1. REDUKSI NOISE Pertemuan-8 John Adler
KK-Komputasi dan Kecerdasan Buatan Teknik Komputer
Universitas Komputer Indonesia-UNIKOM
Saturday, June 08, 2019

2. Face morphing

3. Procedures adopted Pre-processing:
When getting an image containing human faces,
it is always better to do some pre-processing such
like removing the noisy backgrounds,
clipping to get a proper facial image,
and scaling the image to a reasonable size.

4. Noise reduction

5. Features finding:
4 major feature points, namely the two eyes, and the two endpoints of the mouth. Within the scope of this project, we developed an eye-finding algorithm that successfully detects eyes at 84% rate.

6. บทที่ 4 การประมวลผลภาพดิจิตอล และ การประมวลผลภาพหลอดเลือด
Saturday, June 08, 2019
เอกสารประกอบการสอน วิชาการสร้างภาพทางการแพทย์ Medical Imaging

7. บทที่ 4 การประมวลผลภาพดิจิตอล และ การประมวลผลภาพหลอดเลือด
Saturday, June 08, 2019
เอกสารประกอบการสอน วิชาการสร้างภาพทางการแพทย์ Medical Imaging

8. บทที่ 4 การประมวลผลภาพดิจิตอล และ การประมวลผลภาพหลอดเลือด
Saturday, June 08, 2019
เอกสารประกอบการสอน วิชาการสร้างภาพทางการแพทย์ Medical Imaging

9. Mask = Median value of the appropriate 9 pixels in the original image
Median filtering
Mask = Median value of the appropriate 9 pixels in the original image
Saturday, June 08, 2019

10. Median filtering images
Digital chest radiograph with unwanted dot artifacts
After application of 3x1 medial filter to remove dots
Saturday, June 08, 2019

11. Reduksi Noise
Saturday, June 08, 2019

12. Shot & Salt Pepper Noise
Saturday, June 08, 2019

13. Filter Median
Saturday, June 08, 2019

14. Filter Min & Max Min
Saturday, June 08, 2019

15. Filter Max & Min Max
Saturday, June 08, 2019

16. DITHERING
John Adler
KK-Komputasi dan Kecerdasan Buatan Teknik Komputer
Universitas Komputer Indonesia-UNIKOM
Saturday, June 08, 2019

17. Dithering
Dithering Methods
There are three dithering methods that are commonly used in image processing programs:
noise
pattern
error diffusion dithering

18. Dithering Methods - Noise
Noise dithering (also called random dithering)
Eliminates the patchiness and high black/white contrast by adding high frequency noise—speckles of black and white that, when combined by the eye, look like shades of gray
Figure 3.3, shows thesholding which results in large patches of black and white
Figure 3.4 Noise (i.e., random)
dithering
(very noisy)
Figure 3.3 Thresholding

19. Dithering Methods - Pattern
Pattern dithering (also called ordered dithering or the Bayer method) (a)
Uses a regular pattern of dots to simulate colors
An m × m array of values between 1 and m2 is applied to each m × m block of pixels in the image file called a mask
The numbers in the mask will determine the appearance of the pattern in the dithered image file, e.g.,
1   7   4
5   8   3
6   2   9
Each pixel value p is scaled to a value p′ between
0 and m2

20. Dithering Methods - Pattern
Pattern dithering (also called ordered dithering or the Bayer method) (b)
In this case, we can divide all the pixels by 25.6 and drop the remainder
Assuming that the values initially are between 0 and 255, this will result in normalized values between 0 and 9
Then the normalized pixel value is compared to the value in the corresponding position in the mask
If p′ is less than that value, the pixel is given the value 0, or black; otherwise, it is white

21. Dithering Methods - Pattern
Pattern dithering (also called ordered dithering or the Bayer method) (c)
Pattern dithering is easy and fast, but it can result in a crosshatched effect, see Figure 3.5
Figure 3.5 Pattern dithering

22. Dithering Methods – Error Diffusion (1)
Error diffusion dithering (also called the Floyd–Steinberg algorithm)
Is a method that disperses the error, or difference between a pixel’s original value and the color (or grayscale) value available
For each pixel, the difference between the original color and the color available is calculated
Then this error is divided up and distributed to neighboring pixels that have not yet been visited
After all pixels have been visited, the color used for each pixel is the color in the reduced palette closest to the pixel’s
new value

23. Dithering Methods – Error Diffusion (2)
Error diffusion dithering (also called the Floyd–Steinberg algorithm)
The results of error diffusion dithering method are shown in Figure 3.6
Figure 3.6 Error diffusion dithering

24. Dithering Compensates for lack of color resolution
Eye does spatial averaging
Black/white dithering to achieve gray scale
Each pixel is black or white
From far away, color determined by fraction of white
For 3x3 block, 10 levels of gray scale

25. Dithering
Dithering takes advantage of the human eye's tendency to "mix"
two colors in close proximity to one another.

26. Dithering
Dithering takes advantage of the human eye's tendency to "mix"
two colors in close proximity to one another.
original
no dithering
with dithering
Colors = 224
Colors = 28
Colors = 28

27. Ordered Dithering How do we select a good set of patterns?
Regular patterns create some artifacts
Example of good 3x3 dithering matrix

28. Floyd-Steinberg Error Diffusion
Diffuse the quantization error of a pixel to its neighboring pixels
Scan in raster order
At each pixel, draw least error output value
Add the error fractions into adjacent, unwritten pixels
If a number of pixels have been rounded downwards, it becomes more likely that the next pixel is rounded upwards
7/16
3/16
5/16
1/16

29. Floyd-Steinberg Error Diffusion

30. Floyd-Steinberg Error Diffusion
Enhances edges
Retains high frequency
Some checkerboarding
From

31. Color Dithering Example: 8 bit framebuffer Dither RGB separately
Set color map by dividing 8 bits into 3,3,2 for RGB
Blue is deemphasized because we see it less well
Dither RGB separately
Works well with Floyd-Steinberg
Generally looks good

32. SPATIAL FILTERING John Adler
KK-Komputasi dan Kecerdasan Buatan Teknik Komputer
Universitas Komputer Indonesia-UNIKOM
Saturday, June 08, 2019

33. Convolution – Gaussian Blur (1)
Spatial Filtering
Convolution – Gaussian Blur (1)
The mask shown in Figure 3.25 takes an average of the pixels in a 3 × 3 neighborhood
An alternative for smoothing is to use a Gaussian blur, where the coefficients in the convolution mask get smaller as you move away from the center of the mask
Figure 3.25 Convolution for
Averaging pixels in a 3 × 3
neighborhood

34. Convolution – Gaussian Blur (2)
Spatial Filtering
Convolution – Gaussian Blur (2)
It is called a Gaussian blur because the mask values both the horizontal and vertical directions vary in the shape of a Gaussian bell curve (see Figure 3.26)
These values result in a weighted average of neighboring pixels
Figure 3.26 Convolution
mask for Gaussian blur

35. Example: Noise Reduction
Image with noise
Median filter (5x5)

36. Example: Noise Reduction
Original image
Image with noise
Median filter (5x5)

40. Some common types are: Neighborhood-averaging filters Median filters
Mode filters

41. Neighborhood-averaging filters
These replace the value of each pixel, by a weighted-average of the pixels in some neighborhood around it, i.e. a weighted sum of the weights are non-negative. If all the weights are equal then this is a mean filter. "linear"

42. Median filters
This replaces each pixel value by the median of its neighbors, i.e. the value such that 50% of the values in the neighborhood are above, and 50% are below. This can be difficult and costly to implement due to the need for sorting of the values. However, this method is generally very good at preserving edges.

43. Mode filters
Each pixel value is replaced by its most common neighbor. This is a particularly useful filter for classification procedures where each pixel corresponds to an object which must be placed into a class; in remote sensing, for example, each class could be some type of terrain, crop type, water, etc..

44. Referensi
Erkki Rämö, Digital Media, “Image Processing”, Principal Lecturer, Metropolia University of Applied Sciences.
Richard Alan Peters II, EECE/CS Image Processing, Lecture Note : Reduction of Uncorrelated Noise, Department of Electrical Engineering and Computer Science, Fall Semester 2011, Vanderbilt University School of Engineering
Saturday, June 08, 2019Saturday, June 08, 2019

45. TERIMA KASIH
Saturday, June 08, 2019