Lecture 4 Image Restoration

Lecture 4

Image Restoration

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Image Restoration

• Image restoration: recover an image that has been

degraded by using a prior knowledge of the degradation

phenomenon.

• Model the degradation and applying the inverse process in

order to recover the original image.

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A Model of Image Degradation/Restoration

Process

• Degradation

– Degradation function H

– Additive noise ),( yx

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Process

If is a process, then

the degraded image is given in the spatial domain by

( , ) ( , ) ( , ) ( , )

H linear, position-invariant

g x y h x y f x y x y

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Process

The model of the degraded image is given in

the frequency domain by

( , ) ( , ) ( , ) ( , )G u v H u v F u v N u v

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Noise Sources

• The principal sources of noise in digital images arise during

image acquisition and/or transmission

Image acquisition

e.g., light levels, sensor temperature, etc.

Transmission

e.g., lightning or other atmospheric disturbance in wireless

network

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Noise Models (1)

• White noise

– The Fourier spectrum of noise is constant

• With the exception of spatially periodic noise, we assume

– Noise is independent of spatial coordinates

– Noise is uncorrelated with respect to the image itself

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Noise Models (2)

Gaussian noiseElectronic circuit noise, sensor noise due to poor illumination and/or high temperature

Rayleigh noiseRange imaging

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Range Imaging (1)

Short for High Dynamic Range Imaging. HDRI is an imaging technique that allows for a greater dynamic range of exposure than would be obtained through any normal imaging process.

It is now popularly used to refer to the process of tone mapping* together with bracketed** exposures of normal digital images, giving the end result a high, often exaggerated dynamic range

* Tone mapping is a technique used in image processing and computer graphics to map a set of colours to another; often to approximate the appearance of HDRI in media with a more limited dynamic range

** bracketing is the general technique of taking several shots of the same subject using different or the same camera settings

http://en.wikipedia.org/wiki/Tone_mapping

http://en.wikipedia.org/wiki/Bracketing

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Range Imaging (2)

The intention of HDRI is to accurately represent the wide range of intensity levels found in real scenes ranging from direct sunlight to shadows

HDRI, also called HDR (High Dynamic Range) is a feature commonly found in high-end graphics and imaging software

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Range Imaging: Examples (1)

Tower Bridge in Sacramento,

CA

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Range Imaging: Examples (2)

http://en.wikipedia.org/wiki/High_dynamic_range_imaging

Sydney Harbour Bridge HDRi produces greater detail and fewer shadows

13Old Saint Paul’s Wellinton, New Zealand

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Noise Models (3)

Erlang (gamma) noise: Laser imaging

Exponential noise: Laser imaging

Uniform noise: Least descriptive; Basis for numerous random number generators

Impulse noise: quick transients,

such as faulty switching

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Gaussian Noise (1)

2 2( ) /2

The PDF of Gaussian random variable, z, is given by

1 ( )

2

z zp z e

where, represents intensity

is the mean (average) value of z

is the standard deviation

z

z

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Gaussian Noise (2)

2 2( ) /2

The PDF of Gaussian random variable, z, is given by

1 ( )

2

z zp z e

– 70% of its values will be in the range

– 95% of its values will be in the range

)(),(

)2(),2(

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Rayleigh Noise

2( ) /

The PDF of Rayleigh noise is given by

2( ) for

( )

0 for

z a bz a e z ap z b

z a

2

The mean and variance of this density are given by

/ 4

(4 )

4

z a b

b

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Erlang (Gamma) Noise

1

The PDF of Erlang noise is given by

for 0 ( ) ( 1)!

0 for

b baza z

e zp z b

z a

2 2


/

/

z b a

b a

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Exponential Noise

The PDF of exponential noise is given by

for 0 ( )

0 for

azae zp z

z a

2 2


1/

1/

z a

a

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Uniform Noise

The PDF of uniform noise is given by

1 for a

( )

0 otherwise

z bp z b a

2 2


( ) / 2

( ) /12

z a b

b a

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Impulse (Salt-and-Pepper) Noise

The PDF of (bipolar) impulse noise is given by

for

( ) for

0 otherwise

a

b

P z a

p z P z b

If either or is zero, the impulse noise is calleda bP P

unipolar

if , gray-level will appear as a light dot,

while level will appear like a dark dot.

b a b

a

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Examples of Noise: Original Image

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Examples of Noise: Noisy Images(1)

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Examples of Noise: Noisy Images(2)

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Periodic Noise

• Periodic noise in an image arises typically from electrical or

electromechanical interference during image acquisition.

• It is a type of spatially dependent noise

• Periodic noise can be reduced significantly via frequency

domain filtering

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An Example of Periodic Noise

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Estimation of Noise Parameters (1)

The shape of the histogram identifies the closest PDF match

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Estimation of Noise Parameters (2)

Consider a subimage denoted by , and let ( ), 0, 1, ..., -1,

denote the probability estimates of the intensities of the pixels in .

The mean and variance of the pixels in :

s iS p z i L

S

S

1

0

12 2

0

( )

and ( ) ( )

L

i s i

i

L

i s i

i

z z p z

z z p z

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Restoration in the Presence of Noise Only

Spatial Filtering

Noise model without degradation

( , ) ( , ) ( , )

and

( , ) ( , ) ( , )

g x y f x y x y

G u v F u v N u v

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Spatial Filtering: Mean Filters (1)

Let represent the set of coordinates in a rectangle

subimage window of size , centered at ( , ).

xyS

m n x y

Arithmetic mean filter

xySts

tsgnm

yxf

),(

),(.

1),(ˆ

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Spatial Filtering: Mean Filters (2)

Generally, a geometric mean filter achieves smoothing comparable to the arithmetic mean filter, but it tends to lose less image detail in the process

mn

Sts xy

tsgyxf

1

),(

),(),(ˆ

Geometric mean filter:

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Spatial Filtering: Example

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Spatial Filtering: Order-Statistic Filters (1)

),(max),(ˆ),(

tsgyxfxySts

),(),(ˆ),(

tsgmedianyxfxySts

Median filter:

),(min),(ˆ),(

tsgyxfxySts

Max filter:

Min filter:

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),(min),(max

2

1),(ˆ

),(),(tsgtsgyxf

xyxy StsSts

Mid-point filter:

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We delete the / 2 lowest and the / 2 highest intensity values of

( , ) in the neighborhood . Let ( , ) represent the remaining

- pixels.

xy r

d d

g s t S g s t

mn d

xySts

r tsgdmn

yxf

),(

),(1

),(ˆ

Alpha-trim mean filter:

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Spatial Filtering: Adaptive Filters

Adaptive filters

The behavior changes based on statistical characteristics of the image inside the filter region defined by the mхn rectangular window.

The performance is superior to that of the filters discussed

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Adaptive Filters:

Adaptive, Local Noise Reduction Filters (1)

2

: local region

The response of the filter at the center point (x,y) of

is based on four quantities:

(a) ( , ), the value of the noisy image at ( , );

(b) , the variance of the noise corrupti

xy

xy

S

S

g x y x y

2

ng ( , )

to form ( , );

(c) , the local mean of the pixels in ;

(d) , the local variance of the pixels in .

L xy

L xy

f x y

g x y

m S

S

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Adaptive Filters:


2

2

The behavior of the filter:

(a) if is zero, the filter should return simply the value

of ( , ).

(b) if the local variance is high relative to , the filter

should return a value cl

g x y

ose to ( , );

(c) if the two variances are equal, the filter returns the

arithmetic mean value of the pixels in .xy

g x y

S

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Adaptive Filters:


L

L

myxgyxgyxf ),(),(),(ˆ2

2

An adaptive expression for obtaining a filtered image based on the assumptions:

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Adaptive Filters:

Adaptive Median Filters (1)

min

max

med

max

The notation:

minimum intensity value in

maximum intensity value in

median intensity value in

intensity value at coordinates ( , )

maximum all

xy

xy

xy

xy

z S

z S

z S

z x y

S

owed size of xyS

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Adaptive Filters:


med min med max

max

The adaptive median-filtering works in two stages:

Stage A:

A1 = ; A2 =

if A1>0 and A2<0, go to stage B

Else increase the window size

if window size , re

z z z z

S

med

min max

med

peat stage A; Else output

Stage B:

B1 = ; B2 =

if B1>0 and B2<0, output ; Else output

xy xy

xy

z

z z z z

z z

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Adaptive Filters:


med min med max

max

The adaptive median-filtering works in two stages:

Stage A:

A1 = ; A2 =

if A1>0 and A2<0, go to stage B

Else increase the window size

if window size , re

z z z z

S

med

min max

med

peat stage A; Else output

Stage B:

B1 = ; B2 =

if B1>0 and B2<0, output ; Else output

xy xy

xy

z

z z z z

z z

The median filter output is an impulse

or not

The processed point is an impulse or not

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Example:

Adaptive Median Filters

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Periodic Noise Reduction by Frequency

Domain Filtering

The basic idea

Periodic noise appears as concentrated bursts of energy in the Fourier transform, at locations corresponding to the frequencies of the periodic interference

Approach

A selective filter is used to isolate the noise

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Perspective Plots of Bandreject Filters

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Perspective Plots of Notch Filters

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Several interference components are present, the methods discussed in the preceding sections are not always acceptable because they remove much image information The components tend to have broad skirts that carry information about the interference pattern and the skirts are not always easily detectable.

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Linear, Position-Invariant Degradations

( , ) ( , ) ( , )g x y H f x y x y

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An operator having the input-output relationship

( , ) ( , ) is said to be position invariant

if

( , ) ( , )

for any ( , ) and any and .

g x y H f x y

H f x y g x y

f x y

1 2 1 2

1 2

is linear

( , ) ( , ) ( , ) ( , )

and are any two input images.

H

H af x y bf x y aH f x y bH f x y

f f

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In the presence of additive noise,

if is a linear operator and position invariant,

( , ) ( , ) ( , ) ( , )

( , ) ( , ) ( , )

( , ) ( , ) ( , ) ( , )

H

g x y f h x y d d x y

h x y f x y x y

G u v H u v F u v N u v

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Estimating the Degradation Function

• Three principal ways to estimate the degradation function

1. Observation

2. Experimentation

3. Mathematical Modeling

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Mathematical Modeling (1)

• Environmental conditions cause degradation

A model about atmospheric turbulence

2 2 5/6( ) ( , )

: a constant that depends on

the nature of the turbulence

k u vH u v e

k

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• Derive a mathematical model from basic principles

E.g., An image blurred by uniform linear motion between the

image and the sensor during image acquisition

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0 0

Suppose that an image ( , ) undergoes planar motion,

( ) and ( ) are the time-varying components of motion

in the - and -directions, respectively.

The optical imaging process is perfect. T is th

f x y

x t y t

x y

0 00

e duration

of the exposure. The blurred image ( , )

( , ) ( ), ( )T

g x y

g x y f x x t y y t dt

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0 0

0 0

2 ( ) ( )

0

Suppose that the image undergoes uniform linear motion

in the -direction and -direction, at a rate given by

( ) / and ( ) /

( , )

T j ux t vy t

x y

x t at T y t bt T

H u v e dt

e

2 [ ] /

0

( ) sin ( )( )

Tj ua vb t T

j ua vb

dt

Tua vb e

ua vb

(Consider g(x,y) as f(x,y) convolves with an array of Dirac pulses at different (x0,y0))

T « time of integration »

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Inverse Filtering

),(/),(),(

),(

),(),(*),(),(ˆ

),(

),(),(ˆ

vuHvuNvuF

vuH

vuNvuHvuFvuF

vuH

vuGvuF

An estimate of the original image:

Problem : noise is at high frequencies, H(u,v) is very small at high frequencies => amplify noise !!!

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Inverse Filtering

1. We can't exactly recover the undegraded image

because ( , ) is not known.

2. If the degradation function has zero or very

small values, then the ratio ( , ) / ( , ) could

easily dominate th

N u v

N u v H u v

e estimate ( , ).F u v

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Inverse Filtering

2

/2 (

EXAMPLE

The image in Fig. 5.25(b) was inverse filtered using the

exact inverse of the degradation function that generated

that image. That is, the degradation function is

( , )k u M v

H u v e

5/62/2)

, 0.0025N

k

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A Butterworth

lowpass

function of

order 10

The poor performance of direct inverse

filtering in general

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Minimum Mean Square Error (Wiener)

Filtering

N. Wiener (1942)

Objective

Find an estimate of the uncorrupted image such that the mean square error between them is minimized

22 )ˆ( ffEe

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Filtering

),(),(/),(),(

),(

),(

1),(ˆ

2

2

vuGvuSvuSvuH

vuH

vuHvuF

f

H(u,v): degradation function

H*(u,v): complex conjugate of H(u,v)

|H(u,v)|2 = H(u,v)H*(u,v)

Sn(u,v)= |N(u,v)|2: power spectrum of the noise

Sf(u,v)= |F(u,v)|2 : power spectrum of the undegraded image

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Filtering

),(),(

),(

),(

1),(ˆ

2

2

vuGKvuH

vuH

vuHvuF

K is a specified constant. Generally, the value of K is chosen interactively to yield the best visual results.

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Filtering

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Left: degradated image

Middle: inverse filtering

Right: Wiener filtering

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Constrained Least Squares Filtering

• In Wiener filter, the power spectra of the undegraded

image and noise must be known. Although a constant

estimate is sometimes useful, it is not always suitable.

• Constrained least squares filtering just requires the mean

and variance of the noise.

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Constrained Least Squares Filtering

2 2

*( , )( , ) ( , )

| ( , ) | | ( , ) |

( , ) is the Fourier transform of the function

0 1 0

( , ) 1 4 1

0 1 0

is a parameter

H u vF u v G u v

H u v P u v

P u v

p x y

(Regualarization term: ensure « smoothness » at high frequencies-Tikhonov regularization - minimize the output of the Laplacian operator ! )

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Examples

Lecture 4 Image Restoration

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Transcript of Lecture 4 Image Restoration