digital image processing, 2nd ed. © 2002 r. c. gonzalez & r. e. woods chapter 3 image...
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![Page 1: Digital Image Processing, 2nd ed. © 2002 R. C. Gonzalez & R. E. Woods Chapter 3 Image Enhancement in the Spatial Domain Chapter](https://reader037.vdocuments.site/reader037/viewer/2022110205/56649c925503460f9494dcc2/html5/thumbnails/1.jpg)
Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com
© 2002 R. C. Gonzalez & R. E. Woods
Chapter 3Image Enhancementin the Spatial Domain
Chapter 3Image Enhancementin the Spatial Domain
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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com
© 2002 R. C. Gonzalez & R. E. Woods
Image Enhancement in theSpatial Domain
Image Enhancement in theSpatial Domain
• The spatial domain: – The image plane– For a digital image is a Cartesian coordinate system of discrete rows
and columns. At the intersection of each row and column is a pixel. Each pixel has a value, which we will call intensity.
• The frequency domain :– A (2-dimensional) discrete Fourier transform of the spatial domain– We will discuss it in chapter 4.
• Enhancement :– To “improve” the usefulness of an image by using some transformation
on the image. – Often the improvement is to help make the image “better” looking,
such as increasing the intensity or contrast.
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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com
© 2002 R. C. Gonzalez & R. E. Woods
BackgroundBackground
• A mathematical representation of spatial domain enhancement:
where f(x, y): the input image
g(x, y): the processed image
T: an operator on f, defined over some neighborhood of (x, y)
)],([),( yxfTyxg
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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com
© 2002 R. C. Gonzalez & R. E. Woods
Gray-level TransformationGray-level Transformation
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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com
© 2002 R. C. Gonzalez & R. E. Woods
Some Basic Gray Level TransformationsSome Basic Gray Level Transformations
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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com
© 2002 R. C. Gonzalez & R. E. Woods
Image NegativesImage Negatives
rLs 1
• Let the range of gray level be [0, L-1], then
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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com
© 2002 R. C. Gonzalez & R. E. Woods
Log TransformationsLog Transformations
)1log( rcs where c : constant
0r
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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com
© 2002 R. C. Gonzalez & R. E. Woods
Power-Law TransformationPower-Law Transformation
crs
where c, : positive constants
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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com
© 2002 R. C. Gonzalez & R. E. Woods
Power-Law TransformationExample 1: Gamma Correction
Power-Law TransformationExample 1: Gamma Correction
4.0
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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com
© 2002 R. C. Gonzalez & R. E. Woods
Power-Law TransformationExample 2: Gamma CorrectionPower-Law TransformationExample 2: Gamma Correction
4.0 3.0
6.01
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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com
© 2002 R. C. Gonzalez & R. E. Woods
Power-Law TransformationExample 3: Gamma CorrectionPower-Law TransformationExample 3: Gamma Correction
0.3
0.50.4
1
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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com
© 2002 R. C. Gonzalez & R. E. Woods
Piecewise-Linear Transformation FunctionsCase 1: Contrast Stretching
Piecewise-Linear Transformation FunctionsCase 1: Contrast Stretching
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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com
© 2002 R. C. Gonzalez & R. E. Woods
Piecewise-Linear Transformation FunctionsCase 2:Gray-level Slicing
Piecewise-Linear Transformation FunctionsCase 2:Gray-level Slicing
An image Result of using the transformation in (a)
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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com
© 2002 R. C. Gonzalez & R. E. Woods
Piecewise-Linear Transformation FunctionsCase 3:Bit-plane Slicing
Piecewise-Linear Transformation FunctionsCase 3:Bit-plane Slicing
• Bit-plane slicing: – It can highlight the contribution made to total image appearance by
specific bits.
– Each pixel in an image represented by 8 bits.
– Image is composed of eight 1-bit planes, ranging from bit-plane 0 for the least significant bit to bit plane 7 for the most significant bit.
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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com
© 2002 R. C. Gonzalez & R. E. Woods
Piecewise-Linear Transformation FunctionsBit-plane Slicing: A Fractal Image
Piecewise-Linear Transformation FunctionsBit-plane Slicing: A Fractal Image
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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com
© 2002 R. C. Gonzalez & R. E. Woods
Piecewise-Linear Transformation FunctionsBit-plane Slicing: A Fractal Image
Piecewise-Linear Transformation FunctionsBit-plane Slicing: A Fractal Image
2
7 6
45 3
1 0
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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com
© 2002 R. C. Gonzalez & R. E. Woods
Histogram ProcessingHistogram Processing
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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com
© 2002 R. C. Gonzalez & R. E. Woods
Histogram ProcessingHistogram Processing
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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com
© 2002 R. C. Gonzalez & R. E. Woods
Histogram EqualizationHistogram Equalization
• Histogram equalization:– To improve the contrast of an image
– To transform an image in such a way that the transformed image has a nearly uniform distribution of pixel values
• Transformation:– Assume r has been normalized to the interval [0,1], with r = 0
representing black and r = 1 representing white
– The transformation function satisfies the following conditions:• T(r) is single-valued and monotonically increasing in the interval
•
10 r
10for 1)(0 rrT
10 )( rrTs
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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com
© 2002 R. C. Gonzalez & R. E. Woods
Histogram EqualizationHistogram Equalization
• For example:
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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com
© 2002 R. C. Gonzalez & R. E. Woods
Histogram EqualizationHistogram Equalization
• Histogram equalization is based on a transformation of the probability density function of a random variable.
• Let pr(r) and ps(s) denote the probability density function of random variable r and s, respectively.
• If pr(r) and T(r) are known, then the probability density function ps(s) of the transformed variable s can be obtained
• Define a transformation function where w is a dummy variable of integration and the right side of this equation is the cumulative distribution
function of random variable r.
r
r dwwprTs0
)()(ds
drrpsp rs )()(
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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com
© 2002 R. C. Gonzalez & R. E. Woods
Histogram EqualizationHistogram Equalization
• Given transformation function T(r),
ps(s) now is a uniform probability density function.
• T(r) depends on pr(r), but the resulting ps(s) always is uniform.
10 1)(
1)()()( s
rprp
ds
drrpsp
rrrs
)()()(
0rpdwwp
dr
d
dr
rdT
ds
drr
r
r
r
r dwwprT0
)()(
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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com
© 2002 R. C. Gonzalez & R. E. Woods
Histogram EqualizationHistogram Equalization
• In discrete version:– The probability of occurrence of gray level rk in an image is
n : the total number of pixels in the image
nk : the number of pixels that have gray level rk
L : the total number of possible gray levels in the image– The transformation function is
– Thus, an output image is obtained by mapping each pixel with level rk in the input image into a corresponding pixel with level sk.
1,...,2,1,0 )()(0 0
Lkn
nrprTs
k
j
k
j
jjrkk
1,...,2,1,0 )( Lkn
nrp k
r
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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com
© 2002 R. C. Gonzalez & R. E. Woods
Histogram EqualizationHistogram Equalization
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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com
© 2002 R. C. Gonzalez & R. E. Woods
Histogram EqualizationHistogram Equalization
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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com
© 2002 R. C. Gonzalez & R. E. Woods
Histogram EqualizationHistogram Equalization
• Transformation functions (1) through (4) were obtained form the histograms of the images in Fig 3.17(1), using Eq. (3.3-8).
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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com
© 2002 R. C. Gonzalez & R. E. Woods
Histogram MatchingHistogram Matching
• Histogram matching is similar to histogram equalization, except that instead of trying to make the output image have a flat histogram, we would like it to have a histogram of a specified shape, say pz(z).
• We skip the details of implementation.
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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com
© 2002 R. C. Gonzalez & R. E. Woods
Local EnhancementLocal Enhancement
• The histogram processing methods discussed above are global, in the sense that pixels are modified by a transformation function based on the gray-level content of an entire image.
• However, there are cases in which it is necessary to enhance details over small areas in an image.
original global local
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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com
© 2002 R. C. Gonzalez & R. E. Woods
Use of Histogram Statistics for Image EnhancementUse of Histogram Statistics for Image Enhancement
• Moments can be determined directly from a histogram much faster than they can from the pixels directly.
• Let r denote a discrete random variable representing discrete gray-levels in the range [0,L-1], and p(ri) denote the normalized histogram component corresponding to the ith value of r, then the nth moment of r about its mean is defined as
where m is the mean value of r
• For example, the second moment (also the variance of r) is
1
0
)(L
iii rprm
1
0
22 )()()(
L
iii rpmrr
1
0
)()()(L
ii
nin rpmrr
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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com
© 2002 R. C. Gonzalez & R. E. Woods
Use of Histogram Statistics for Image EnhancementUse of Histogram Statistics for Image Enhancement
• Two uses of the mean and variance for enhancement purposes:– The global mean and variance (global means for the entire
image) are useful for adjusting overall contrast and intensity.
– The mean and standard deviation for a local region are useful for correcting for large-scale changes in intensity and contrast. ( See equations 3.3-21 and 3.3-22.)
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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com
© 2002 R. C. Gonzalez & R. E. Woods
Use of Histogram Statistics for Image EnhancementExample: Enhancement based on local statistics
Use of Histogram Statistics for Image EnhancementExample: Enhancement based on local statistics
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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com
© 2002 R. C. Gonzalez & R. E. Woods
Use of Histogram Statistics for Image EnhancementExample: Enhancement based on local statistics
Use of Histogram Statistics for Image EnhancementExample: Enhancement based on local statistics
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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com
© 2002 R. C. Gonzalez & R. E. Woods
Use of Histogram Statistics for Image EnhancementExample: Enhancement based on local statistics
Use of Histogram Statistics for Image EnhancementExample: Enhancement based on local statistics
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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com
© 2002 R. C. Gonzalez & R. E. Woods
Enhancement Using Arithmetic/Logic OperationsEnhancement Using Arithmetic/Logic Operations
• Two images of the same size can be combined using operations of addition, subtraction, multiplication, division, logical AND, OR, XOR and NOT. Such operations are done on pairs of their corresponding pixels.
• Often only one of the images is a real picture while the other is a machine generated mask. The mask often is a binary image
consisting only of pixel values 0 and 1. • Example: Figure 3.27
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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com
© 2002 R. C. Gonzalez & R. E. Woods
Enhancement Using Arithmetic/Logic OperationsEnhancement Using Arithmetic/Logic Operations
AND
OR
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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com
© 2002 R. C. Gonzalez & R. E. Woods
Image SubtractionExample 1
Image SubtractionExample 1
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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com
© 2002 R. C. Gonzalez & R. E. Woods
Image SubtractionExample 2
Image SubtractionExample 2
• When subtracting two images, negative pixel values can result. So, if you want to display the result it may be necessary to readjust the dynamic range by scaling.
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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com
© 2002 R. C. Gonzalez & R. E. Woods
Image AveragingImage Averaging
• When taking pictures in reduced lighting (i.e., low illumination), image noise becomes apparent.
• A noisy image g(x,y) can be defined by
where f (x, y): an original image : the addition of noise• One simple way to reduce this granular noise is to take
several identical pictures and average them, thus smoothing out the randomness.
),(),(),( yxyxfyxg
),( yx
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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com
© 2002 R. C. Gonzalez & R. E. Woods
Noise Reduction by Image AveragingExample: Adding Gaussian Noise
Noise Reduction by Image AveragingExample: Adding Gaussian Noise
• Figure 3.30 (a): An image of Galaxy Pair NGC3314.
• Figure 3.30 (b): Image corrupted by additive Gaussian noise with zero mean and a standard deviation of 64 gray levels.
• Figure 3.30 (c)-(f): Results of averaging K=8,16,64, and 128 noisy images.
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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com
© 2002 R. C. Gonzalez & R. E. Woods
Noise Reduction by Image AveragingExample: Adding Gaussian NoiseNoise Reduction by Image AveragingExample: Adding Gaussian Noise
• Figure 3.31 (a):
From top to bottom: Difference images between Fig. 3.30 (a) and the four images in Figs. 3.30 (c) through (f), respectively.
• Figure 3.31 (b): Corresponding histogram.
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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com
© 2002 R. C. Gonzalez & R. E. Woods
Basics of Spatial FilteringBasics of Spatial Filtering
• In spatial filtering (vs. frequency domain filtering), the output image is computed directly by simple calculations on the pixels of the input image.
• Spatial filtering can be either linear or non-linear.
• For each output pixel, some neighborhood of input pixels is used in the computation.
• In general, linear filtering of an image f of size MXN with a filter mask of size mxn is given by
where a=(m-1)/2 and b=(n-1)/2
• This concept called convolution. Filter masks are sometimes called convolution masks or convolution kernels.
a
as
b
bt
tysxftswyxg ),(),(),(
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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com
© 2002 R. C. Gonzalez & R. E. Woods
Basics of Spatial FilteringBasics of Spatial Filtering
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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com
© 2002 R. C. Gonzalez & R. E. Woods
• Nonlinear spatial filtering usually uses a neighborhood too, but some other mathematical operations are use. These can include conditional operations (if …, then…), statistical (sorting pixel values in the neighborhood), etc.
• Because the neighborhood includes pixels on all sides of the center pixel, some special procedure must be used along the top, bottom, left and right sides of the image so that the processing does not try to use pixels that do not exist.
Basics of Spatial FilteringBasics of Spatial Filtering
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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com
© 2002 R. C. Gonzalez & R. E. Woods
Smoothing Spatial FiltersSmoothing Spatial Filters
• Smoothing linear filters– Averaging filters (Lowpass filters in Chapter 4))
• Box filter
• Weighted average filter
Box filter Weighted average
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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com
© 2002 R. C. Gonzalez & R. E. Woods
Smoothing Spatial FiltersSmoothing Spatial Filters
• The general implementation for filtering an MXN image with a weighted averaging filter of size mxn is given by
where a=(m-1)/2 and b=(n-1)/2
a
as
b
bt
a
as
b
bt
tsw
tysxftswyxg
),(
),(),(),(
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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com
© 2002 R. C. Gonzalez & R. E. Woods
Smoothing Spatial FiltersImage smoothing with masks of various sizes
Smoothing Spatial FiltersImage smoothing with masks of various sizes
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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com
© 2002 R. C. Gonzalez & R. E. Woods
Smoothing Spatial FiltersAnother Example
Smoothing Spatial FiltersAnother Example
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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com
© 2002 R. C. Gonzalez & R. E. Woods
Order-Statistic FiltersOrder-Statistic Filters
• Order-statistic filters– Median filter: to reduce impulse noise (salt-and-
pepper noise)
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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com
© 2002 R. C. Gonzalez & R. E. Woods
Sharpening Spatial FiltersSharpening Spatial Filters
• Sharpening filters are based on computing spatial derivatives of an image.
• The first-order derivative of a one-dimensional function f(x) is
• The second-order derivative of a one-dimensional function f(x) is
)()1( xfxfx
f
)(2)1()1(2
2
xfxfxfx
f
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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com
© 2002 R. C. Gonzalez & R. E. Woods
Sharpening Spatial FiltersAn Example
Sharpening Spatial FiltersAn Example
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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com
© 2002 R. C. Gonzalez & R. E. Woods
Use of Second Derivatives for EnhancementThe Laplacian
Use of Second Derivatives for EnhancementThe Laplacian
• Development of the Laplacian method– The two dimensional Laplacian operator for continuous
functions:
– The Laplacian is a linear operator.
2
2
2
22
y
f
x
ff
),(2),1(),1(2
2
yxfyxfyxfx
f
),(2)1,()1,(2
2
yxfyxfyxfy
f
)(4)]1,()1,(),1(),1([2 xfyxfyxfyxfyxff
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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com
© 2002 R. C. Gonzalez & R. E. Woods
Use of Second Derivatives for EnhancementThe Laplacian
Use of Second Derivatives for EnhancementThe Laplacian
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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com
© 2002 R. C. Gonzalez & R. E. Woods
Use of Second Derivatives for EnhancementThe Laplacian
Use of Second Derivatives for EnhancementThe Laplacian
• To sharpen an image, the Laplacian of the image is subtracted from the original image.
• Example: Figure 3.40
positive. ismask Laplacian theoft coefficiencenter theif ),(
negative. ismask Laplacian theoft coefficiencenter theif ),(),(
2
2
fyxf
fyxfyxg
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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com
© 2002 R. C. Gonzalez & R. E. Woods
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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com
© 2002 R. C. Gonzalez & R. E. Woods
Use of Second Derivatives for EnhancementThe Laplacian: Simplifications
Use of Second Derivatives for EnhancementThe Laplacian: Simplifications
The g(x,y) maskNot only f2
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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com
© 2002 R. C. Gonzalez & R. E. Woods
Use of First Derivatives for EnhancementThe Gradient
Use of First Derivatives for EnhancementThe Gradient
• Development of the Gradient method– The gradient of function f at coordinates (x,y) is defined as
the two-dimensional column vector:
– The magnitude of this vector is given by
y
fx
f
G
G
y
xf
2
122
2
122)(mag
y
f
x
fGGf yxf
yx GGf
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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com
© 2002 R. C. Gonzalez & R. E. Woods
Use of First Derivatives for EnhancementThe Gradient
Use of First Derivatives for EnhancementThe Gradient
Roberts cross-gradientoperators
Sobeloperators
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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com
© 2002 R. C. Gonzalez & R. E. Woods
Use of First Derivatives for EnhancementThe Gradient: Using Sobel Operators
Use of First Derivatives for EnhancementThe Gradient: Using Sobel Operators
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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com
© 2002 R. C. Gonzalez & R. E. Woods
Combining Spatial Enhancement Methods
Combining Spatial Enhancement Methods
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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com
© 2002 R. C. Gonzalez & R. E. Woods
Combining Spatial Enhancement Methods
Combining Spatial Enhancement Methods
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Digital Image Processing, 2nd ed.Digital Image Processing, 2nd ed.www.imageprocessingbook.com
© 2002 R. C. Gonzalez & R. E. Woods