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8/6/2019 Class5 Image Restoration
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ImageAcquisition
ImageEnhancement
ImageRestoration
ImageCompression
DIP Components
Image
Segmentation
Representation& Description
Recognition &Interpretation
Knowledge Base
Preprocessing low level
ImageCoding
MorphologicalImage Processing
WaveletAnalysis
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What Is Image Enhancement?
Image enhancement is the process ofmaking images more useful
The reasons for doing this include: Highlighting interesting detail in images Removing noise from images Making images more visually appealing
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Image Enhancement ExamplesImagestakenfromGonzalez&Woods,DigitalImageProcessing(2
002)
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Image Enhancement Examples(cont)
ImagestakenfromGonzalez&Woods,DigitalImageProcessing(2
002)
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Image Enhancement Examples(cont)
ImagestakenfromGonzalez&Woods,DigitalImageProcessing(2
002)
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Image Enhancement Examples(cont)
ImagestakenfromGonzalez&Woods,DigitalImageProcessing(2
002)
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Spatial & Frequency Domains
There are two broad categories of imageenhancement techniques
Spatial domain techniques Direct manipulation of image pixels
Frequency domain techniques Manipulation of Fourier transform or wavelet
transform of an image
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Image Histograms
The histogram of an image shows us thedistribution of grey levels in the image
Massively useful in image processing,
especially in segmentation
Grey Levels
Fre
quencies
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Histogram ExamplesImagestakenfromGonzalez&Woods,DigitalImageProcessing(2
002)
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Histogram Examples (cont)ImagestakenfromG
onzalez&Woods,DigitalImageProcessing(2
002)
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Histogram Examples (cont)ImagestakenfromG
onzalez&Woods,DigitalImageProcessing(2
002)
Dark image
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Histogram Examples (cont)ImagestakenfromG
onzalez&Woods,DigitalImageProcessing(2
002)
Dark image
Dark Bright
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Histogram Examples (cont)ImagestakenfromG
onzalez&Woods,DigitalImageProcessing(2
002)
Bright image
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Histogram Examples (cont)ImagestakenfromG
onzalez&Woods,DigitalImageProcessing(2
002)
Bright image
Dark Bright
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Histogram Examples (cont)ImagestakenfromG
onzalez&Woods,DigitalImageProcessing(2
002)
Low contrastimage
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Histogram Examples (cont)ImagestakenfromG
onzalez&Woods,DigitalImageProcessing(2002)
High contrastimage
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Histogram Examples (cont)ImagestakenfromG
onzalez&Woods,DigitalImageProcessing(2002)
High contrast image
Dark Bright
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Histogram Examples (cont)
A selection of images andtheir histograms
Notice the relationshipsbetween the images andtheir histograms
Note that the high contrastimage has the mostevenly spaced histogram
ImagestakenfromG
onzalez&Woods,DigitalImageProcessing(2002)
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Histogram Equalisation
Spreading out the frequencies in an image(or equalising the image) is a simple way toimprove dark or washed out imagesThe formula for histogramequalisation is given where
rk: input intensity
sk: processed intensity
k: the intensity range(e.g 0.0 1.0)
nj: the frequency of intensity j
n: the sum of all frequencies
)( kk rTs =
=
=k
j
jr rp
1
)(
=
=k
j
j
n
n
1
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Equalisation Transformation Function
ImagestakenfromG
onzalez&Woods,DigitalImageProcessing(2002)
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Equalisation ExamplesImagestakenfromG
onzalez&Woods,DigitalImageProcessing(2002)
1
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Equalisation Transformation Functions
The functions used to equalise the imagesin the previous exampleImagestakenfromG
onzalez&Woods,DigitalImageProcessing(2002)
1. Dark2. Bright3. Low
contrast4. High
contrast
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Equalisation ExamplesImagestakenfromG
onzalez&Woods,DigitalImageProcessing(2002)
2
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Equalisation Transformation Functions
The functions used to equalise the imagesin the previous exampleImagestakenfromG
onzalez&Woods,DigitalImageProcessing(2002)
1. Dark2. Bright3. Low
contrast4. High
contrast
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Equalisation Examples (cont)ImagestakenfromG
onzalez&Woods,DigitalImageProcessing(2002)
3
4
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Equalisation Examples (cont)ImagestakenfromG
onzalez&Woods,DigitalImageProcessing(2002)
3
4
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Equalisation Transformation Functions
The functions used to equalise the imagesin the previous examples
ImagestakenfromG
onzalez&Woods,DigitalImageProcessing(2002)
1. Dark2. Bright3. Low
contrast4. High
contrast
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Chapter 3Image Enhancement in the
Spatial Domain
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Contrast Stretching
We can fix images that have poor contrastby applying a pretty simple contrastspecification
The interesting part is how do we decide onthis transformation function?
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Chapter 3Image Enhancement in the
Spatial Domain
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Chapter 3Image Enhancement in the
Spatial Domain
Log transformationS=c log(1+r)
Image negatives
S=L-1-r
Power LawtransformationS=c r
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Chapter 3Image Enhancement in the
Spatial Domain
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Chapter 3Image Enhancement in the
Spatial Domain
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Chapter 3Image Enhancement in the
Spatial Domain
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Chapter 3Image Enhancement in the
Spatial Domain
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Chapter 3Image Enhancement in the
Spatial Domain
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Chapter 3Image Enhancement in the
Spatial Domain
Low contrast image
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Chapter 3Image Enhancement in the
Spatial Domain
Low contrast image-Poor illumination-Lack of dynamicrange in image sensor-Wrong setting of lens
aperture
Binary image(thresholding)r1= r2
s1= 0, s2 = L-1
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Chapter 3Image Enhancement in the
Spatial DomainGrey level slicing
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Chapter 3Image Enhancement in the
Spatial Domain
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Chapter 3Image Enhancement in the
Spatial Domain
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Chapter 3Image Enhancement in the
Spatial Domain
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Chapter 3Image Enhancement in the
Spatial Domain
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Chapter 3Image Enhancement in the
Spatial Domain
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Chapter 3Image Enhancement in the
Spatial Domain
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Chapter 3Image Enhancement in the
Spatial Domain
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Chapter 3Image Enhancement in the
Spatial Domain
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Chapter 3Image Enhancement in the
Spatial Domain
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Chapter 3Image Enhancement in the
Spatial Domain
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Chapter 3Image Enhancement in the
Spatial Domain
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Chapter 3Image Enhancement in the
Spatial Domain
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Chapter 3Image Enhancement in the
Spatial Domain
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Chapter 3Image Enhancement in the
Spatial Domain
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Chapter 3Image Enhancement in the
Spatial Domain
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Degradation Models
Image degradation can occur for many reasons, some typicaldegradation models are
2 2
2
2
2 2 2
2
1 0( , )
0
( , )
1,
( , ) 2 2
0
1
( , )
0
i j
ai bjh i j
otherwise
h i j Ke
L Li j
h i j L
otherwise
i j Rh i j R
otherwise
+
+ ==
=
=
+ =
Motion Blur: due to camerapanning or subject moving quickly.
Atmospheric Blur: long exposure
Uniform 2D Blur
Out-of-Focus Blur
CGU IPAM 2003: Inverse Problems
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Noise sources
Image acquisition Image transmission
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Noise models
Spatially independent noise models Gaussian noise Rayleigh noise
Erlang (Gamma) noise Exponential noise Impulse (salt-and-pepper) noise
Spatially dependent noise model Periodic noise
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Noise Models
Most noise models assume the noise is some known probabilitydensity function. The density function is chosen based on theunderlining physics.
Gaussian: poor illumination.
Rayleigh: range image
Salt andPepper: faulty switch during imaging
Gammaor Exp: laser imaging
CGU IPAM 2003: Inverse Problems
22 2/)(1)(
= zezp
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2)(
= ezp