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)],

Image Enhancement Techniques

Point Operations

Mask Operations

Transform Operations

Coloring Operations

• Image Negative

• Contrast Stretching

• Compression of dynamic range

• Graylevel slicing

• Image Subtraction

• Image Averaging

• Histogram operations

• Smoothing operations

• Median Filtering

• Sharpening operations

• Derivative operations

• Histogram operations

• Low pass filtering

• High pass Filtering

• Band pass filtering

• Homomorphic filtering

• Histogram operations

• False coloring

• Full color processing

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]. M atlab Aside:

• This is roughly what matlab does when you convert a ui nt 8 image to doubl e a = i mr ead( ’ i mage. t i f ’ ) ; b = i m2doubl e( a) ;

00 1

1

r

s

Input grayvalue (normalized)

Outut grayvalue (normalized)

• a is an uint8 array, with gravalues in [0, 255]. b is a double array, with gravalues in [0, 1] (obtained by linear scaling).

Image Negative

“Film Negative” Negative of “ film negative”

grayvalue max. : ,1)( LrLsrT −−==

0r

s

0

1−L

1−L

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]. • Special cases:

� Thresholding or binar ization

r1 = r2 , s1 = 0 and s2 = 1

00 1

1

r

s

r1 r2

s1

s2

01 =s

• Useful when we are only interested in the shape of the objects

and on on their actual grayvalues.

0 r

s

r1 = r2

12 =s

Grayscale “Blood cell” image a = i mr ead( ’ i mage. t i f ’ )

Binary “Blood cell” image b = i m2bw( a, 0. 5)

• Gamma correction:

Output Image is “darker”

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Output Image is “brighter”

• Example:

Grayscale Photograph a = i mr ead( ’ i mage. t i f ’ )

Contrast enhanced photograph b=i madj ust ( a, [ 0. 3, 0. 7] ,[ 0, 1] , 0. 7)

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.

)1log()( rcrTs +==

Saturn Image Mag. Spectrum Mag. Spectrum in log scale

• Graylevel Slicing: Highlight a specific range of grayvalues. •

• Example:

Original Image

r

s

r

s

Without background With background

Highlighted Image (no background)

Highlighted Image (with background)

• Bitplane Slicing: Display the different bits as individual binary images.

8 bitplanes of cameraman image

Image Subtraction

• In this case, the difference between two “similar” images is computed to highlight or enhance the differences between them:

• It has applications in image segmentation and enhancement

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Example: Mask mode radiography

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

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:

• The noise η (m, n) at each pixel (m, n) is modeled as a

random variable. • Usually, η (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”

),(),(),( nmnmfnmg η+=

Noisy Image Noise-free Image

Noise

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=M

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• In this case, we can show that

• Therefore, as the number of observations increases ( ∞→M ),

the effect of noise tends to zero.

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nmM

nmg

nmfnmgE

η=

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Image Averaging Example

Noise-free Image

Noisy Image Noise Variance = 0.05

M =2 M =5 M =10

M =25 M =50 M =100