Lecture 2 Intensity Transformation and Spatial Filtering Spatial
- Slides: 88
Lecture 2. Intensity Transformation and Spatial Filtering
Spatial Domain vs. Transform Domain ► Spatial domain image plane itself, directly process the intensity values of the image plane ► Transform domain process the transform coefficients, not directly process the intensity values of the image plane 6/19/2021 2
Spatial Domain Process 6/19/2021 3
Spatial Domain Process 6/19/2021 4
Spatial Domain Process 6/19/2021 5
Some Basic Intensity Transformation Functions 6/19/2021 6
Image Negatives 6/19/2021 7
Example: Image Negatives Small lesion 6/19/2021 8
Log Transformations 6/19/2021 9
Example: Log Transformations 6/19/2021 10
Power-Law (Gamma) Transformations 6/19/2021 11
Example: Gamma Transformations 6/19/2021 12
Example: Gamma Transformations Cathode ray tube (CRT) devices have an intensity-to-voltage response that is a power function, with exponents varying from approximately 1. 8 to 2. 5 6/19/2021 13
Example: Gamma Transformations 6/19/2021 14
Example: Gamma Transformations 6/19/2021 15
Piecewise-Linear Transformations ► Contrast Stretching — Expands the range of intensity levels in an image so that it spans the full intensity range of the recording medium or display device. ► Intensity-level Slicing — Highlighting a specific range of intensities in an image often is of interest. 6/19/2021 16
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Highlight the major blood vessels and study the shape of the flow of the contrast medium (to detect blockages, etc. ) 6/19/2021 Measuring the actual flow of the contrast medium as a function of time in a series of images 18
Bit-plane Slicing 6/19/2021 19
Bit-plane Slicing 6/19/2021 20
Bit-plane Slicing 6/19/2021 21
Histogram Processing ► Histogram Equalization ► Histogram Matching ► Local Histogram Processing ► Using Histogram Statistics for Image Enhancement 6/19/2021 22
Histogram Processing 6/19/2021 23
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Histogram Equalization 6/19/2021 25
Histogram Equalization 6/19/2021 26
Histogram Equalization 6/19/2021 27
Histogram Equalization 6/19/2021 28
Example 6/19/2021 29
Example 6/19/2021 30
Histogram Equalization 6/19/2021 31
Example: Histogram Equalization Suppose that a 3 -bit image (L=8) of size 64 × 64 pixels (MN = 4096) has the intensity distribution shown in following table. Get the histogram equalization transformation function and give the ps(sk) for each sk. 6/19/2021 32
Example: Histogram Equalization 6/19/2021 33
Example: Histogram Equalization 6/19/2021 34
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Question Is histogram equalization always good? No 6/19/2021 37
Histogram Matching Histogram matching (histogram specification) — generate a processed image that has a specified histogram 6/19/2021 38
Histogram Matching 6/19/2021 39
Histogram Matching: Procedure ► Obtain pr(r) from the input image and then obtain the values of s ► Use the specified PDF and obtain the transformation function G(z) ► Mapping from s to z 6/19/2021 40
Histogram Matching: Example Assuming continuous intensity values, suppose that an image has the intensity PDF Find the transformation function that will produce an image whose intensity PDF is 6/19/2021 41
Histogram Matching: Example Find the histogram equalization transformation for the input image Find the histogram equalization transformation for the specified histogram The transformation function 6/19/2021 42
Histogram Matching: Discrete Cases ► Obtain pr(rj) from the input image and then obtain the values of sk, round the value to the integer range [0, L-1]. ► Use the specified PDF and obtain the transformation function G(zq), round the value to the integer range [0, L-1]. ► Mapping from sk to zq 6/19/2021 43
Example: Histogram Matching Suppose that a 3 -bit image (L=8) of size 64 × 64 pixels (MN = 4096) has the intensity distribution shown in the following table (on the left). Get the histogram transformation function and make the output image with the specified histogram, listed in the table on the right. 6/19/2021 44
Example: Histogram Matching Obtain the scaled histogram-equalized values, Compute all the values of the transformation function G, 6/19/2021 45
Example: Histogram Matching 6/19/2021 46
Example: Histogram Matching Obtain the scaled histogram-equalized values, Compute all the values of the transformation function G, s 0 s 2 s 1 s 3 s 4 s 5 s 6 s 7 6/19/2021 47
Example: Histogram Matching 6/19/2021 48
Example: Histogram Matching 6/19/2021 49
Example: Histogram Matching 6/19/2021 50
Example: Histogram Matching 6/19/2021 51
Example: Histogram Matching 6/19/2021 52
Example: Histogram Matching 6/19/2021 53
Local Histogram Processing Define a neighborhood and move its center from pixel to pixel At each location, the histogram of the points in the neighborhood is computed. Either histogram equalization or histogram specification transformation function is obtained Map the intensity of the pixel centered in the neighborhood Move to the next location and repeat the procedure 6/19/2021 54
Local Histogram Processing: Example 6/19/2021 55
Using Histogram Statistics for Image Enhancement Average Intensity Variance 6/19/2021 56
Using Histogram Statistics for Image Enhancement 6/19/2021 57
Using Histogram Statistics for Image Enhancement: Example 6/19/2021 58
Spatial Filtering A spatial filter consists of (a) a neighborhood, and (b) a predefined operation Linear spatial filtering of an image of size Mx. N with a filter of size mxn is given by the expression 6/19/2021 59
Spatial Filtering 6/19/2021 60
Spatial Correlation 6/19/2021 61
Spatial Convolution 6/19/2021 62
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Smoothing Spatial Filters Smoothing filters are used for blurring and for noise reduction Blurring is used in removal of small details and bridging of small gaps in lines or curves Smoothing spatial filters include linear filters and nonlinear filters. 6/19/2021 64
Spatial Smoothing Linear Filters 6/19/2021 65
Two Smoothing Averaging Filter Masks 6/19/2021 66
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Example: Gross Representation of Objects 6/19/2021 68
Order-statistic (Nonlinear) Filters — Nonlinear — Based on ordering (ranking) the pixels contained in the filter mask — Replacing the value of the center pixel with the value determined by the ranking result E. g. , median filter, max filter, min filter 6/19/2021 69
Example: Use of Median Filtering for Noise Reduction 6/19/2021 70
Sharpening Spatial Filters ► Foundation ► Laplacian Operator ► Unsharp Masking and Highboost Filtering ► 6/19/2021 Using First-Order Derivatives for Nonlinear Image Sharpening — The Gradient 71
Sharpening Spatial Filters: Foundation ► The first-order derivative of a one-dimensional function f(x) is the difference ► The second-order derivative of f(x) as the difference 6/19/2021 72
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Sharpening Spatial Filters: Laplace Operator The second-order isotropic derivative operator is the Laplacian for a function (image) f(x, y) 6/19/2021 74
Sharpening Spatial Filters: Laplace Operator 6/19/2021 75
Sharpening Spatial Filters: Laplace Operator Image sharpening in the way of using the Laplacian: 6/19/2021 76
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Unsharp Masking and Highboost Filtering ► Unsharp masking Sharpen images consists of subtracting an unsharp (smoothed) version of an image from the original image e. g. , printing and publishing industry ► Steps 1. Blur the original image 2. Subtract the blurred image from the original 3. Add the mask to the original 6/19/2021 78
Unsharp Masking and Highboost Filtering 6/19/2021 79
Unsharp Masking: Demo 6/19/2021 80
Unsharp Masking and Highboost Filtering: Example 6/19/2021 81
Image Sharpening based on First-Order Derivatives Gradient Image 6/19/2021 82
Image Sharpening based on First-Order Derivatives z 1 z 2 z 3 z 4 z 5 z 6 z 7 z 8 z 9 6/19/2021 83
Image Sharpening based on First-Order Derivatives z 1 z 2 z 3 z 4 z 5 z 6 z 7 z 8 z 9 6/19/2021 84
Image Sharpening based on First-Order Derivatives 6/19/2021 85
Example 6/19/2021 86
Example: Combining Spatial Enhancement Methods Goal: Enhance the image by sharpening it and by bringing out more of the skeletal detail 6/19/2021 87
Example: Combining Spatial Enhancement Methods Goal: Enhance the image by sharpening it and by bringing out more of the skeletal detail 6/19/2021 88
- Intensity transformation and spatial filtering
- Intensity transform functions
- Intensity transformation and spatial filtering
- Intensity transformations and spatial filtering
- Doterra taiwan
- Intensity transformations and spatial filtering
- Ingress filtering vs egress filtering
- Abbe imaging experiment
- Digital image processing
- Contra harmonic filter
- Bahadir gunturk
- Spatial filtering
- Spatial filtering
- Combining spatial enhancement methods
- Restoration in the presence of noise only-spatial filtering
- Spatial filtering matlab
- Power law transform
- Full colour image processing
- Intensity transformation in digital image processing
- 01:640:244 lecture notes - lecture 15: plat, idah, farad
- Spatial data vs non spatial data
- Spatial data transformation
- Collaborative filtering pros and cons
- Define stateful packet inspection
- Risk ranking and filtering
- Lanczos
- Microsoft windows filtering platform hyper-v
- Collaborative filtering medium
- Knapp's theory of relationship development
- Stateless packet filtering
- Screened host firewall
- Selim aksoy
- Dft performs filtering operation in
- Fwps_callout
- Explain the constrained least square filtering.
- Competitive filtering
- Matched filtering gravitational waves
- Linear filtering citra
- Filtration
- Filtering mode
- Perceptron-based prefetch filtering
- Band pass filtering in biomedical instrumentation
- Content filtering trusts
- Recursive bilateral filtering
- Optical packet switching
- Linear filtering
- Association rules vs collaborative filtering
- Post filtering in computer graphics
- Ofsted web filtering
- Becta web filtering
- Caobin
- Firewall adalah
- Image filtering
- Homomorphic filtering block diagram
- Yehuda koren
- Neural collaborative filtering
- Particle filtering
- Filtering self-rescue respirator
- Frequency filtering
- Packet filtering firewall
- Frequency filtering
- Carbon filtering
- Frequency filtering
- Filtering organizational behavior
- Point processing operations
- Collaborative filtering with temporal dynamics
- Weighted area sampling
- Bwhitmiss
- Adverbs of frequency meaning
- Sound intensity and resonance
- Poynting vector and intensity
- Intensity of pressure
- Poynting vector and intensity
- Poynting vector and intensity
- Rotational spectral lines
- Speed and agility frequency, intensity, time type
- Hue value intensity
- Hsi color wheel
- Equation for electric field intensity
- Clouds fog or dew will always form when
- Supports intensity scale rating key
- Lambert beer law
- Radiation intensity of antenna
- Earthquake intensity depends primarily on the height of
- Intensity ratio formula
- A literary work in which special intensity
- A literary work in which special intensity is
- Low intensity vibration device
- Light intensity equation with wavelength