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Digital Image Processing: Image Enhancement - Duong Anh Duc

Digital Image Processing
Image Enhancement

21/11/15

Duong Anh Duc - Digital Image Processing

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Image Enhancement
 To process an image so that output is “visually better”
than the input, for a specific application.
 Enhancement is therefore, very much dependent on
the particular problem/image at hand.
 Enhancement can be done in either:
– Spatial domain: operate on the original image

g(m,n) = T[f(m,n)]
– Frequency domain: operate on the DFT of the original image


G(u,v) = T[F(u,v)],

where

F(u,v) = F[f(m,n)], and G(u,v) = F [g(m,n)],

21/11/15

Duong Anh Duc - Digital Image Processing

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Image Enhancement Techniques

Point Operations









Image Negative
Contrast
Stretching
Compression of
dynamic range
Graylevel slicing
Image
Subtraction
Image Averaging
Histogram
operations

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Mask Operations









Smoothing
operations
Median Filtering
Sharpening
operations
Derivative
operations
Histogram
operations

Transform Operations

Coloring Operations











Low pass
Filtering
Hi pass Filtering
Band pass
Filtering
Homomorphic
Filtering
Histogram
operations

Duong Anh Duc - Digital Image Processing

False Coloring
Full color
Processing

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Point Operations
 Output pixel value g(m, n) at pixel (m, n) depends only on the input
pixel value at f(m, n) at (m, n) (and not on the neighboring pixel
values).
 We normally write s = T(r), where s is the output pixel value and r is the
input pixel value.

 T is any increasing function that maps [0,1] into [0,1].
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Duong Anh Duc - Digital Image Processing

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Image Negative

T(r) = s = L-1-r, L: max grayvalue
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Duong Anh Duc - Digital Image Processing

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Negative Image

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Duong Anh Duc - Digital Image Processing

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Contrast Stretching



Increase the dynamic range of grayvalues in the input image.
Suppose you are interested in stretching the input intensity values in the
interval [r1, r2]:



Note that (r1- r2) < (s1- s2). The grayvalues in the range [r1, r2] is stretched into
the range [s1, s2].

21/11/15

Duong Anh Duc - Digital Image Processing

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Contrast Stretching
 Special cases:
– Thresholding or
binarization
r1 = r2 , s1 = 0 and s2 = 1
– Useful when we are
only interested in the
shape of the objects
and on on their actual
grayvalues.
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Duong Anh Duc - Digital Image Processing

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Contrast Stretching

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Duong Anh Duc - Digital Image Processing

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Contrast Stretching
 Special cases (cont.):
– Gamma correction:
S1 = 0, S2 = 1 and
0, r
g

T r

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r1

r r1
, r1 r
r2 r1
1, r r2

r2

Duong Anh Duc - Digital Image Processing

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Contrast Stretching
Gamma correction

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Duong Anh Duc - Digital Image Processing

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Compression of Dynamic
Range
 When the dynamic range of the input
grayvalues is large compared to that of
the display, we need to “compress” the
grayvalue range --- example: Fourier
transform magnitude.
 Typically we use a log scale.
s = T(r) = c log(1+ r )

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Duong Anh Duc - Digital Image Processing

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Compression of Dynamic
Range

Saturn Image

Mag. Spectrum

Mag. Spectrum
in log scale

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Duong Anh Duc - Digital Image Processing

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Compression of Dynamic
Range
 Graylevel Slicing: Highlight a specific
range of grayvalues.

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Duong Anh Duc - Digital Image Processing

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Compression of Dynamic
Range
 Example:

Highlighted Image (no background)

Original Image

Highlighted Image (with background)
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Duong Anh Duc - Digital Image Processing

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Compression of Dynamic
Range
 Bitplane Slicing: Display the different
bits as individual binary images.

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Duong Anh Duc - Digital Image Processing

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Compression of Dynamic
Range

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Duong Anh Duc - Digital Image Processing

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Image Subtraction
 In this case, the difference between two
“similar” images is computed to highlight
or enhance the differences between
them:
g(m,n) = f1(m,n)-f2(m,n)
 It has applications in image segmentation
and enhancement

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Duong Anh Duc - Digital Image Processing

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Example: Mask mode radiography

f1(m, n): Image before dye injection
f2(m, n): Image after dye injection
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g(m, n): Image after dye injection,
followed by subtraction

Duong Anh Duc - Digital Image Processing

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Image Averaging for noise
reduction
 Noise is any random (unpredictable)
phenomenon that contaminates an image.
 Noise is inherent in most practical systems:
– Image acquisition
– Image transmission
– Image recording

 Noise is typically modeled as an additive process:
g(m,n) = f(m,n) + (m,n)
Noisy
Image

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Noise-free
Image

Duong Anh Duc - Digital Image Processing

Noise

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Image Averaging for noise
reduction
 The noise h (m, n) at each pixel (m, n) is modeled
as a random variable.
 Usually, h (m, n) has zero-mean and the noise values
at different pixels are uncorrelated.
 Suppose we have M observations {gi(m, n)}, i=1, 2, …,
M, we can (partially) mitigate the effect of noise by
“averaging”

g m, n

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1
M

M

g i m, n

i 1

Duong Anh Duc - Digital Image Processing

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Image Averaging for noise
reduction
 In this case, we can show that:
E g m, n
Var g m, n

f m, n
1
Var
M

m, n

 Therefore, as the number of observations
increases (M
), the effect of noise
tends to zero.
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Duong Anh Duc - Digital Image Processing

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Image Averaging Example

Noise-free Image

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Noisy Image
Noise Variance = 0.05

Duong Anh Duc - Digital Image Processing

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Image Averaging Example

M =2

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M =5

Duong Anh Duc - Digital Image Processing

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Image Averaging Example

M =10

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Duong Anh Duc - Digital Image Processing

M =25

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