Lec5 Image Enhancement

8/11/2019 Lec5 Image Enhancement

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Digital Image Processing

Lecture 3: Basic Image Processing

Sibt ul Hussain


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Slides Credit: Andrew Zisserman


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An Image as 2D Function


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Key Stages in Digital Image Processing: A

Traditional Linear View

Image

Acquisition

Image

Restoration

or!"ological

Processing

Segmentation

#$%ect

Recognition

Image&n"ancement

Re!resentation

' Descri!tion

Pro$lem Domain

Colour Image

Processing

Image

Com!ression


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Key Stages in Digital Image Processing: A

Traditional Linear View

Image

Acquisition

Image

Restoration

or!"ological

Processing

Segmentation

#$%ect

Recognition

Image&n"ancement

Re!resentation

' Descri!tion

Pro$lem Domain

Colour Image

Processing

Image

Com!ression


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Key Stages in Digital Image Processing:

Image &n"ancement

Image

Acquisition

Image

Restoration

or!"ological

Processing

Segmentation

#$%ect

Recognition

Image&n"ancement

Re!resentation

' Descri!tion

Pro$lem Domain

Colour Image

Processing

Image

Com!ression

Processing an image so t"at t"e result ismore suita$le (or a !articular a!!lication) *s"ar!ening or de+$lurring an out o( (ocus image, "ig"lig"ting edges, im!ro-ingimage contrast, or $rig"tening an image, remo-ing noise.

T"i $ id d i t"


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Image Resotration

Image

Acquisition

Image

Restoration

or!"ological

Processing

Segmentation

#$%ect

Recognition

Image&n"ancement

Re!resentation

' Descri!tion

Pro$lem Domain

Colour Image

Processing

Image

Com!ression

T"is may $e considered as re-ersing t"edamage done to an image $y a /nown cause) *remo-ing o( $lurcaused $y linear motion, remo-al o( o!tical distortions.


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or!"ological Processing

Image

Acquisition

Image

Restoration

or!"ological

Processing

Segmentation

#$%ect

Recognition

Image&n"ancement

Re!resentation

' Descri!tion

Pro$lem Domain

Colour Image

Processing

Image

Com!ression


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Segmentation

Image

Acquisition

Image

Restoration

or!"ological

Processing

Segmentation

#$%ect

Recognition

Image&n"ancement

Re!resentation

' Descri!tion

Pro$lem Domain

Colour Image

Processing

Image

Com!ression


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Re!resentation ' Descri!tion

Image

Acquisition

Image

Restoration

or!"ological

Processing

Segmentation

#$%ect

Recognition

Image&n"ancement

Re!resentation

' Descri!tion

Pro$lem Domain

Colour Image

Processing

Image

Com!ression


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#$%ect Recognition

Image

Acquisition

Image

Restoration

or!"ological

Processing

Segmentation

#$%ect

Recognition

Image&n"ancement

Re!resentation

' Descri!tion

Pro$lem Domain

Colour Image

Processing

Image

Com!ression


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Image Com!ression

Image

Acquisition

Image

Restoration

or!"ological

Processing

Segmentation

Re!resentation

' Descri!tion

Image&n"ancement

#$%ect

Recognition

Pro$lem Domain

Colour Image

Processing

Image

Com!ression


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Color Image Processing

Image

Acquisition

Image

Restoration

or!"ological

Processing

Segmentation

Re!resentation

' Descri!tion

Image&n"ancement

#$%ect

Recognition

Pro$lem Domain

Color Image

Processing

Image

Com!ression


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Sampling

Digitization of Coordinate Values


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Quantization


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Image Formation

Sampling: Digitization of the coordinate values,

Depends on effective number of digital sensors.

Defines the spatial resolution of image.

Quantization: Digitization of the amplitude values

Defined in number of bits, usually 8 bits are used

to record 256 different shades or light variations.


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Types of image

Binary: Each pixel is black or white, so only one bit is needed

per pixel.

GrayScale: Each pixel is a shade of gray normally from black

(0) to white (255) so 8 bits per pixel.

Colour: Each pixel is a colour described by the mix of red,

green or blue (RGB). Each channel has a range of 0-255, so

24bits in total.

Indexed: Most colour images only use a subset of the possible

colours, for reduction of storage a colour map is used.


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Binary Image


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Gray Scale Image

White = 255 Black = 0

>> w=imread(tire.tif); figure ,imshow(w), pixval on


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RGB Image

>> pep=imread('peppers.png'); figure(1) ,imshow(pep); pixval on;

>> r=pep(:,:,1);g=pep(:,:,2); b=pep(:,:,3);figure(2),imshow(r),pixval on


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Indexed Image


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Image Size

Image files tend to be large

e.g. 512 x 512 gray scale image requires 256

Kbytes = 512 x 512 x 8bits. For an RGB color image this will be 768

Kbytes= 256 x 3


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General Commands

imread: Read an image

figure: creates a figure on the screen.

imshow(g): which displays the matrix g as an image.

pixval on: turns on the pixel values in our figure.

impixel(i,j): the command returns the value of the pixel (i,j)

iminfo: Information about the image.


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Image Enhancement

Image is composed of informative pattern modified bynon-informative variations.

Goal : Enhance informative pattern based on image data.

Examples: noise filtering,contrast adjustment, gamma

correction, etc. Two types of image enhancement methods

Spatial domain (local) methods: operate directly onimage pixels.

Frequency domain (global) methods: operate in atransformed domain.

We will mainly deal with spatial domain methods.


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Spatial Domain Methods

f(x,y)

g(x,y)

g(x,y)

f(x,y)

Point

Processing

Area/MaskProcessing


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Point Processing

Simplest yet contain powerful processing methods

for image enhancement.

Arithmetic operations

Histogram based methods

Histogram Stretching

Histogram Equalization


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Point Processing Transformations

Convert a given pixel value to a new pixel value based on

some predefined function.


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Identity Transformation


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1. Arithmetic Operations

Apply simple function y= f(x) to each pixel gray value, those

include

Additions and subtractions:

Multiplication by a constant:

Where C is a constant

Note: Clipping will be required to keep the values in the range.


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Addition and Subtraction

dd dSb


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Adding a constant lighten the image.

Subtracting makes it darker.

Add dSb


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>> im=imread('blocks.tif');

>> im = double(im) + 128;

>> im = min(im, 255); figure, imshow(im,[])

>> im=imread('block.jpg');

>> im = double(im) - 128;

>> im = max(im, 0); figure, imshow(im,[])

OR

>> im=imread('blocks.tif'); im=imadd(im,128),imshow(im);

>> im=imread('block.jpg'); im=imsubtract(im,128),

M lili i dDiii


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Multiplication and Division

Multiplication implies scaling.

Lightens if C > 1; Darkens if C < 1;

M ltiliti dDiii


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Multiplication and Division

I C l t


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Image Complements

Sliti


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Solarization

Sliti


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Solarization

Complementing dark Pixels. Complementing light Pixels.


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SomeExamples


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Some Examples

Multiplication

SomeExamples


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Some Examples

Division

Credits:0aris Pa!asai/a+0anusc"

odesy

SlicingTransformations


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Slicing Transformations

Stretch grayle!el ranges "here "e desire more information (slope # $)%&ompress grayle!el ranges that are of little interest (' slope $)%

SlicingTransformations


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Slicing Transformations

Add


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Image Addition Useful for combining information between twoimages

ImageAveraging


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Image Averaging Image quality can be improved by averaging a

number of images together (very useful inastronomy applications).

1 23

43 13 233

images must $e

registered5

I Sb i


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Image Subtraction

Useful for change detection.

I Sbt ti ( td)


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Image Subtraction (contd)

edical a!!lication)

iodine medium in%ected

into t"e $loodstream

di((erence en"anced

Image


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gMultiplication/Division

Suppose a sensor introduces some shading in the form:g(x,y)=f(x,y) h(x,y)

We can estimate h(x,y) and remove shading by division.

original s"ade !attern s"ade correction

C ti !!!


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Caution !!!

Arithmetic operation can produce pixel values outside of the

range [0 255].

You should convert values back to the range [0 255] to ensure

that the image is displayed properly.

How would you do the following mapping?

[fmin fmax][ 0 255]

PowerLaw()Transformations


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Power Law ( ) Transformations

PowerLaw()Transformations


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Power Law ( ) Transformations

Image Histograms


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ge stog s

An image histogram is a plot of the grayle!el fre*+encies (i%e%, the n+mer of

pixelsinthe image that ha!e that gray le!el)%

Image Histograms (Cont.)


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g g ( )

Divide frequencies by total number of pixels to represent as

probabilities.

Nnp kk /=

Image Histograms


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g g Records the distribution of different pixel values e.g.

Given a greyscale image, its histogram consists of a graph

indicating the number of times each grey level occurs in

the image.

>> pep=imread('pout.tif'); figure(1) ,imhist(pep),axis tight;

Image Histograms


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g g

We can infer a lot about image appearance from its

histogram.

In adark image, the gray levels (and hence the histogram)

would be clustered at the lower end.

In auniformly bright image, the gray levels would beclustered at the upper end.

In awell contrastedimage, the gray levels would be well

spread out over much of the range:

Image Histograms


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g g

Image Histograms


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g g

Given a poorly contrasted or a dark image we would

like to improve its contrast or lighten it. We can do

this by

1.Histogram (Contrast) Stretching (Requires ManualInput)

2.Histogram Equalization. (Automatic)

1. Histogram Stretching


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g g

1. Histogram Stretching


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g g

2. Histogram Equalization


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We know that in a well contrasted image, the gray levels

would bewell spread out over much of the range.

In other words each gray level value (shade) from [0,

255] will have equal occurrence probability in animage.

Goal: The goal of histogram equalization is to apply a

transformation function T(r) on the input image such that

each gray level has equal occurrence probability.



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We know that in a well contrasted image, the gray levels

would bewell spread out over much of the range.

In other words each gray level value (shade) from [0,

255] will have equal occurrence probability in animage.

Goal: The goal of histogram equalization is to apply a

transformation function T(r) on the input image such that

each gray level has equal occurrence probability.


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The transformation function needs to satisfy two conditions.

1.It must be a monotonic function.

2.It must be within the following range


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In discrete case it is very simply, we simply map each

gray level to its corresponding CDF value i.e.

A gray level k will be mapped to sum of

probabilities upto level k.

where (L-1) is the total number of gray levelsand is the scaling factor used to map pixels

back in the range [0, L-1].



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Example



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Example



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Example



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Histogram Matching


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Goal: The goal is to apply a transformation function T(r)

on the input image such that its graylevels have

distribution similar to the pre-specified one.

!*6.

6L+23

Histogram Matching


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!*6.

6L+23

Step I Step II

Step III

Algorithm: Histogram Matching


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*+

1.Equalize the input image to obtain equalized histograms=T(r).

2.Find the transformation G(z) for the pre-specified histogram,

and built alookuptable for mapping z G(z).3.For each value of s, find its closest G(z) value and respectively

its corresponding z value.

4.Finally map the s value to z.

Histogram Matching:Example


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**

Histogram Matching


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*,


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Histogram Matching


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,1


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Histogram Matching


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,)

Histogram Matching


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,-Note: Histogram matching is a trial and test method.

Local Enhancement


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,5

Normally, transformation function based on thecontent of an entire image.

Some cases it is necessary to enhance detailsover small areas (local neighbourhoods) in animage.

The histogram processing techniques are easilyadaptable to local enhancement.

Local Enhancement


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,.

Pi7el+to+!i7el translation 8ono-erla!!ing region

Local Equalization


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,+

Local Equalization


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,*

Bit-plane slicing


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,,

Instead of highlighting gray-level ranges,highlighting the contribution made to totalimage appearance by specific bits might bedesired.

Suppose that each pixel in an image isrepresented by 8 bits. Imagine that the image iscomposed of eight 1-bit planes, ranging from

bit-plane 0, the least significant bit to bit-plane

7, the most significant bit.

2

2

9it+!lane 3*least signi(icant.


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100

23223322

2

3

3

2

23

2

9it+!lane

*most signi(icant.


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Bit-plane Slicing


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102


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Lookup Tables Point operations can be done very efficiently using


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p y y g

Lookup (LUT) tables.

1.We pre-calculate the transformation function T(r)

(mapping) value for each gray-scale value and

store that in a table.

2.We map each pixel value to its corresponding

mapped value via a single look-up.

Example: consider the division by 2 operations,

the lookup table will look like.


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Spatial Domain Image Enhancement


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These methods can be categorized into two classes:

1. Neighbourhood processing: To modify or change a pixel

value we only need to know its local neighbour gray values.

2. Point processing: works independently on each pixel i.echanges its value without knowledge of its surroundings.

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