Download presentation

Presentation is loading. Please wait.

Published byAlysha Davis Modified over 3 years ago

1
Linear Filtering – Part I Selim Aksoy Department of Computer Engineering Bilkent University saksoy@cs.bilkent.edu.tr

2
CS 484, Spring 2010©2010, Selim Aksoy2 Importance of neighborhood Both zebras and dalmatians have black and white pixels in similar numbers. The difference between the two is the characteristic appearance of small group of pixels rather than individual pixel values. Adapted from Pinar Duygulu, Bilkent University

3
CS 484, Spring 2010©2010, Selim Aksoy3 Outline We will discuss neighborhood operations that work with the values of the image pixels in the neighborhood. Spatial domain filtering Frequency domain filtering Image enhancement Finding patterns

4
CS 484, Spring 2010©2010, Selim Aksoy4 Spatial domain filtering What is the value of the center pixel? What assumptions are you making to infer the center value? 333 3?3 333 343 2?3 342 3 3

5
CS 484, Spring 2010©2010, Selim Aksoy5 Spatial domain filtering Some neighborhood operations work with the values of the image pixels in the neighborhood, and the corresponding values of a subimage that has the same dimensions as the neighborhood. The subimage is called a filter (or mask, kernel, template, window). The values in a filter subimage are referred to as coefficients, rather than pixels.

6
CS 484, Spring 2010©2010, Selim Aksoy6 Spatial domain filtering Operation: modify the pixels in an image based on some function of the pixels in their neighborhood. Simplest: linear filtering (replace each pixel by a linear combination of its neighbors). Linear spatial filtering is often referred to as “convolving a mask with an image”. Filter masks are sometimes called convolution masks (or convolution kernels).

7
CS 484, Spring 2010©2010, Selim Aksoy7 Spatial domain filtering Filtering process: Masks operate on a neighborhood of pixels. The filter mask is centered on a pixel. The mask coefficients are multiplied by the pixel values in its neighborhood and the products are summed. The result goes into the corresponding pixel position in the output image. This process is repeated by moving the filter mask from pixel to pixel in the image.

8
CS 484, Spring 2010©2010, Selim Aksoy8 Spatial domain filtering This is called the cross-correlation operation and is denoted by Be careful about indices, image borders and padding during implementation. Input image F[r,c] Mask overlaid with image at [r,c] Output image G[r,c] H[-1,-1]H[-1,0]H[-1,1] H[0,-1]H[0,0]H[0,1] H[1,-1]H[1,0]H[1,1] Filter

9
CS 484, Spring 2010©2010, Selim Aksoy9 Smoothing spatial filters Often, an image is composed of some underlying ideal structure, which we want to detect and describe, together with some random noise or artifact, which we would like to remove. Smoothing filters are used for blurring and for noise reduction. Linear smoothing filters are also called averaging filters.

10
CS 484, Spring 2010©2010, Selim Aksoy10 Smoothing spatial filters Averaging (mean) filterWeighted average

11
CS 484, Spring 2010©2010, Selim Aksoy11 Smoothing spatial filters 1/9.(10x1 + 11x1 + 10x1 + 9x1 + 10x1 + 11x1 + 10x1 + 9x1 + 10x1) = 1/9.( 90) = 10 1/9.( 90) = 10 10 1110 9 10 11 10910 1 10 10 2 9 0 9 0 9 9 9 9 0 1 99 10 1011 10 1 11 11 11 11 1010 I 1 1 1 1 1 1 1 1 1 F X XX X 10 X X X X X X X X X X X X X X XX 1/9 O Adapted from Octavia Camps, Penn State

12
CS 484, Spring 2010©2010, Selim Aksoy12 Smoothing spatial filters 1/9.(10x1 + 9x1 + 11x1 + 9x1 + 99x1 + 11x1 + 11x1 + 10x1 + 10x1) = 1/9.( 180) = 20 1/9.( 180) = 20 I 1 1 1 1 1 1 1 1 1 F X XX X 20 X X X X X X X X X X X X X X XX 1/9 O 10 1110 9 10 11 10910 1 10 10 2 9 0 9 0 9 9 9 9 0 1 99 10 1011 10 1 11 11 11 11 1010 Adapted from Octavia Camps, Penn State

13
CS 484, Spring 2010©2010, Selim Aksoy13 Smoothing spatial filters Common types of noise: Salt-and-pepper noise: contains random occurrences of black and white pixels. Impulse noise: contains random occurrences of white pixels. Gaussian noise: variations in intensity drawn from a Gaussian normal distribution. Adapted from Linda Shapiro, U of Washington

14
CS 484, Spring 2010©2010, Selim Aksoy14 Adapted from Linda Shapiro, U of Washington

15
CS 484, Spring 2010©2010, Selim Aksoy15 Smoothing spatial filters Adapted from Gonzales and Woods

16
CS 484, Spring 2010©2010, Selim Aksoy16 Smoothing spatial filters Adapted from Gonzales and Woods

17
CS 484, Spring 2010©2010, Selim Aksoy17 Smoothing spatial filters Adapted from Darrell and Freeman, MIT

18
CS 484, Spring 2010©2010, Selim Aksoy18 Smoothing spatial filters A weighted average that weighs pixels at its center much more strongly than its boundaries. 2D Gaussian filter Adapted from Martial Hebert, CMU

19
CS 484, Spring 2010©2010, Selim Aksoy19 Smoothing spatial filters If σ is small: smoothing will have little effect. If σ is larger: neighboring pixels will have larger weights resulting in consensus of the neighbors. If σ is very large: details will disappear along with the noise. Adapted from Martial Hebert, CMU

20
CS 484, Spring 2010©2010, Selim Aksoy20 Smoothing spatial filters Result of blurring using a uniform local model. Produces a set of narrow horizontal and vertical bars – ringing effect. Result of blurring using a Gaussian filter. Adapted from David Forsyth, UC Berkeley

21
CS 484, Spring 2010©2010, Selim Aksoy21 Smoothing spatial filters Adapted from Martial Hebert, CMU

22
CS 484, Spring 2010©2010, Selim Aksoy22 Smoothing spatial filters Adapted from Martial Hebert, CMU

23
CS 484, Spring 2010©2010, Selim Aksoy23 Order-statistic filters Order-statistic filters are nonlinear spatial filters whose response is based on ordering (ranking) the pixels contained in the image area encompassed by the filter, and then replacing the value of the center pixel with the value determined by the ranking result. The best-known example is the median filter. It is particularly effective in the presence of impulse or salt-and-pepper noise, with considerably less blurring than linear smoothing filters.

24
CS 484, Spring 2010©2010, Selim Aksoy24 Order-statistic filters 10 1110 9 10 11 10910 1 10 10 2 9 0 9 0 9 9 9 9 0 1 99 10 1011 10 1 11 11 11 11 1010 I X XX X 10 X X X X X X X X X X X X X X XX O 10,11,10,9,10,11,10,9,109,9,10,10,10,10,10,11,11 sort median Adapted from Octavia Camps, Penn State

25
CS 484, Spring 2010©2010, Selim Aksoy25 Order-statistic filters 10 1110 9 10 11 10910 1 10 10 2 9 0 9 0 9 9 9 9 0 1 99 10 1011 10 1 11 11 11 11 1010 I X XX X 10 X X X X X X X X X X X X X X XX O 10,9,11,9,99,11,11,10,109,9,10,10,10,11,11,11,99 sort median Adapted from Octavia Camps, Penn State

26
CS 484, Spring 2010©2010, Selim Aksoy26 Salt-and-pepper noise Adapted from Linda Shapiro, U of Washington

27
CS 484, Spring 2010©2010, Selim Aksoy27 Gaussian noise Adapted from Linda Shapiro, U of Washington

28
CS 484, Spring 2010©2010, Selim Aksoy28 Order-statistic filters Adapted from Gonzales and Woods

29
CS 484, Spring 2010©2010, Selim Aksoy29 Order-statistic filters Adapted from Martial Hebert, CMU

30
CS 484, Spring 2010©2010, Selim Aksoy30 Sharpening spatial filters Objective of sharpening is to highlight or enhance fine detail in an image. Since smoothing (averaging) is analogous to integration, sharpening can be accomplished by spatial differentiation. First-order derivative of 1D function f(x) f(x+1) – f(x). Second-order derivative of 1D function f(x) f(x+1) – 2f(x) + f(x-1).

31
CS 484, Spring 2010©2010, Selim Aksoy31 Sharpening spatial filters Adapted from Gonzales and Woods

32
CS 484, Spring 2010©2010, Selim Aksoy32 Sharpening spatial filters Adapted from Gonzales and Woods

33
CS 484, Spring 2010©2010, Selim Aksoy33 Sharpening spatial filters Observations: First-order derivatives generally produce thicker edges in an image. Second-order derivatives have a stronger response to fine detail (such as thin lines or isolated points). First-order derivatives generally have a stronger response to a gray level step. Second-order derivatives produce a double response at step changes in gray level.

34
CS 484, Spring 2010©2010, Selim Aksoy34 Sharpening spatial filters

35
CS 484, Spring 2010©2010, Selim Aksoy35 Sharpening spatial filters Adapted from Gonzales and Woods

36
CS 484, Spring 2010©2010, Selim Aksoy36 Sharpening spatial filters Robert’s cross-gradient operators Sobel gradient operators

37
CS 484, Spring 2010©2010, Selim Aksoy37 Sharpening spatial filters High-boost filtering Adapted from Darrell and Freeman, MIT

38
CS 484, Spring 2010©2010, Selim Aksoy38 Sharpening spatial filters Adapted from Darrell and Freeman, MIT

39
CS 484, Spring 2010©2010, Selim Aksoy39 Sharpening spatial filters Adapted from Darrell and Freeman, MIT

40
CS 484, Spring 2010©2010, Selim Aksoy40 Combining spatial enhancement methods

Similar presentations

OK

Grauman Today: Image Filters Smooth/Sharpen Images... Find edges... Find waldo…

Grauman Today: Image Filters Smooth/Sharpen Images... Find edges... Find waldo…

© 2019 SlidePlayer.com Inc.

All rights reserved.

To make this website work, we log user data and share it with processors. To use this website, you must agree to our Privacy Policy, including cookie policy.

Ads by Google