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Chapter 3 Image Enhancement

Image Enhancement

To process an image so that the result is more

suitable than the original image for a specific

application.

Objective Image enhancement is one of the most

interesting and visually appealing areas of image

processing.

Note (1) images by non-prejudicial information

(2) image enhancement in general is a

complex image processing system of

"preconditioning" link.

Categories of Image Enhancement

- Image enhancement approaches fall into two broad

categories - Spatial domain refers to the image plane itself,

and approaches in this category are based on

direct manipulation of pixels in an image. - Frequency domain processing techniques are based

on modifying the Fourier transform of an image.

Main contents of Image enhancement

Background I

- The simplest form of T is when the neighborhood

is of size 1 1 (that is, a single pixel). - In this case, g depends only on the value of f

at (x, y), and T becomes a gray-level (also

called an intensity or mapping) transformation

function of the form -

(3-2) - r and s are variables denoting, respectively, the

gray level of f(x, y) and g(x, y).

Background II

- 33 neighborhood about a point (x, y) in an

image

concept of point operating

- The scope operator T for 1 ? 1, that is, only the

role of a single pixel, the output of g (x, y)

only with the position (x, y) the Agency's input

f (x, y), and "point-to-point treatment " T

operator as a "gray-scale transformation

function" (gray-scale transformation, GST) (also

known as the intensity map), describes the input

and output gray level gray-scale mapping

relationship between. Also known as the

"contrast enhancement", "contrast stretching" or

"gray-scale transformation."

s T (r), r the original

image gray level, s enhanced gray-scale image, T

mapping relations.

Histogram I

- An image histogram is a graphical representation

of the number of pixels in an image as a function

of their intensity.

Histogram II

- The vertical axis is the relative number of

pixels at each of the 255 tonal values. - The taller hump in the graph, the more pixels

reside at that particular tonal range.

Histogram III

A quite good image should have some different

tonal values. That means the tonal range of the

image should distribute from left to right in the

histograms. Simultaneously the histograms both

sides will not have overflowed.

Histogram IV

Histogram Processing - How It Works I

- The operation is very simple. The image is

scanned in a single pass and a running count of

the number of pixels found at each intensity

value is kept. This is then used to construct a

suitable histogram. - The histogram of an 8-bit image, for example can

be thought of as a table with 256 entries, or

bins, indexed from 0 to 255. In bin 0 we record

the number of times a gray level of 0 occurs in

bin 1 we record the number of times a grey level

of 1 occurs, and so on, up to bin 255.

Histogram Processing - How It Works II

- Assign zero values to all element of the array hf

- For all pixels (x,y) of the image f , increment

hf f(x,y) by 1 .

Another way to get the histogram is to use the C

code, as following

int i,j int imgnm int hstnum

for(i0iltnumi) hsti0 for(j0jltn

j) for(I0iltmi) hstimgjI

Histogram equalization (HE)

- Transforms the intensity values so that the

histogram of the output image approximately - Matches the flat (uniform) histogram

Histogram equalization II

- As for the discrete case the following formula

applies - k 0,1,2,...,L-1
- L number of grey levels in image (e.g., 255)
- nj number of times j-th grey level appears in

image - n total number of pixels in the image

Histogram of the nature of

- Image reflects the distribution And visual

experience has the consistency A given image

histogram only, given the corresponding image

histogram is not the only Reflect the whole,

does not reflect the details

The use of histogram

- (1) of the digitized image to provide a

reasonable reference (2) The histogram of the

statistical properties easily obtained images of

objects of interest to the area. (3) determine

the image binarization threshold

Digitized image to provide a reasonable reference

- Histogram give a visual indicator used to judge

the digital image are quantified Whether the

rational use of all the gray area permit

A) proper quantification

B) can not effectively make use of dynamic range

C) exceeds the dynamic range

Determine the image binarization threshold

Corresponds to the Background section

Background image are black, gray objects.

Background of black pixels produced a

histogram on the left peak, Objects in the

gray-scale histogram produced a peak on the

right. As the Objects in a relatively small

number of border pixels, resulting in two peaks

of Between Valley. Choose Valley as the

corresponding gray-scale range T, to use under

Type of image binarization obtained a binary

image.

Corresponds to the Object part

Threshold T

Programming method with histogram express

int i,j int imgnm int hstnum

for(i0iltnumi) hsti0 for(j0jltn

j) for(I0iltmi) hstimgjI

Histogram equalization deal

- Histogram equalization refers to a histogram with

gray-scale mapping transform so as to achieve the

purpose of image enhancement methods - Disadvantage
- 1) transformed to reduce the gray-scale images,

some details of the disappearance of 2) certain

images, such as histogram has a peak contrast

ratio of treated too unnatural enhancement.

- Histogram equalization deal with the "central

idea" is the original image histogram from the

more concentrated into a gray zone in all of gray

within the scope of uniform distribution.

Histogram equalization is a non-linear image

stretching, re-distribution of image pixel values

are within the scope of a certain gray-scale

pixel quantity or less the same. Histogram

equalization is to a given change in the

distribution of the image histogram into a

"uniform" distribution histogram distribution. ?

Figure (b) are changes in function. Figure (c)

the result is a similar, very good balanced

result. Although the balance derived from the

image histogram is not flat, gray-level

reduction, but the distribution of Of view, than

the original image histogram read flat, and

expanded dynamic range. Therefore, the straight

side Figure balanced in real terms is to reduce

the gray level of image contrast for the

expansion.

Histogram equalization III

Histogram of the provisions of

- Histogram equalization to automatically determine

the transformation function, which seek to

produce a uniform output image histogram.

Histogram refers to the provisions of the

adoption of an image after gray-scale

transformation so that it will have a particular

histogram graph form, such as the image with a

standard image have the same histogram, or the

image has a specific function form of histogram

Histogram equalization IV

Histogram equalization V

cumulative histogram

Histogram equalization VI

Histogram equalization VII

Histogram equalization VIII

Histogram can also be taken on a part of the

image

Histogram specification (HS)

- An image's histogram is transformed according to

a desired function - Transforming the intensity values so that the

histogram of the output image approximately

matches a specified histogram.

Histogram specification II

histogram1

histogram2

S-1T

S

T

Histogram specification III

- 1.
- 2.
- 3.

Gray-scale transformation

- Gray-scale image enhancement transformation is

another means, it will enable increased dynamic

range images, are the expansion of image

contrast, image more clearly, the characteristics

become more pronounced. Gray-scale

transformation of its substance is to amend

certain rules according to each image pixel

gray-scale, thus changing the image of the

dynamic range. Gray-scale transformation,

including a linear gray-scale transformation,

piecewise linear gray-scale transformation, such

as non-linear gray-scale transformation.

Gray Level Transformations II

OK

cancel

overload

store

smooth

auto

preview

input

output

Linear gray-scale transformation

The assumption that the image f (x, y) of

gray-scale range of a, b, Linear

transformation images g (x, y) the range of a ',

b',

Histogram specification IV

Gray Level Transformations

- Contrast stretching is a simple image enhancement

technique that attempts to improve the contrast

in an image by stretching the range of intensity

values it contains to span desired range of

values . - Gray Level Transformations concludes linear

contrast stretch ,Piecewise Linear Contrast

Stretch , Nonlinear contrast stretch .

Gray Level Transformations I

sr sT(r)

Linear contrast stretch I

Linear gray-scale transformation

Linear contrast stretch II

Linear contrast stretch III

255

Piecewise Linear Contrast Stretch

Nonlinear contrast stretch I

- The general form of the log transformations is
- The general form of the Index transformations is

Non-linear gray-scale transformation

- Logarithmic transform the general expression for
- Exponential transform the general expression for

When the hope that the image of the low gray area

greater tensile and compression of

high-gray-zone, the This can transform, which

enables the image gray distribution and

characteristics of human visual match.

This transformation can be the high gray-scale

image gives a much greater tensile zone.

Nonlinear contrast stretch II

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