Title: Selective smoothing and sharpening of images by generalized unsharp masking
Abstract: A method of deriving from an existing selective image smoothing filter either a corresponding filter for selective image sharpening, or a corresponding filter for both selective image smoothing and selective image sharpening. The selective sharpening filter can be implemented quickly by using implementations of the existing selective smoothing filter and a derived matching non-selective smoothing filter as black boxes and combining their outputs in a simple manner. Alternatively, the derived selective sharpening filter can be implemented by inlining the combination of the implementations of the existing selective smoothing.
Patent Number: 6,980,696 Issued on 12/27/2005 to Maurer
| Inventors:
|
Maurer; Ron P. (Haifa, IL)
|
| Assignee:
|
Hewlett-Packard Development Company, L.P. (Houston, TX)
|
| Appl. No.:
|
683148 |
| Filed:
|
October 9, 2003 |
| Current U.S. Class: |
382/262; 382/262; 382/263; 382/264 |
| Intern'l Class: |
G06T 005/50; G06K 009/40 |
| Field of Search: |
382/261,263,264
|
References Cited [Referenced By]
U.S. Patent Documents
Other References
Andrea Polesel et al; "Image Enhancement via Adaptive Unsharp Masking" IEEE Transactions
on Image Processing, vol. 9, No. 3, Mar. 2000 pp. 505-510.
|
Primary Examiner: Johns; Andrew W.
Assistant Examiner: Edwards; Patrick L.
Parent Case Text
"This application is a continuation of application Ser. No. 09/676201, filed
on Sep. 29, 2000, now U.S. Pat. No. 6,665,448."
Claims
1. A method of designing a selective sharpening image processing filter comprising:
selecting a pre-existing selective smoothing filter having an associated selectivity mechanism;
deriving a matching non-selective smoothing filter from the pre-existing selective
smoothing filter by disabling its selectivity mechanism;
in an unsharp masking filter having an associated high-pass filter operation,
substituting a difference operation of the selective smoothing filter and the derived
matching non-selective smoothing filter for the high-pass filter operation to form
the image processing filter, wherein the difference operation generates a filter
difference result which is mapped according to a selected function.
2. The method as described in claim 1 wherein the selected function is non-decreasing.
3. The method as described in claim 1 wherein the selected function is a saturated
linear mapping.
4. The method as described in claim 1 wherein the function limits the filter
difference result to at least one selected threshold value.
5. A method of designing a selective smoothing and selective sharpening image
processing filter comprising:
selecting a pre-existing selective smoothing filter having an associated selectivity mechanism;
deriving a matching non-selective smoothing filter from the pre-existing selective
smoothing filter by disabling its selectivity mechanism;
in an unsharp masking filter having an associated high-pass filter operation,
substituting a difference operation of the selective smoothing filter and the derived
matching non-selective smoothing filter for the high-pass filter operation, wherein
the difference operation generates a filter difference result which is mapped according
to a selected function; and
substituting selectively smoothed image pixel values for image pixel values in
the unsharp masking filter, wherein the selectively smoothed image pixel values
are added to the scaled difference operation in the unsharp masking filter to form
the image processing filter.
6. The method as described in claim 5 wherein the selected function is non-decreasing.
7. The method as described in claim 5 wherein the selected function is a saturated
linear mapping.
8. The method as described in claim 5 wherein the function limits the filter
difference result to at least one selected threshold value.
9. A method of designing a selective sharpening image processing filter comprising:
selecting a pre-existing selective smoothing filter having an associated selectivity mechanism;
deriving a smoothing filter from the pre-existing selective smoothing filter
by setting its selectivity mechanism to a weaker selectivity state;
in an unsharp masking filter having an associated high-pass filter operation,
substituting a difference operation of the selective smoothing filter and the derived
smoothing filter for the high-pass filter operation to form the image processing
filter, wherein the difference operation generates a filter difference result which
is mapped according to a selected function.
10. The method as described in claim 9 wherein the selected function is non-decreasing.
11. The method as described in claim 9 wherein the selected function is a saturated
linear mapping.
12. The method as described in claim 9 wherein the function limits the filter
difference result to at least one selected threshold value.
13. A method of designing a selective smoothing and selective sharpening image
processing filter comprising:
selecting a pre-existing selective smoothing filter having an associated selectivity mechanism;
deriving a smoothing filter from the pre-existing selective smoothing filter
by setting its selectivity mechanism to a weaker selectivity state;
in an unsharp masking filter having an associated high-pass filter operation,
substituting a difference operation of the selective smoothing filter and the derived
smoothing filter for the high-pass filter operation, wherein the difference operation
generates a filter difference result which is mapped according to a selected function; and
substituting selectively smoothed image pixel values for image pixel values in
the unsharp masking filter, wherein the selectively smoothed image pixel values
are added to the scaled difference operation in the unsharp masking filter to form
the image processing filter.
14. The method as described in claim 13 wherein the selected function is non-decreasing.
15. The method as described in claim 13 wherein the selected function is a saturated
linear mapping.
16. The method as described in claim 13 wherein the function limits the filter
difference result to at least one selected threshold value.
17. A method of processing image data including a plurality of pixel values:
for each pixel value of interest of the image data, selecting a neighborhood
of pixels including the pixel value of interest;
applying a selective smoothing filter to the neighborhood to obtain a first filtered
pixel value;
applying a non-selective smoothing filter to the neighborhood to obtain a second
filtered pixel value, wherein the non-selective smoothing filter is derived from
the selective smoothing filter by disabling its selectivity mechanism;
multiplying by a sharpening factor the difference of the first filtered pixel
value and the second filtered pixel value to obtain a filtered pixel difference value;
in a first case, adding the pixel value of interest to the filtered pixel difference
value to obtain a first enhanced pixel of interest value; and
in a second case, adding the first filtered pixel value to the filtered pixel
difference value to obtain a second enhanced pixel of interest value.
18. The method of claim 17 wherein the selective smoothing filter and the derived
matching non-selective smoothing filter have common computations and these common
computations are performed by a single computational step such that each pixel
value of interest processed by the method is processed only once to determine the
first filtered pixel value and the second filtered pixel value.
19. A method of processing image data including a plurality of pixel values:
for each pixel value of interest of the image data, selecting a neighborhood
of pixels including the pixel value of interest;
applying a selective smoothing filter to the neighborhood to obtain a first filtered
pixel value;
applying a non-selective smoothing filter to the neighborhood to obtain a second
filtered pixel value, wherein the non-selective smoothing filter is derived from
the selective smoothing filter by setting its selectivity mechanism to a weaker
selectivity state;
multiplying by a sharpening factor the difference of the first filtered pixel
value and the second filtered pixel value to obtain a filtered pixel difference value;
in a first case adding the pixel value of interest to the filtered pixel difference
value to obtain a first enhanced pixel of interest value; and
in a second case adding the first filtered pixel value to the filtered pixel
difference value to obtain a second enhanced pixel of interest value.
20. The method of claim 19 wherein the selective smoothing filter and the derived
matching non-selective smoothing filter have common computations and these common
computations are performed by a single computational step such that each pixel
value of interest processed by the method is processed only once to determine the
first filtered pixel value and the second filtered pixel value.
21. A system of processing image data including a plurality of pixel values:
a selective smoothing filter for applying to a neighborhood of pixel values of
the image data including a pixel value of interest to obtain a first filtered pixel value;
a non-selective smoothing filter for applying to the neighborhood to obtain a
second filtered pixel value, wherein the non-selective smoothing filter derived
from the selective smoothing filter by disabling its selectivity mechanism;
a means for multiplying the difference of the first filtered pixel value and
the second filtered pixel value by a sharpening factor to obtain a filtered pixel
difference value;
a means for selecting one of the pixel value of interest and the first filtered
pixel value;
a means for adding, in a first case, the pixel value of interest to the filtered
pixel difference value to obtain a first enhanced pixel of interest value and,
in a second case, the first filtered pixel value to the filtered pixel difference
value to obtain a second enhanced pixel of interest value.
22. The system described in claim 21 wherein the selective smoothing filter and
the derived matching non-selective smoothing filter have common computations and
these common computations are performed by a single computational step such that
each pixel value of interest processed by the method is processed only once to
determine the first filtered pixel value and the second filtered pixel value.
23. A system of processing image data including a plurality of pixel values:
a selective smoothing filter for applying to a neighborhood of pixel values of
the image data including a pixel value of interest to obtain a first filtered pixel value;
a non-selective smoothing filter for applying to the neighborhood to obtain a
second filtered pixel value, wherein the non-selective smoothing filter derived
from the selective smoothing filter by setting its selectivity mechanism to a weaker
selectivity state;
a means for multiplying the difference of the first filtered pixel value and
the second filtered pixel value by a sharpening factor to obtain a filtered pixel
difference value;
a means for selecting one of the pixel value of interest and the first filtered
pixel value;
a means for adding, in a first case, the pixel value of interest to the filtered
pixel difference value to obtain a first enhanced pixel of interest value and,
in a second case, the first filtered pixel value to the filtered pixel difference
value to obtain a second enhanced pixel of interest value.
24. The system described in claim 23 wherein the selective smoothing filter and
the derived matching non-selective smoothing filter have common computations and
these common computations are performed by a single computational step such that
each pixel value of interest processed by the method is processed only once to
determine the first filtered pixel value and the second filtered pixel value.
Description
FIELD OF THE INVENTION
The present invention relates to processing of image data and in particular to
the enhancement of images by sharpening and smoothing filtering.
BACKGROUND OF THE INVENTION
In many image-processing applications it is desirable to apply both smoothing
and sharpening to image data in order to improve their appearance. In the linear-filtering
domain, smoothing is done by attenuating high-frequency components of the image
(low-pass filtering). Alternatively, sharpening is done by amplifying high-frequency
components, also known as Unsharp Masking (USM), which is expressed mathematically
as:
##EQU1##
where x is the input image signal, y is the output image signal, λ is
a real constant termed "the sharpness gain", and H is a linear high-pass filter.
The linear high-pass filter H can also be expressed as the difference between an
identity-filter I and a linear low-pass filter L. The main advantage of linear
filters for denoising or sharpening is their simplicity and efficiency. Unfortunately,
sharpening and denoising undo each other's operation, so that achieving both effective
denoising and effective sharpening is not possible with linear-filters. This remains
true even when the denoising and sharpening are performed in separate steps.
Many selective denoising techniques have been investigated, which effectively
attenuate selected types of noise without smoothing edges. These techniques do
not utilize the selectiveness of the denoising filter to enhance edges and instead
just leaved them un-smoothed. In a similar manner, many selective sharpening methods
are known which effectively enhance edges without attenuating small amplitude noise
in flat regions. These techniques do not utilize the selectiveness of the sharpening
filter to denoise non-edge regions and instead just leave them unsharpened.
There are also many image-enhancement techniques that are known, which perform
both denoising and sharpening. Most are based on a hard classification of neighborhoods
corresponding to "non-features" (e.g., background, noise), and "features" (edges).
Then a denoising algorithm is applied to "non-feature" neighborhoods and an unrelated
sharpening algorithm is applied to "feature" neighborhoods. One limitation of such
an approach is its relatively high computational complexity. Specifically, there
are two separate operations that are performed at each pixel: a block/neighborhood
classification and either a smoothing or a sharpening operation. Another limitation
of the "hard" classification approach is the possibility of artifacts due to misclassifications,
especially in noisy images.
This drawback can be eliminated, in part, by performing another technique in
which both a smoothing operation and an unrelated sharpening operation is performed
on each pixel and then the results of the smoothing and sharpening operations are
mixed using a soft-decision function. However, this technique increases the computational
complexity of the image enhancement process even more, since now both the smoothing
algorithm and the unrelated sharpening algorithm needs to be applied at each pixel.
A simpler method for combining smoothing and sharpening is based on linear unsharp
masking, (Eq. 1) by modifying the local "sharpness gain factor" λ(ij) such
that it has positive values (sharpening) in activity regions but negative values
(smoothing) in flat regions. The local sharpness gain factor λ(ij) is in
fact a soft-decision factor corresponding to a measure of the desired feature (activity).
The computational complexity of this method is still relatively high since at each
pixel both the high-pass filter response (H*x) and the activity measure λ(ij)
must be determined. Also, neither the linear high-pass filter nor the activity
measure differentiate between dither patterns and directional edges. It is hard
to extend this method to handle different activity patterns in different ways,
since both the high-pass filter and the activity measure must be redesigned.
Hence, what is needed is a simple manner in which to design efficient selective
image sharpening or selective image sharpening and selective image smoothing filters.
SUMMARY OF THE INVENTION
A method of designing an image processing filter in which a pre-existing selective
smoothing filter is used to derive a matching non-selective smoothing filter by
disabling the selectivity mechanism of the selective smoothing filter and then
the difference of the pre-existing and derived filters is substituted into the
high-pass filter operation of an unsharp masking filter operation to form the image
processing filter.
BRIEF DESCRIPTION OF THE DRAWINGS
The objects, features, and advantages of the present invention will be apparent
to one skilled in the art, in view of the following detailed description in which:
FIG. 1 illustrates an embodiment of a generalized method of designing a filter
for selective sharpening of image data;
FIG. 2 illustrates an embodiment of a generalized method of designing a filter
for selective sharpening and selective smoothing of image data;
FIG. 3A illustrates a method of applying the filter as designed in FIGS. 1 and 2;
FIG. 3B illustrates an exemplary 3×3 neighborhood.
DETAILED DESCRIPTION OF THE INVENTION
A generalized method of designing selective filters given a selective smoothing
filter {circumflex over (l)} is described and a method therein of applying the
selective filter to image data.
FIG. 1 shows a first embodiment of a method of designing a filter which performs
selective sharpening of image data. The selective sharpening filter is designed
from a pre-existing selective smoothing filter {circumflex over (l)}. In general,
a selective smoothing filter is defined as a filter that operates on selected "non-feature"
pixel values in an image and replaces them with a weighted average of its neighbors.
This attenuates high frequency components, namely abrupt changes, in pixel intensity.
The selectiveness of the selective smoothing filter {circumflex over (l)} is based
on a classification of neighborhoods which range from "feature" type neighborhoods
(also referred to as φ-type neighborhoods) which generally correspond to
edges and "non-feature" type neighborhoods (also referred to as α-type neighborhoods).
The selective smoothing filter {circumflex over (l)} fully attenuates "non-feature"
pixels having α-type neighborhoods, does not attenuate "feature" pixels having
φ-type neighborhoods, and attenuates to some degree pixels that are classified
between "feature" or "non-feature". In general, selective smoothing filters include
a selectivity mechanism which is dependent on the classification of the "feature"
and "non-feature" types. The selectivity mechanism can be based on differences
in photometric distance between pixels (i.e., intensity differences) and/or geometric
distance between pixels or on other criteria used for classifying image neighborhoods.
Given the selective smoothing filter {circumflex over (l)} (block
102),
it is assumed that it is always possible to derive a corresponding/matching smoothing
filter {circumflex over (L)} (block
104) that is non-selective by disabling
the selectivity mechanism of {circumflex over (l)}. The non-selective smoothing
filter {circumflex over (L)} is derived in such a manner such that in "non-feature"
neighborhoods (type α), it has exactly the same effect as {circumflex over
(l)}. On the other hand, in "feature" neighborhoods (type φ) non-selective
filter {circumflex over (L)} has a smoothing effect while selective filter {circumflex
over (l)} does not.
One example, of how to derive a matching non-selective smoothing filter from
a pre-existing selective smoothing filter by disabling the selectivity mechanism
of the selective filter is described with respect to a robust anisotropic diffusion
(RAD) filter. A given RAD filter is applied to a 3×3 neighborhood about a
pixel of interest P
0 to perform an operation as defined by Eq. 2 on
each pixel in the image:
##EQU2##
where ψ is the influence-function of the robust error-norm, T is the
characteristic scale of ψ, P
0 and P
j are pixel values
in a 3×3 neighborhood as shown in FIG. 3B, and C
j is the spatial
weight which can be interpreted either as spatial filter coefficients, or as stochastic
probabilities of random-walk transitions from pixel P
j to pixel P
0
in the 3×3 neighborhood. In this example, Δt=1. The influence
function ψ corresponds to a photometric weighting function which determines
the selectivity of the RAD filter function. In this example the influence function
is defined by the following conditions:
##EQU3##
wherein ΔP is P
j-P
0. The influence function and
hence, the selectivity of the filter, is dependent on whether the photometric difference
(ΔP) is greater than, less than, or equal to parameter T. Consequently, to
disable the selectivity of the RAD filter, the parameter T can be set to ∞
so that the influence function is the same no matter what the photometric difference is.
Next, a difference operation of the selective smoothing filter {circumflex
over (l)} and the derived matching non-selective smoothing filter {circumflex over
(L)} is substituted for the high-pass filter operation performed on the input image
data in an unsharp masking filter (block
106). The resulting filter is a
new selective sharpening filter designed from an unsharp masking filter in which
the high-pass filter operation is replaced with the difference operation of the
selective smoothing filter and the derived matching non-selective smoothing filter.
It should be noted that in one embodiment, the method is performed in a single
iteration of processing the image data.
The following describes the substitution of the high-pass filter operation with
the difference operation ({circumflex over (l)}-{circumflex over (L)}) in an unsharp
masking filter. Analyzing the difference operation, it can be shown that the operation
has a different effect on each of the neighborhood types, φ and α.
Specifically, in α-type neighborhoods, the filtering effects of {circumflex
over (L)} and {circumflex over (l)} are exactly the same. As a result, the difference
operation is zero. In φ-type neighborhoods, {circumflex over (l)} has no
effect and is equivalent to the identity operator {circumflex over (l)}, while
{circumflex over (L)} has a strong smoothing effect (the smoothing effect is naturally
larger on "feature" pixels). Thus, in φ-neighborhoods ({circumflex over (l)}-{circumflex
over (L)})˜({circumflex over (l)}-{circumflex over (L)}). In other words,
({circumflex over (l)}-{circumflex over (L)}) is a selective feature-enhancement
filter, generating a zero output signal at "non-feature" pixels (α), and
a strong output signal at "feature" pixels (φ). Generally, since {circumflex
over (L)} is a feature-attenuation filter, then {circumflex over (l)}-{circumflex
over (L)} is a feature-enhancement filter. In particular, when {circumflex over
(L)} is a linear low-pass filter, {circumflex over (l)}-{circumflex over (L)} is
a linear high-pass filter. Consequently, since ({circumflex over (l)}-{circumflex
over (L)})˜({circumflex over (l)}-{circumflex over (L)}), and since {circumflex
over (l)}-{circumflex over (L)} is a linear high-pass filter, ({circumflex over
(l)}-{circumflex over (L)}) can be substituted for the high-pass filter operation
in the unsharp masking filter. Scaling the output of ({circumflex over (l)}-{circumflex
over (L)}) by a real positive factor λ and adding the result to the original
image data x yields a selective unsharp masking filter having a function defined
by Eq. 3:
##EQU4##
In a particular case where {circumflex over (L)} is linear, and the "feature"
pixels correspond to edges, the unsharp masking filter as defined by Eq. 3 is equivalent
to a linear unsharp masking filter (Eq. 1) at edges, and equivalent to the identity
filter in "non-edge" pixels (flat regions). In other words, noise is not enhanced
in flat regions.
The advantage of this method of filter design is that pre-existing selective
smoothing filters that have been developed so as to have particular filtering characteristics
can be used to design new selective sharpening filters having the same filtering
characteristics. For example, if a pre-existing selective smoothing filter is characterized
as a filter that selectively smoothes the image data while preserving edges with
well defined directionality, then the selective sharpening filter which is designed
according to the method shown in FIG. 1 will sharpen only edges with well defined directionality.
FIG. 2 shows a second embodiment of the method of designing a filter that both
selectively smoothes "non-feature" pixels and selectively sharpens "feature" pixels
in a single pass over the image. According to this method, a selective smoothing
filter {circumflex over (l)} is selected (block
102), a matching non-selective
smoothing filter {circumflex over (L)} is derived (block
104), and the difference
operation ({circumflex over (l)}-{circumflex over (L)}) is substituted for the
high-pass filter operation in an unsharp masking filter as in the method shown
in FIG. 1. Next, the selectively smoothed image pixel values {circumflex over (l)}x
is substituted for the image pixel values x in the unsharp masking filter such
that the smoothed image pixel values {circumflex over (l)}x are added to the scaled
difference operation of the image data (i.e., λ·({circumflex over (l)}-{circumflex
over (L)})x) instead of the input image pixel values x. This substitution within
the unsharp masking filter is illustrated by Eq. 4 as follows:
##EQU5##
The net effect of this substitution is that the portion of the unsharp masking
filter that implements the λ({circumflex over (l)}-{circumflex over (L)})x
operation performs selective sharpening of the image data while the portion of
the unsharp masking filter that implements the {circumflex over (l)}x operation
performs selective smoothing of the image data. Consequently, the method as shown
in FIG. 2 illustrates that a selective sharpening and selective smoothing filter
can be designed given a pre-existing selective smoothing filter {circumflex over
(l)}. One advantage of the new selective smoothing and sharpening filter obtained
according to this design method is that the smoothing filter portion operates on
non-feature neighborhoods without affecting the feature neighborhoods and the sharpening
filter portion performs a complimentary function of enhancing feature neighborhoods
without affecting non-feature neighborhoods. As a result, smoothing and sharpening
operations do not interfere with each other or undo each others filtering affects
as is seen in prior art filtering techniques.
FIG. 3A shows a method of applying a filter designed according to the methods
shown in FIGS. 1 and 2 of the present invention by implementing {circumflex over
(l)} and {circumflex over (L)} separately and combining their outputs in a difference
operation. Initially, a pixel value x(i,j) corresponding to a pixel of interest
which is to be enhanced is selected (block
112) from the image data (block
110). Next, a neighborhood W of pixels is selected (block
114). An
exemplary 3×3 neighborhood is shown in FIG. 3B which includes the pixel of
interest P
0 and its neighboring pixels P
1-P
8.
Each of the smoothing filter {circumflex over (l)} and the non-selectively smoothing
filter {circumflex over (L)} is applied (blocks
116,
118) to the
pixel value x and its corresponding selected neighborhood to generate image data
{circumflex over (l)}x and {circumflex over (L)}x (where {circumflex over (l)}x
corresponds to the selectively smoothed pixel value generated by filtering pixel
value x with smoothing filter {circumflex over (l)} and {circumflex over (L)}x
corresponds to the smoothed pixel value generated by filtering pixel value x with
derived matching non-selective smoothing filter {circumflex over (L)}).
The difference of these two pixel values is determined (
120) to obtain
the difference operation ({circumflex over (l)}-{circumflex over (L)})W for pixel
value x and its corresponding window W. The difference operation is multiplied
(
122) by the sharpening strength factor λ to obtain the value λ({circumflex
over (l)}-{circumflex over (L)}) which is added (
126) to either the original
pixel value x or to the selectively smoothed pixel value {circumflex over (l)}x
dependent on whether the new filter is designed according to the method of FIG.
1 or FIG. 2.
Specifically, in the case in which the filter is designed according
to FIG. 1 switch
124 is set such that the original pixel value x is added
to λ({circumflex over (l)}-{circumflex over (L)}) to obtain the enhanced
output value y (
128). Alternatively, in the case in which the filter is
designed according to FIG. 2, switch
124 is set such that the selectively
smoothed pixel value {circumflex over (l)}x is added to λ({circumflex over
(l)}-{circumflex over (L)}) to obtain the enhanced value y (
128).
It should be noted that although a 3×3 square-shaped neighborhood is used
when performing both selective and non-selective image smoothing, the system and
method of image processing according to the present invention is not limited to
such a neighborhood. The neighborhood is not limited to any particular size. The
number of pixels is not limited to nine. Although a fixed number of pixels in the
neighborhood is preferred for all pixels of interest, the size of the neighborhood
may be changed dynamically to accommodate a particular class of image region (e.g.,
text, graphics, natural features).
The neighborhood is not limited to any particular geometry. For example, the
shape of the neighborhood may be diamond shaped. In addition, the neighborhood
for each of the selective smoothing filtering operation and the selective sharpening
operation need not be the same size.
As can be seen, the implementation requires (besides the computation of {circumflex
over (l)}x and {circumflex over (L)}x) just one multiplication and two subtraction/addition
operations per pixel. It also has a modularity advantage, so that the implementation
of either of the filters {circumflex over (l)}, {circumflex over (L)} could be
changed without affecting the final result.
According to another method of applying a filter designed using the methods
shown in FIGS. 1 and 2 the two filters {circumflex over (L)} and {circumflex over
(l)} can be applied to each pixel value with a single in-line process in which
common computations in the implementations of {circumflex over (L)} and {circumflex
over (l)} are performed by or combined into a single computation to reduce filter
complexity and processing time. In addition, by incorporating both filter functions
into a single in-line process, a single iteration of each pixel value is required
to obtain both {circumflex over (l)}x and {circumflex over (L)}x instead of two
separate iterations for the two filters.
In the embodiments of designing a selective sharpening filter as shown in FIG.
1 or a selective smoothing and selective sharpening filter as shown in FIG. 2,
a non-selective smoothing filter {circumflex over (L)} is derived from a pre-existing
selective smoothing filter {circumflex over (l)} by disabling the selectivity mechanism
of filter {circumflex over (l)}. In another embodiment of each of the methods shown
in FIGS. 1 and 2, instead of disabling the selectivity mechanism, it is set to
a "weaker" selectivity state wherein a "weaker" selectivity state is defined as
a state in which the selectivity mechanism of the filter causes the filter to function
more like a non-selective smoothing filter than a selective smoothing filter. In
other words, by decreasing the selectivity state of a selective smoothing filter,
less "feature" neighborhoods are preserved and more "non-feature" neighborhoods
are processed by the filter. An example of how to weaken the selectivity state
of a selective smoothing filter can be shown with respect to the RAD filter. By
setting the parameter T to a higher value (but not ∞) the selectivity mechanism
of the RAD filter is set into a weaker selectivity state.
By deriving filter {circumflex over (L)} in this manner the filter difference
({circumflex over (l)}-{circumflex over (L)}) essentially becomes a selective bandpass
filter instead of a selective high-pass filter. This bandpass filter can then be
substituted into the USM filter as described for the methods shown in FIGS. 1 and
2. The resulting filter designed in this manner provides more control over the
types of neighborhoods where image sharpening is to occur.
Although in both Eqs. 3 and 4 the filter-difference ({circumflex over (l)}-{circumflex
over (L)}) is mapped linearly by λ, it should be understood that the generalized
unsharp masking method is not limited to this particular mapping. For example,
λ can have a local dependence λ=λ(i,j), according to some criterion
which is not incorporated through the difference ({circumflex over (l)}-{circumflex
over (L)}). Another manner in which to generalized Eqs. 3 and 4 is by applying
a general non-decreasing mapping f(t) to the result of the filter difference ({circumflex
over (l)}-{circumflex over (L)})x (subject to the condition f(0)=0). Namely, the
filters as described in Eqs. 3 and 4 can be replaced by filters as described by:
##EQU6##
respectively. A simple example mapping f(t) is a saturated linear mapping,
which ensures that the absolute sharpening signal does not pass a pre-determined
threshold λT:
##EQU7##
In the preceding description, numerous specific details are set forth, such as
specific filters and filter implementations in order to provide a thorough understanding
of the present invention. It will be apparent, however, to one skilled in the art
that these specific details need not be employed to practice the present invention.
In other instances, well-known digital image processing steps have not been described
in detail in order to avoid unnecessarily obscuring the present invention.
In addition, although elements of the present invention have been described in
conjunction with certain embodiments, it is appreciated that the invention can
be implemented in a variety of other ways. Consequently, it is to be understood
that the particular embodiments shown and described by way of illustration is in
no way intended to be considered limiting. Reference to the details of these embodiments
is not intended to limit the scope of the claims which themselves recited only
those features regarded as essential to the invention.
*
