DESIGNING AND MAKING OF NOISE REDUCTION

APPLICATION USING IMAGE MORPHOLOGY

by : Dionisius Kristal / 26406061

Preliminary

0The image result from the digital camera is not suit with the expected result.

0The camera that produces image with a little noise is very expensive.

0Some people develop algorithms to eliminate the noise (called noise reduction).

Previous Work

0 In 1986, Sternberg introduced the idea of noise reduction by repeatedly opening and closing with an increasing structuring elements size.

0Song and Delp in 1990, discovered a technique they called "generalized morphological filter". However, use of structuring elements must be precise so that results can be maximized.

Theory

0 Image ProcessingDigital image processing is a discipline that studies

matters relating to improvement of image quality.

0MorphologyMorphology is the science of form and structure. It is

about regions or shapes, how they can be changed and counted, and how their areas can be evaluated.

Theory

0Dilation

to expand/grow

0 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 00 0 0 1 1 1 1 1 1 1 0 0 00 0 0 1 1 1 1 1 1 1 0 0 00 0 0 1 1 1 1 1 1 1 0 0 00 0 0 1 1 1 1 1 1 1 0 0 00 0 0 1 1 1 1 1 1 1 0 0 00 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0

1 0 10 1 01 0 1

0 0 0 0 0 0 0 0 0 0 0 0 00 0 1 1 1 1 1 1 1 1 1 0 00 0 1 1 1 1 1 1 1 1 1 0 00 0 1 1 1 1 1 1 1 1 1 0 00 0 1 1 1 1 1 1 1 1 1 0 00 0 1 1 1 1 1 1 1 1 1 0 00 0 1 1 1 1 1 1 1 1 1 0 00 0 1 1 1 1 1 1 1 1 1 0 00 0 0 0 0 0 0 0 0 0 0 0 0

Theory

0Erosion

to reduce/shrink

0 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 00 0 0 1 1 1 1 1 1 1 0 0 00 0 0 1 1 1 1 1 1 1 0 0 00 0 0 1 1 1 1 1 1 1 0 0 00 0 0 1 1 1 1 1 1 1 0 0 00 0 0 1 1 1 1 1 1 1 0 0 00 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0

11111

0 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 00 0 0 1 1 1 1 1 1 1 0 0 00 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 0 0 0

Theory

Dilation Erosion

EXPANDED REDUCED

Theory

0Opening (Erosion, then Dilation)

( )A B A B B

Theory

0Closing (Dilation, then Erosion)

( )A B A B B

Theory

0Peak Signal to Noise Ratio

2

2

[ ( , ) ( , )]f i j F i jMSE

N

Peak^2 is the peak pixel value between two

images.RMSE is square root of

MSE.

The Sum of (Original Image – Result

Image)^2/(width*height)^2

Theory

What is Noise Reduction?Noise Reduction is program/system that has ability to reduce the (image) noise.

Original Image Result Image

TheoryNoise Reduction using Mathematical Morphology?0“Noisy” image = clean image + noise0Segment into features and noise (the residual image).0Residual image = the difference between an original image and smoothed version.0The features from residual image will be added back to the smoothed image.0The results is an image whose edges and other one dimensional features are as sharp as the original one, but has smooth regions between them.

Original Image

Result Image

How it works?

0The process is divided by 2 processes : smoothing process, and detail recovery.

0Smoothing process is OCCO filtering (morphologycal opening-closing-closing-opening)

0Detail recovery process is TOPBOT filtering (Tophat and Bothat) where Tophat is a positive residual image and Bothat is a negative residual image.

0The output of TOPBOT filtering is Tophat and Bothat accumulation, where in this case, Tophat and Bothat accumulation is clean from noise.

How it works?

The final result :

“Clean” image = Tophat Accumulation + OCCO Image – Bothat Accumulation

How it works?

OCCO image

Tophat Accumulation

Bothat Accumulation

“Clean” image = Tophat Accumulation + OCCO Image – Bothat Accumulation

How it works?

OCCO image

How it works?

Final Result

Flowchart (main)

Start

Input Citra I, input repeat

S=OCCO(I,SE)

End

Output Citra Hasil

Inisialisasi T

T’=TOPBOT(T)

Tacc=Tacc+T’

Inisialisasi B

B’=TOPBOT(B)

Bacc=Bacc+B’

counter<repeat counter++SE+=2

Hasil=Tacc+S-Bacc

Y

N

OCCO filtering

Tophat filtering

Bothat filtering

Final Summary

Flowchart OCCO

O=OPEN(I)

OC=CLOSE(O)

C=CLOSE(I)

OCCO image = ½ (OC+CO)

Start

Input Citra I, Input SE

Inisialisasi Citra S, O, OC, C, CO, X=0, Y=0

End

Output Citra OCCO

O=OPEN(I,SE)

OC=CLOSE(O,SE)

C=CLOSE(I,SE)

CO=OPEN(C,SE)

X<Image width

Y<Image Height

S=SetPixel(½ (OC.GetPixel(X,Y) + CO.GetPixel(X,Y))

Y

N

Y

X++

Y++N

CO=OPEN(C)

Flowchart TOPBOT

Start

Input Citra R

Inisialisasi variabel X1,X2,X3,X4,X5,X6

X0=THRESHOLD(R)

X1=RANK(X0)

X2=DILATE(X1)

X3=X0 AND X2

X4=ISODEL(X3)

Ada deletion

X0=X4

Y

N

X5=SKELETON(X4)

X6=DILATE(X5)

R’=R AND X6

Output Citra R’

END

System Design and Application

03 modules of the program are :• Load Module opening an image• Noise Reduction Module processing the noisy image• Save Image Module saving the image processing

result.

Experiment

• High ISO images are shot with ISO 400 and ISO 800• Then those images are being processed with the program and

Photoshop• The image results will be compared with Low ISO image and there

will be a number that indicate the PSNR score

ExperimentThere are 6 categories of image :0Image with small particle object (Kopi.jpg, Komputer.jpg, Tombol.jpg)0Image with bright object and dark background (Pasir.jpg, Beras.jpg)0Image with letter (Box.jpg, Majalah.jpg, Notes.jpg, Koran.jpg)0Image with certain pattern (Lemari.jpg, Jeans.jpg, Batik.jpg)0Face Image (Wajah1.jpg, Wajah2.jpg)0Image with Noise Generator added on it

Experiment Table (Kodak

M1033 ISO 400)

ISO Gambar Perulangan Indeks PSNR MIC NR PSNR Photoshop

400

Box.jpg (Gambar

5.42)

1 Gambar 5.38 25,43

32,012 Gambar 5.39 25,13

3 Gambar 5.40 24,93

Kopi.jpg (Gambar

5.44)

1 Gambar 5.41 24,06

31,152 Gambar 5.42 23,89

3 Gambar 5.43 23,84

Majalah.jpg (Gambar

5.46)

1 Gambar 5.44 27,33

25,212 Gambar 5.45 27,39

3 Gambar 5.46 27,33

Notes.jpg (Gambar

5.48)

1 Gambar 5.47 16,51

28,772 Gambar 5.48 16,35

3 Gambar 5.49 16,29

Pasir.jpg (Gambar

5.50)

1 Gambar 5.47 24,3

29,432 Gambar 5.48 24,09

3 Gambar 5.49 24,02

Tombol.jpg (Gambar

5.52)

1 Gambar 5.38 36,65

36,562 Gambar 5.39 36,32

3 Gambar 5.40 36,15

Komputer.jpg (Gambar

5.54)

1 Gambar 5.41 32,39

32,682 Gambar 5.42 31,82

3 Gambar 5.43 31,53

Koran.jpg (Gambar

5.56)

1 Gambar 5.44 21,35

23,282 Gambar 5.45 20,92

3 Gambar 5.46 20,72

Lemari.jpg (Gambar

5.58)

1 Gambar 5.47 33,14

33,72 Gambar 5.48 32,19

3 Gambar 5.49 31,69

Beras.jpg (Gambar

5.60)

1 Gambar 5.47 30,1

31,022 Gambar 5.48 29,34

3 Gambar 5.49 28,88

Batik.jpg (Gambar

5.62)

1 Gambar 5.47 27,1

27,762 Gambar 5.48 26,03

3 Gambar 5.49 25,49

Jeans.jpg (Gambar

5.64)

1 Gambar 5.47 20,25

24,612 Gambar 5.48 19,34

3 Gambar 5.49 19,27

Experiment Table (Kodak

M1033 ISO 800)

ISO Gambar Perulangan Indeks PSNR MIC NR PSNR Photoshop

800

Box.jpg (Gambar

5.43)

1 Gambar 5.38 26,68

31,252 Gambar 5.39 26,28

3 Gambar 5.40 26,01

Kopi.jpg (Gambar

5.45)

1 Gambar 5.41 28,96

32,442 Gambar 5.42 28,65

3 Gambar 5.43 28,57

Majalah.jpg (Gambar

5.47)

1 Gambar 5.44 24,62

23,692 Gambar 5.45 24,63

3 Gambar 5.46 24,57

Notes.jpg (Gambar

5.49)

1 Gambar 5.47 16,10

27,892 Gambar 5.48 15,93

3 Gambar 5.49 15,89

Pasir.jpg (Gambar

5.51)

1 Gambar 5.47 25,15

26,912 Gambar 5.48 24,94

3 Gambar 5.49 24,87

Tombol.jpg (Gambar

5.53)

1 Gambar 5.38 35,68

36,232 Gambar 5.39 34,62

3 Gambar 5.40 34,35

Komputer.jpg (Gambar

5.55)

1 Gambar 5.41 29,87

30,172 Gambar 5.42 29,43

3 Gambar 5.43 29,17

Koran.jpg (Gambar

5.57)

1 Gambar 5.44 20,39

21,322 Gambar 5.45 20,21

3 Gambar 5.46 20,1

Lemari.jpg (Gambar

5.59)

1 Gambar 5.47 33,05

33,232 Gambar 5.48 32,33

3 Gambar 5.49 31,94

Beras.jpg (Gambar

5.61)

1 Gambar 5.47 26,67

26,82 Gambar 5.48 26,56

3 Gambar 5.49 26,34

Batik.jpg (Gambar

5.63)

1 Gambar 5.47 23,66

24,042 Gambar 5.48 23,14

3 Gambar 5.49 22,82

Jeans.jpg (Gambar

5.65)

1 Gambar 5.47 19,86

21,52 Gambar 5.48 19,34

3 Gambar 5.49 19,27

Experiment Table (Nikon D90 ISO 400)

ISO Gambar Perulangan Indeks PSNR MIC NR PSNR Photoshop

400

Box.jpg (Gambar

5.66)

1 Gambar 5.38 24,38

27,542 Gambar 5.39 24,44

3 Gambar 5.40 24,22

Kopi.jpg (Gambar

5.68)

1 Gambar 5.41 21,57

24,542 Gambar 5.42 29,54

3 Gambar 5.43 30,44

Majalah.jpg (Gambar

5.70)

1 Gambar 5.44 30,53

31,782 Gambar 5.45 29,85

3 Gambar 5.46 29,56

Notes.jpg (Gambar

5.72)

1 Gambar 5.47 17,96

28,632 Gambar 5.48 17,5

3 Gambar 5.49 17,7

Pasir.jpg (Gambar

5.74)

1 Gambar 5.47 25,81

29,952 Gambar 5.48 22,37

3 Gambar 5.49 20,64

Tombol.jpg (Gambar

5.76)

1 Gambar 5.38 37,95

37,962 Gambar 5.39 37,18

3 Gambar 5.40 36,91

Komputer.jpg (Gambar

5.78)

1 Gambar 5.41 33,33

33,712 Gambar 5.42 32,77

3 Gambar 5.43 32,42

Koran.jpg (Gambar

5.80)

1 Gambar 5.44 24,2

25,722 Gambar 5.45 23,57

3 Gambar 5.46 23,69

Lemari.jpg (Gambar

5.82)

1 Gambar 5.47 37,49

37,452 Gambar 5.48 36,67

3 Gambar 5.49 35,45

Beras.jpg (Gambar

5.84)

1 Gambar 5.47 30,74

35,82 Gambar 5.48 29,1

3 Gambar 5.49 28,63

Batik.jpg (Gambar

5.86)

1 Gambar 5.47 22,31

22,382 Gambar 5.48 21,97

3 Gambar 5.49 21,8

Jeans.jpg (Gambar

5.88)

1 Gambar 5.47 17,15

16,252 Gambar 5.48 17,29

3 Gambar 5.49 17,28

Experiment Table (Nikon D90 ISO 800)

ISO Gambar Perulangan Indeks PSNR MIC NRPSNR

Photoshop

800

Box.jpg (Gambar

5.67)

1 Gambar 5.38 24,39

27.862 Gambar 5.39 24,08

3 Gambar 5.40 23,85

Kopi.jpg (Gambar

5.69)

1 Gambar 5.41 30,77

22.482 Gambar 5.42 30,53

3 Gambar 5.43 30,44

Majalah.jpg (Gambar

5.71)

1 Gambar 5.44 25,95

26.072 Gambar 5.45 25,79

3 Gambar 5.46 25,07

Notes.jpg (Gambar

5.73)

1 Gambar 5.47 17,67

28.252 Gambar 5.48 17,5

3 Gambar 5.49 17,39

Pasir.jpg (Gambar

5.75)

1 Gambar 5.47 25,3

31.042 Gambar 5.48 25,08

3 Gambar 5.49 24,90

Tombol.jpg (Gambar

5.77)

1 Gambar 5.38 39,01

39,152 Gambar 5.39 38,12

3 Gambar 5.40 37,75

Komputer.jpg

(Gambar 5.79)

1 Gambar 5.41 30,83

30,932 Gambar 5.42 30,56

3 Gambar 5.43 30,33

Koran.jpg (Gambar

5.81)

1 Gambar 5.44 22,21

22,852 Gambar 5.45 21,97

3 Gambar 5.46 21,90

Lemari.jpg (Gambar

5.83)

1 Gambar 5.47 34,17

33,592 Gambar 5.48 34,15

3 Gambar 5.49 33,58

Beras.jpg (Gambar

5.85)

1 Gambar 5.47 27,52

28,162 Gambar 5.48 27,07

3 Gambar 5.49 26,9

Batik.jpg (Gambar

5.87)

1 Gambar 5.47 22,33

22,422 Gambar 5.48 21,98

3 Gambar 5.49 21,89

Jeans.jpg (Gambar

5.89)

1 Gambar 5.47 17,46

16,492 Gambar 5.48 17,53

3 Gambar 5.49 17,2

Conclusion0The score difference between the program and

Photoshop are not too far. It means, the output of the program is similar to Photoshop has.

0The program does not process the “small particle” image too well because there are detail from the image (which is very small) that lost. (Ex: Kopi.jpg)

0The program consumes high resources and takes a long time. The program runs about 3 minutes for each repetition

0The repetition maximum amount is 3, otherwise the result image will be blurred.

Conclusion (cont.)0 Image resolution also affects the result image. The

result of low resolution image will be more blurred (compare to the high resolution image).

0There are several types of images that are not suitable to use skeletonize process due to the residues that are not accurate and the effectiveness of time.

THANK YOU