.001807 t.sub.c +2.75701H10.sup.-5 t.sub.c.sup.2 
b.sub.2,1 =-0.005784 t.sub.c +7.84850 H10.sup.-5 t.sub.c.sup.2 
b.sub.2,2 =1-0.013538 t.sub.c +2.31533 H10.sup.-5 t.sub.c.sup.2 
b.sub.2,3 =-0.005054 t.sub.c +2.37184 H10.sup.-5 t.sub.c.sup.2 
b.sub.3,1 =-0.003040 t.sub.c +5.89691 H10.sup.-5 t.sub.c.sup.2 
b.sub.3,2 =-0.004265 t.sub.c +1.55155 H10.sup.-5 t.sub.c.sup.2 
b.sub.3,3 =1 -0.013908 t.sub.c +2.13988 H10.sup.-5 t.sub.c.sup.2 
Although these were obtained using data for EKTACOLOR PLUS, it has been 
found in practice that they can be applied with good success when used 
with the lightness distribution mapping and log exposure metric (as 
described below), to many different photographic prints. 
However, the present invention appreciates there are several factors which 
inherently limit the accuracy of the inverse fade restoration procedure. 
These include: 
1. The limited accuracy of the original fade data from which the model was 
constructed. 
2. The fact that the model parameters pertain to a particular photographic 
paper, fading under certain controlled conditions. In practical situations 
a range of different origination materials and fade conditions will be 
encountered. Thus, while the model can still produce good results even 
with various faded images, it's accuracy will vary. 
3. The restoration model should be applied in an analytical density metric 
but a number of system advantages can be obtained by processing images by 
using a signal representation of the image in a source independent space, 
for example a log exposure metric. It is therefore preferred to apply the 
restoration matrix to the image signal representation in the preferred log 
exposure signals. 
The foregoing errors may appear as either: 
1. incorrect contrast; 
2. Incorrect density; or 
3. slight overall color balance error. 
The present invention realizes that a lightness distribution mapping 
following, but not preceding, application of the inverse fade model to the 
matrix can further improve the restoration of a faded image. 
In particular, the preferred method used for histogram mapping on a 
digitized original image to which the restoration model has been applied 
to generate a preliminary restoration, is as follows: 
1. Compute the lightness histograms of each of the red, green and blue 
color channels after application of the restoration model. 
2. Compute the cumulative histograms for the each of the red, blue and 
green color channels. 
3. Next, the histogram of the preliminary restoration is to be mapped so 
that the percentage of the image having values above a preselected white 
point and below a preselected black point, will be typical of that for an 
ideal image. This is done as follows: 
One obtains white and black values, w and b, respectively from a cumulative 
histogram (FIG. 2F) of an ideal image (a histogram for which is shown in 
FIG. 2E). This can be done by preselecting proportions of the image that 
have values lying above a visual "white" and below a visual "black", and 
obtaining the corresponding white and black values, w and b, from FIG. 2F. 
Alternatively, one can adjust the white and black values empirically based 
upon a subjective judgement of a number of actual images. Once determined 
the white and black values, w and b, respectively, are assigned as 
constants for any image. 
Then, the values of Vu and Vl are calculated for each channel which 
correspond to the same percentiles 62 and 60. These are shown 
diagramatically on the cumulative histogram of FIG. 2H (where FIG. 2G is 
an actual histogram of a preliminary restoration). A lightness mapping of 
the lightness values in each color channel is then performed for each 
color channel as follows: 
where: 
V.sub.u --The upper percentile point on the histogram 
V.sub.1 --The lower percentile point on the histogram 
b--The black reference value 
w--The white reference value 
g--The mid-grey reference value 
Then calculate a mid-gray, V.sub.m for the input histogram: 
##EQU6## 
Calculate the auto shift, S: 
EQU S=g-V.sub.m 
Calculate the auto gain, G.sub.a : 
##EQU7## 
If an additional, manual gain (contrast) adjustment G.sub.m is also in 
effect, then calculate the effective total gain G.sub.eff from the 
equation below. If there is no manual gain (contrast) adjustment in place 
then use G.sub.m =1: 
EQU G.sub.eff =1+G.sub.m (G.sub.a -1) 
Finally, modify the image: 
EQU V.sub.out =G.sub.eff (V.sub.in -V.sub.m)+V.sub.m +S 
All pixels above the upper percentile limit will be mapped to whites whiter 
than the reference white and all pixels below the lower percentile will be 
mapped to blacks below (blacker than) the reference black, and pixels in 
between will be mapped linearly according to the above equations. In our 
preferred embodiment the percentiles chosen lie in the ranges: 
0% to 5% for the lower percentile corresponding to black point (0% is 
preferred for this implementation); 
95%-100% for the upper percentile, corresponding to the white point (100% 
is preferred for this implementation). 
Note that if the percentile values are altered of course, the white and 
black values would also be altered accordingly. Further, a function other 
than a linear function could be used for the mapping, but the above linear 
function is preferred. However, any function chosen should preferably be 
monotonically increasing. That is, as the input lightness value increases, 
the output lightness value must also increase (note that this does not 
necessarily mean that the output value has to be greater than the input 
value). 
The preferred mapping point for the reference black is at a code value of 
between 40 and 70 (50 is preferred) (using 8 bits per color channel of 
dynamic range) in log exposure space. The preferred mapping point for the 
reference white is at a code value of between 200 and 255 (230 Is 
preferred) (using 8 bits per color channel of dynamic range) in log 
exposure space. 
The fact that application of the restoration model alone does not provide 
an ideal restoration, but when combined with a subsequent histogram 
mapping can yield an improved restoration, is illustrated in FIGS. 3A, 3B, 
4A and 4B. FIG. 3A illustrates RED, GREEN and BLUE color channel 
histograms from a typical faded color negative image but similar 
considerations apply to color positive images also. Note that in the 
unfaded image all of the histograms would appear to more closely coincide 
and be centered about mid-way between the darkest value of 0 and the 
lightest value of 255. Applying the restoration model described above 
results in a preliminary image histogram such as shown in FIG. 3B. Note 
again that, unlike a simple histogram stretching, that application of the 
restoration model, for each color dye, accounts at every pixel for the 
presence and amounts of the other two dyes, as described above. 
However, the preliminary restoration is not ideal, as can be seen from FIG. 
3B, for the reasons discussed above. More particularly, referring to FIG. 
4A, there is illustrated the fading over time of a neutral section of an 
image. Before any fading (that is, at 0 Room-Years) the RED, GREEN and 
BLUE Density values will all be the same at position 90. Note that the 
section fades over time along respective lines 100, 102 and 104 to become 
both non-neutral and much lower in overall density. Due to the limitations 
of the restoration matrix discussed above, the application of the 
restoration matrix effectively moves the RED, GREEN and BLUE density 
values back along the broken lines 100a, 102a, and 104a to their 
intersections 101, 103, and 105 with the "Density" axis at 0 Room-Years 
fade time, rather than the ideal of moving them back along the solid lines 
100, 102 and 104. Thus, while the image is partially restored an ideal 
restoration is not obtained from the restoration model alone. 
The lightness distribution mapping then achieves the following: 
1. It forces the very light and very dark portions of the image to be 
neutral. 
2. It compensates for errors in restoration introduced by the limitations 
of the restoration model. 
3. It compensates for the compression and errors introduced by applying the 
restoration model in a log exposure space rather than an analytical 
density space. 
By subsequently applying the lightness distribution mapping as described 
above, white and grey points in the image are forced to neutral. This can 
be seen by reference to FIGS. 3B and 4B. Because each color channel 
lightness distribution is mapped in the manner described above (with 
common proportions mapped to the same white and black point values), the 
result will be FIG. 4B. That is, in FIG. 4A, RED, GREEN and BLUE densities 
are forced from density values 101, 103, and 105, respectively, back to 
the density value at position 90. 
Thus, application of a lightness distribution mapping (which alone cannot 
effectively restore faded images) to a preliminary restoration produced 
from application of the reverse fading model (which alone produces a rough 
restoration) yields a reasonably good restoration. 
Referring now to FIG. 5, there is shown a block diagram of an apparatus 
that can be used to carry out the present invention. The apparatus 10 has 
a scanner 12 for scanning an original input image 14, which in FIG. 5 is 
in the form of a faded print. Scanner 12 obtains a digital image signal 
representation of input image 14 in scanner RGB space, which is input to a 
workstation 16. Workstation 16 may conveniently be an appropriately 
programmed digital computer or equivalent hardware circuitry. A control 
apparatus 36 allows for user input, and may typically be a keyboard and 
mouse combination. Workstation 16 converts the digital image signal from 
scanner 12 RGB space into a source independent space. Techniques for 
converting from a source dependent space to a source independent space are 
described, for example, in U.S. Pat. No. 4,979,032, U.S. Pat. No. 
5,267,030, and U.S. Pat. No. 5,420,979. Workstation 16 also processes the 
image using the restoration model and lightness distribution mapping as 
described above and further below. Potential restorations are converted 
from a source independent space to a video R'G'B' color space by 
workstation 16, and are displayed on a monitor 18. When a user desires, a 
selected potential restoration can be sent to an output device 38 (which 
could, for example, be a color printer). Note that workstation 16 would 
convert the selected potential restoration from device independent space 
to output device R"G"B" color space code values. 
A typical screenshot as might be seen on monitor 18, is depicted in FIG. 
5A. There are shown a 3.times.3 matrix of low resolution potential 
restorations (although it will be appreciated that other matrices or 
arrangements could be used). Potential restorations in column A assume a 
shorter fade restoration time than column B, whereas those in column C 
assume a greater fade restoration time than column B. Fade restoration 
times are the same within a given column, and thus incrementally increase 
from column to column in the direction of arrow 28. Because of the matrix 
nature of the display, another image manipulation parameter can be 
displayed in different rows. In FIG. 5A it will be assumed that rows 22, 
24, and 26 show different contrast factors (with contrast increasing 
incrementally in direction of arrow 30 from row to row), the contrast 
factor in a given row being constant. Thus, as described in further detail 
below, it is a simple matter for a user to not only restore an image, but 
enhance the restored image (note that the original image may not have been 
that pleasing to the eye, such that enhancement may be desirable). Control 
apparatus 36 allows a user, to point and select any of the potential 
restorations displayed on monitor 18 at any given time. Also, functions 
from a control bar 20 can be selected. Such functions include a selection 
of the increments in the fade time and contrast parameters, a Done button, 
a Revert button (which allows a user to return to the original scanned 
image) as well as a command to send a selected potential restoration to 
output device 38, and an auto-restoration which assumes a typical 
preselected fade time and applies the restoration model and lightness 
distribution mapping accordingly. 
A method according to the present invention is shown in the flowchart of 
FIG. 6. Such a method can be executed on the apparatus of FIG. 5, the 
workstation 16 of which contains a suitable program (that is, a computer 
program code means) for performing the method. The program can be stored 
on any suitable computer readable storage medium. The computer readable 
storage medium can include, for example: magnetic storage media such as 
magnetic disc (such as a floppy disc) or magnetic tape; optical storage 
media such as optical disc, optical tape, or machine readable bar code; 
solid state electronic storage devices such as random access memory (RAM), 
or read only memory (ROM); or any other physical device or medium employed 
to store a computer program. 
Referring to FIG. 6, the original image is scanned and digitized 106 to 
obtain a digital image signal in scanner RGB space. The digital image 
signal is then converted 107 to logE space, and is then subsampled 108 to 
provide lower resolution images for display purposes as will be described. 
An initial restoration time, t.sub.i, and contrast adjustment factor, c, 
are retrieved 109 from the memory in workstation 16. Similarly, an initial 
time increment, dt, and contrast increment, dc, are retrieved 110 from 
memory. The initial values for t.sub.i, c, dt, and dc, are pre-selected by 
the programmer, although they could of course be altered. Workstation 16 
then calculates 111 restoration matrices using the initial values, for 
t.sub.i, t.sub.i .+-.dt and the general restoration matrix stored in the 
program, and applies them 116 to the sub-sampled image to produce three 
preliminary restorations. Contrast modification factors, c, c-dc, and c+dc 
are then applied 118 to the three preliminary restorations giving now a 
total of 9 preliminary restorations (representing three different 
restoration times at three different contrast levels). The 9 images are 
then displayed 120 in a 3.times.3 grid as shown in FIG. 5A and described 
above. At this point the user can use control apparatus 36 to select 122 a 
preferred preliminary restoration, or alternatively could select 128 an 
auto button or select 134 a Done button. Optionally, a user could select 
from among a number of additional restoration models sorted in the memory 
of workstation 16. These models would be optimized for different media or 
fading conditions. If the user selects 122 a preferred preliminary 
restoration, then the restoration time, t.sub.c, and contrast adjustment 
factor, c, associated with that selected preliminary restoration is 
obtained 124. New restoration matrices are calculated 126 for t.sub.c -dt 
(an additional time), t.sub.c and t.sub.c +dt (another additional time), 
and applied 118 to the subsampled image along with the contrast factors. 
If the user selects 128 the auto-button lightness distribution 
characteristics of each color channel in the currently preferred 
preliminary restoration are obtained 130 in the form of the maximum and 
minimum lightness values of each color channel. These values are input 
into the linear lightness distribution mapping function described above. 
The lightness distribution mapping function is now applied 132 to all nine 
images in the grid, and the newly calculated images displayed 120, with 
the selected image always be displayed in the center of the grid. At this 
point a user can again go through the loops commencing with step 122 or 
128. When the user is satisfied with the restoration displayed in the 
center of the grid, he selects 134 the Done button. The restoration and 
lightness distribution mapping parameters, and contrast factor, of the 
center image are then applied 135 to the entire original digital image 
signal (in the device independent space). The resulting image can then be 
stored and/or sent to output device 38 or further manipulated using other 
image processing tools after exiting 136 from the routine. 
After application of the histogram stretch and scale, an optional 
additional contrast boost or reduction may be applied by the user. This 
can be used to change the contrast of the image to the customer's 
reference. 
The invention has been described with reference to a preferred embodiment. 
However, it will be appreciated that variations and modifications can be 
effected by a person of ordinary skill in the art without departing from 
the scope of the invention. 
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TS LIST 
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C, M, and Y Layers 
2 Element 
10 Apparatus 
12 Scanner 
14 Input Image 
16 Workstation 
18 Monitor 
20 Control Bar 
22, 24, and 26 Rows 
28 Arrow 
30 Arrow 
36 Control Apparatus 
38 Output Device 
40a through 40g Vertical Columns 
60 and 62 Percentiles 
90 Position 
100-136 Steps 
100, 102 and 104 Lines 
100a, 102a, and 104a 
Broken Lines 
101, 103, and 105 Density Values 
101, 103, and 105 Intersections 
150 Value 
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