Simple Image Processing Speaker : Lin Hsiu-Ting Date : 2005 / 04 / 27.

Simple Image Processing

Speaker : Lin Hsiu-Ting

Date : 2005 / 04 / 27

Outline

Concept of Image Processing

Space Domain Image Processing

Frequency Domain Image Processing

Geometry Transform

Shape Processing

Color System

Concept of Image Processing

Concept

The “Image” Signals We Can See Include Special

Information We Process These Signals To Get Relative

Information Integration Technology

Engineering Mathematics Physical Biology Medical Science Entertainments

Concept

Application Digital Photo Map Natural Disaster Monitored Others…

Relative Software Photo Shop Photo Impact Others… These Aren’t Today Key Points

Concept

General Topics of Image Process Image Capture & Image Digitize Image Stretch & Remove Distortion Shape Process Image Features Extracted Color Image Process Image Coding & Compression

Concept

Image Digitized Sampling Quantization Coding

Non-Ideal Situations In Process Quantization Error Distortion Noise

Image with Noise

Images Usually Suffer Noise When Sampling

(Like Use Scanners or Digital Cameras…) Some Common Noise

Dot Noise Uniform Noise Sinusoid Wave Noise Gaussian Noise Other

Sometimes We Can Remove Noise According Their Features

Image with Noise

Dot Noise

Uniform Noise

Image with Noise

Sinusoid Wave Noise

Gaussian Noise

Space Domain Image Processing

Space Domain Image Processing

Characteristic Representation Profile Histogram Statistic ( Mean & Standard Deviation )

Point Operation Binarization Inverse Contract Stretch Histogram Equalization Gamma Correction Arithmetic & Logic Operation

Binarization Before Binarization ( 8-bit Gray Level )

Binarization (Threshold = 200)

Contract Stretch

Before Processing

After Processing

Process Flow

Load Image

Histogram

Statistic

Stretch

n

MinMax

MinffT 2)(

Histogram Equalization

Before Processing

After Processing

Process Flow

Load Image

Histogram

Statistic

Equalization

n

iin fpfT

0

)()(

Arithmetic (Add & Sub) Image #1

Image #2

Image #1 + Image #2

Image #1 - Image #2

Space Domain Image Processing

Range Operation Smoothing ( Low Pass Filter ) Median Filter High Pass Filter Differentiation

Mask Matrix

987

654

321

*),(),('

www

www

www

yxfyxf

Note ： We Can Also Use 5x5 , 7x7 or Larger Matrix Process Range Operation But It Cause More Computing

Median Filter

Before Processing

After Processing

For Every 3 x 3 Block

Search Cn = Median (C)

Let f (x , y) = Cn

Note : The Method Will Have Poor Result When A Lot Of Noise Cluster

987

654

321

CCC

CCC

CCC

Frequency Domain Image Processing

Frequency Domain Image Processing

Fast Fourier Transform

Implement Recursion Algorithm

Butterfly Algorithm

Easy To Achieve Filter High Pass / Low Pass

Band Pass / Notch

1

2

02/

12

02/ ]12[]2[][

N

r

kN

kN

N

r

kN wrxwwrxkX

Frequency Domain Image Processing

2D Fast Fourier Transform

)),(((),( yxfFFTFFTvuF vyux

Do FFT For Every Row

……………..

.................

Do FFT For Every Column

F ( u , v )

Note : We Always Use Log Unit Present The Spectrum Distribute Instead of Linear Because Its Dynamic Range is Larger Then Screen

Frequency Domain Image Processing

Image

Spectrum

Image with Sin Noise

Spectrum

Geometry Transform

Geometry Transform

Coordinates Transform Rotation

Scaling

Twist

Gray Level Interpolation Replicative Interpolation

Bilinear Interpolation

Coordinates Transform

Rotation

Scaling

Twist

yCosxSiny

ySinxCosx

'

'

byy

axx

'

'

yy

yTanxx

'

'

Gray Level Interpolation

When We Transform From R to R* Some Point In R* Can’t Correspond From R Rotation, Magnify Suffer This Question Ex: Magnify

1 2 3

4 5 6

7 8 9

1 ? 2 ? 3 ?

? ? ? ? ? ?

4 ? 5 ? 6 ?

? ? ? ? ? ?

7 ? 8 ? 9 ?

? ? ? ? ? ?

Gray Level Interpolation

Replicative Interpolation Use The Nearest Point To Present

Let j = Int(x+0.5) , k = Int(y+0.5) =>

g ( x’ , y’ ) = f ( j , k )

Bilinear Interpolation Use Four Neighborhood Points More Smooth Than Replicative

Gray Level Interpolation Replicative Interpolation

Bilinear Interpolation

Shape Processing

Shape Processing

Find The Edges And Bones Binarization

Process The Edge And Bone Erosion Dilation Open / Close Remove Isolate Points

Usually Simple Logic Operation

Erosion & Dilation

Binarization Image Erosion

Dilation

Color System

Color System

The Colors We See Wave Length 380 nm ~ 780 nm Use Rods to Recognize Brightness Use Cones to Recognize Colors

(Three Types For R. G. B. Colors) Usually Eyes Are More Sensitive To

Brightness Than Colors This Feature is Convenient For Image

Compressing

Color System

Common Color System

R. G. B. System (Red, Green and Blue)

C. M. Y. System (Cyan, Magenta and Yellow)-- A Complement of R. G. B

Y. U. V System

Y. I. Q System

H. S. I. System

Conclusion

Image Processing Is Useful Image Processing Is Interesting Although We Needn’t Know The

Details Of Techniques Because Many Powerful Software Will Handle Them…

But Knowing General Concept Is Helpful For Us

Reference

數位影像處理 - 連國珍 著 , 儒林出版 http://www.cs.ecnu.edu.cn/teach/down

/dip/Chapter02.pps http://www.fosu.edu.cn