/
Image Enhancement Image enhancement refers to the class of image processing operations Image Enhancement Image enhancement refers to the class of image processing operations

Image Enhancement Image enhancement refers to the class of image processing operations - PowerPoint Presentation

greyergy
greyergy . @greyergy
Follow
383 views
Uploaded On 2020-10-22

Image Enhancement Image enhancement refers to the class of image processing operations - PPT Presentation

The relevant features for the examination task are enhanced The irrelevant features for the examination task are removedreduced Here the input and output image are both digital image in color or gray scale ID: 814688

histogram image contrast enhancement image histogram enhancement contrast spatial original smoothing equalization unsharp filtering operations color averaging mask range

Share:

Link:

Embed:

Download Presentation from below link

Download The PPT/PDF document "Image Enhancement Image enhancement refe..." is the property of its rightful owner. Permission is granted to download and print the materials on this web site for personal, non-commercial use only, and to display it on your personal computer provided you do not modify the materials and that you retain all copyright notices contained in the materials. By downloading content from our website, you accept the terms of this agreement.


Presentation Transcript

Slide1

Image Enhancement

Image enhancement refers to the class of image processing operations whose goal is to produce an output digital image that is visually more suitable as appearance for its visual examination by a human observer

The relevant features for the examination task are enhanced

The irrelevant features for the examination task are removed/reduced

Here the input and output image are both digital image in color or gray scale.

Slide2

Slide3

The following operations are generally done on the image during image enhancement

noise removal;

geometric distortion correction;

edge enhancement;

contrast enhancement;

image zooming;

image subtraction;

pseudo-coloring.

Slide4

Image enhancement techniques can be divided into two broad categories:

1.Spatial domain methods,

which

operate directly on pixels,

and

2.frequency

domain methods,

which

operate on the Fourier transform of an image

.

Unfortunately

, there is no general theory for determining what is `good' image enhancement when it comes to human perception. If it looks good, it is good!

we will discuss them one by one……

Slide5

Slide6

Spatial Domain Methods

Slide7

Spatial Domain: Content

Slide8

Point Operations

Slide9

Contrast Stretching

Increasing the contrast of the output image(say J) such that J maps the values in original image(say I) such that some of the data is saturated at low and high intensities of I.

Slide10

Contrast Stretching

Clipping:

Thresholding

:

Output : Binary image

v

a

b

u

v

a

v

b

L

L

Point Operations

Slide11

Contrast stretching:

When

When

 

v

a

b

u

v

a

v

b

L

L

Slide12

Contrast Stretching

Contrast Stretching, a = 80, b= 160

Slide13

Original Image

Contrast stretched

Clipped

Thresholded

Slide14

Grey level: Gamma

C

orrection

In this operation, the value of

is corrected to enhance the image.

narrow I range to a wider dynamic range

to a narrow range

Thus in this way the gamma is corrected as required.

Its predominantly used for image

capture,display

and printing

 

Slide15

v

u

Gamma

Correction

Original

gamma=0.45

gamma=2.20

v

is the output and u is the input image

Slide16

Image negative, image transform

As is evident by name, it is used to take the complement of the image, or its negative

(

s:out

r:input)

 

s

r

Slide17

Log Transform

In log transform, we compress dynamic range of images that have large variation in intensities.

It’s the most common display for Fast Fourier Transforms(FFTs)

 

s

r

Slide18

Bit Extraction

Range Compression

:

Image Subtraction and Change Detection

medical imaging application : display blood-flow paths

automated inspection of printed circuits

security monitoring

Other Point Operations

Slide19

Histogram

The histogram (h) of an image with L gray levels is defined as:

,

Where,

is the k-

th

gray level and

is the number of pixels with intensity

.

 

Slide20

Slide21

Histogram Processing

Slide22

Histogram equalization

The goal of histogram equalization is to equalize the histogram, it spreads the histogram of input image over a large range of intensities

v is uniformly distributed over (0,1).

 

Slide23

There can be other methods to equalize the histogram as well, thus we have some different choices for F(u). Some of them are:

etc

 

Slide24

Histogram Equalization Result

Slide25

Histogram Equalization: its types

SHE: Standard Histogram Equalization

BHE: Bi Histogram Equalization

RMHE: Recursive Mean-Separate Histogram Equalization

CHE: Clipped Histogram Equalization

BOHE: Block-Overlapped Histogram Equalization

GACHE: Gain-Adjustable Clipped Histogram Equalization

They are used for contrast enhancement of the image

Slide26

Contrast enhancement by various types of HE

(a) Original (b) SHE (c) BHE (d) CHE

Slide27

Discrete case

u

w

Example

Find the transformation between

u

and

v

0

1

2

3

0

1

2

3

Histogram

Specification

Slide28

Histogram Matching

Histogram matching refers to the comparison of histogram of one image to that of other.

For example, in the below image, the histograms of the image of a type and Brain-image are matched.

Slide29

Histogram mapping of image 1

:

Histogram of image 1 is

:

Histogram matching to image 2 can be done as

:

Thus in this way histograms of several images are matched.

 

Slide30

Spatial Operations

They refer to the operations that are applied to the pixels having some common features and which can be enhanced in group.

There are various spatial operations that are applied on the image, some of them are

Slide31

It is used in

noise smoothing, low pass filtering, sub-sampling

Revisiting simple random variable theory

Spatial Averaging and Spatial Low-pass Filtering

Slide32

-

Mean-filtering

-

Noise

reduction

Spatial Averaging

Slide33

Spatial averaging masks

a(

k,l

)

(a) 2 x 2 window

(b) 3 x 3 window

(c) 5-point weighted

averaging

l

k

0

0

0

0

l

k

Spatial averaging masks

a

(

k, l

)

l

k

Example of Spatial

Averaging

Slide34

Spatial Filtering

Original image

Lowpass filtered image

Highpass filtered image

Bandpass filtered image

Slide35

Directional Smoothing

Directional smoothing is a technique which is used to overcome the problems associated with simple averaging mask.in this method we:

Compute

spatial average along several directions

Take

the result from the direction giving the smallest

changes before

& after filtering

Slide36

It is used to

protect the edges from blurring while

smoothing process is going on..

l

k

Directional Smoothing

Slide37

It can be of two types, vertical smoothing and diagonal smoothing.

In vertical smoothing we smoothen the image vertically while in diagonal smoothing diagonal smoothening is carried on.

Vertical smoothing

Diagonal smoothing

Slide38

Median filtering

It is a non-linear smoothing operator which has the following advantages

resilient

to statistical outliers

incurs

less blurring

simple

to

implement

median value ξ over a small window of size

Nw

odd window size is commonly used

3x3

, 5x5,

7x7

5-pixel

“+”-shaped window

Slide39

Example

Median Filtering

Slide40

40

Comparison-Median Filters with Different Sizes

Original

3x3

5x5

9x9

Slide41

Experiment-Gaussian Noise

Noisy

(Gaussian)

3x3

5x5

9x9

Slide42

Unsharp Masking

Unsharp masking

is an image manipulation technique

for increasing

the apparent sharpness of photographic images.

The

"unsharp" of the name derives from the fact that the

technique uses

a blurred, or "unsharp", positive to create a "mask" of

the original

image.

The

unsharped

mask is then combined with

the negative

, creating a resulting image sharper than the original.

Slide43

Steps of Unsharp

Masking

Blur

the image

Subtract

the

blurred version

from the

original (this

is called the mask)

Add the “mask” to

the original image.

Slide44

Signal

Low-pass

High-pass

(1)

(2)

(3)

(1)+

(3)

Unsharp Masking and

Crispening

Sharpened signal

Blurred signal

Unsharp mask

Slide45

Image of moon with unsharp mask

Original Image

Image with unsharp mask applied

Slide46

Contrast

ratio:

Inverse contrast ratio: enhance weak edges

 

Inverse Contrast Ratio Mapping and Statistical Scaling

Slide47

47

Replication

Linear

Interpolation

P-

th

order linear interpolation

Linear interpolation and Magnification

Interlacing by

p

rows and

columns zeros

Convolve

H

H

H

1

2

p

Slide48

Original image

Lowpass filtered image

Interpolation

Subsampled

(1/4)

Zero interlace

(2x)

1st Zooming

2nd Zero interlace

(4x)

2nd Zooming

Slide49

49

The goal is

to attract the attention of observers

A mapping from Gray scale to Color

Color

coordinate

transformation

Display

Feature

extraction

Input

images

R

G

B

False Color and Pseudo Color

Slide50

Color image enhancement

The basic idea behind color image enhancement is to enhance every component image by filtering out R,G,B and then mixing the enhanced versions so as to enhance the colored image.

Slide51

Basic idea

example

:

(Histogram equalization )

Coordinate

conversion

Monochrome

image enhancement

Monochrome

image enhancement

Monochrome

image enhancement

Inverse

coordinate

transformation

Display

R

G

B

Input

image

Color Image Enhancement

Slide52

END