This document discusses various techniques for image enhancement in the spatial domain, including histogram modification, averaging filters, and median filters. Histogram modification techniques like stretching, shrinking, and equalization can increase contrast and enhance an image. Averaging multiple noisy images together reduces noise by decreasing pixel variability. Median filters replace each pixel value with the median value in its neighborhood, which effectively removes salt-and-pepper noise while preserving edges. The document provides examples and equations to demonstrate these different spatial domain enhancement methods.
This document discusses techniques for digital image enhancement through histogram modification, including histogram stretching, shrinking, sliding, equalization, and specification. Histogram modification performs gray level mapping to modify image contrast by considering a histogram's shape and spread. Histogram stretching increases contrast by mapping values across the full range, while shrinking decreases contrast by compressing values. Sliding makes images lighter or darker by adding or subtracting from values. Equalization makes histograms as flat as possible to improve contrast, and specification allows interactively defining a target histogram to remap an image's values. These techniques are useful for improving low-contrast or unbalanced images.
An image histogram represents the distribution of pixel intensities in a digital image. It plots the number of pixels for each tonal value. Histograms can reveal if an image is under-exposed or over-exposed based on where most pixel values are concentrated. Histogram equalization improves contrast by spreading out pixel values across intensity levels. Local histogram equalization applies this within neighborhoods to enhance detail while preserving edges.
An image histogram represents the distribution of pixel intensities in a digital image. It plots the number of pixels for each tonal value. Histograms can reveal if an image is under-exposed or over-exposed based on where most pixel values are concentrated. Histogram equalization improves contrast by spreading out pixel values across intensity levels. Local histogram equalization applies this within neighborhoods to enhance detail while preserving edges.
This document discusses image processing and histograms. It covers topics like image restoration, enhancement, and compression. It also discusses representing digital images with matrices and defines spatial and brightness resolution. Finally, it covers image histograms in depth, including defining histograms, properties, types, applications like thresholding and enhancement, and modifications like stretching, shrinking, and sliding histograms. As an example, it shows a histogram for a hypothetical 128x128 pixel image with 8 gray levels.
This document discusses various techniques for enhancing images in the spatial domain, which involves direct manipulation of pixel values. It describes point processing techniques like gray-level transformations that map input pixel values to output values using functions like negative, logarithm, power-law, and piecewise linear. Histogram processing techniques are also covered, including histogram equalization, which spreads out the most frequent intensity values in an image. The document provides examples to illustrate the effect of these different enhancement methods.
This document discusses various techniques for enhancing images in the spatial domain, which involves direct manipulation of pixel values. It describes point processing techniques like gray-level transformations that map input pixel values to output values using functions like negative, logarithm, power-law, and piecewise linear. Histogram processing techniques are also covered, including histogram equalization, which spreads out the most frequent intensity values in an image. The document provides examples to illustrate the effect of these different enhancement methods.
Comparison of Histogram Equalization Techniques for Image Enhancement of Gray...IJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.
This document discusses techniques for digital image enhancement through histogram modification, including histogram stretching, shrinking, sliding, equalization, and specification. Histogram modification performs gray level mapping to modify image contrast by considering a histogram's shape and spread. Histogram stretching increases contrast by mapping values across the full range, while shrinking decreases contrast by compressing values. Sliding makes images lighter or darker by adding or subtracting from values. Equalization makes histograms as flat as possible to improve contrast, and specification allows interactively defining a target histogram to remap an image's values. These techniques are useful for improving low-contrast or unbalanced images.
An image histogram represents the distribution of pixel intensities in a digital image. It plots the number of pixels for each tonal value. Histograms can reveal if an image is under-exposed or over-exposed based on where most pixel values are concentrated. Histogram equalization improves contrast by spreading out pixel values across intensity levels. Local histogram equalization applies this within neighborhoods to enhance detail while preserving edges.
An image histogram represents the distribution of pixel intensities in a digital image. It plots the number of pixels for each tonal value. Histograms can reveal if an image is under-exposed or over-exposed based on where most pixel values are concentrated. Histogram equalization improves contrast by spreading out pixel values across intensity levels. Local histogram equalization applies this within neighborhoods to enhance detail while preserving edges.
This document discusses image processing and histograms. It covers topics like image restoration, enhancement, and compression. It also discusses representing digital images with matrices and defines spatial and brightness resolution. Finally, it covers image histograms in depth, including defining histograms, properties, types, applications like thresholding and enhancement, and modifications like stretching, shrinking, and sliding histograms. As an example, it shows a histogram for a hypothetical 128x128 pixel image with 8 gray levels.
This document discusses various techniques for enhancing images in the spatial domain, which involves direct manipulation of pixel values. It describes point processing techniques like gray-level transformations that map input pixel values to output values using functions like negative, logarithm, power-law, and piecewise linear. Histogram processing techniques are also covered, including histogram equalization, which spreads out the most frequent intensity values in an image. The document provides examples to illustrate the effect of these different enhancement methods.
This document discusses various techniques for enhancing images in the spatial domain, which involves direct manipulation of pixel values. It describes point processing techniques like gray-level transformations that map input pixel values to output values using functions like negative, logarithm, power-law, and piecewise linear. Histogram processing techniques are also covered, including histogram equalization, which spreads out the most frequent intensity values in an image. The document provides examples to illustrate the effect of these different enhancement methods.
Comparison of Histogram Equalization Techniques for Image Enhancement of Gray...IJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.
Linear contrast stretching uniformly expands the intensity values in an image to utilize the full range of available intensities. Histogram equalization spreads out intensity values to improve contrast by effectively stretching the most frequent values. Piecewise linear stretching uses different linear functions to enhance different intensity ranges differently. Logarithmic stretching compresses higher intensity values while expanding lower ones, enhancing dark areas. Contrast stretching techniques aim to improve poor contrast by modifying intensity distributions, but can increase noise and lose original brightness levels.
Here in the ppt a detailed description of Image Enhancement Techniques is given which includes topics like Basic Gray level Transformations,Histogram Processing.
Enhancement using Arithmetic/Logic Operations.
image averaging and image averaging methods.
Piecewise-Linear Transformation Functions
Setting the lower order bit plane to zero would have the effect of reducing the number of distinct gray levels by half. This would cause the histogram to become more peaked, with more pixels concentrated in fewer bins.
This document describes a project to develop an image printing program based on halftoning. Halftoning approximates grayscale images using patterns of black and white dots. The program implements a simple halftoning scheme with 10 shades of gray represented by 3x3 dot patterns. It reduces image resolution significantly. Testing showed the halftoned images have very low quality due to the coarse approximation and reduced resolution. More advanced halftoning methods are needed to produce higher quality halftoned images.
This document discusses various techniques for image enhancement in spatial domain. It defines image enhancement as improving visual quality or converting images for better analysis. Key techniques covered include noise removal, contrast adjustment, intensity adjustment, histogram equalization, thresholding, gray level slicing, and image rotation. Conversion methods like grayscale and different file formats are also summarized. Experimental results and applications in fields like medicine, astronomy, and security are mentioned.
This document discusses image enhancement techniques in digital image processing. It defines image enhancement as modifying image attributes to make an image more suitable for a given task. The main techniques discussed are spatial domain enhancement methods like noise removal, contrast adjustment, and histogram equalization. Examples are provided to demonstrate the effects of these enhancement methods on images.
This document provides an overview of various image enhancement techniques. It begins with an introduction to image enhancement and its objectives. It then outlines and describes several categories of enhancement methods, including spatial-frequency domain methods, point operations, histogram operations, spatial operations, and transform operations. Specific techniques discussed in detail include contrast stretching, clipping, thresholding, median filtering, unsharp masking, and principal component analysis for multispectral images. The document also covers color image enhancement and techniques for pseudocoloring.
The document discusses various techniques for digital image intensity transformations and histogram processing. It begins with an overview of intensity transformations versus geometric transformations. It then covers log transformations, power-law transformations, and piecewise linear transformations in detail. The document also discusses histogram equalization in depth, including its purpose, principles, and specific operations. Additionally, it compares histogram equalization to other enhancement methods like linear stretch and presents examples of when histogram equalization may fail. Finally, the document introduces fundamentals of spatial filtering, including linear spatial filtering operations using different sized box kernels.
This presentation describes briefly about the image enhancement in spatial domain, basic gray level transformation, histogram processing, enhancement using arithmetic/ logical operation, basics of spatial filtering and local enhancements.
COM2304: Intensity Transformation and Spatial Filtering – I (Intensity Transf...Hemantha Kulathilake
At the end of this lesson, you should be able to;
describe spatial domain of the digital image.
recognize the image enhancement techniques.
describe and apply the concept of intensity transformation.
express histograms and histogram processing.
describe image noise.
characterize the types of Noise.
describe concept of image restoration.
The document discusses digital image representation and processing. It covers:
1) How digital images are represented as 2D arrays of integer pixel values stored in computer memory.
2) The main types of digital images - binary, grayscale, and true color images - based on the number of possible values per pixel.
3) Common image processing techniques like segmentation, thresholding, and histograms that analyze and modify digital images.
4) Thresholding converts pixels to black/white based on a threshold and is often used in segmentation. Histograms show pixel value distributions to aid analysis.
Contrast enhancement using various statistical operations and neighborhood pr...sipij
This document proposes a novel contrast enhancement algorithm using various statistical operations and neighborhood processing. It begins with an overview of histogram equalization and some of its limitations. It then discusses related work on other histogram equalization techniques including classical histogram equalization, brightness preserving bi-histogram equalization, recursive mean separate histogram equalization, and background brightness preserving histogram equalization. The proposed method is then described, which applies statistical operations like mean and standard deviation within a neighborhood to locally enhance pixels. Pixels are replaced from an initially equalized image if their difference from the local mean exceeds a threshold. This aims to preserve local brightness features. Finally, metrics for evaluating image quality like PSNR, SSIM, and CNR are defined to analyze results
Study on Contrast Enhancement with the help of Associate Regions Histogram Eq...IJSRD
Histogram equalization is an uncomplicated and extensively used image distinction enhancement technique. The crucial drawback of histogram equalization is it transforms the brightness of the image. To overcome this drawback, different histogram Equalization methods have been projected. These methods protect the brightness on the result image but, do not have a usual look. Therefore this paper is an attempt to bridge the gap and results after the processed Associate regions are collected into one image. The mock-up result explains that the algorithm can not only improve image information successfully but also remain the imaginative image luminance well enough to make it likely to be used in video arrangement directly.
Intensity Transformation and Spatial filteringShajun Nisha
Dr. S. Shajun Nisha discusses intensity transformation and spatial filtering techniques in image processing. Intensity transformation functions modify pixel intensities based on a transformation function. Spatial filtering involves applying an operator over a neighborhood of pixels. Common intensity transformations include contrast stretching and logarithmic transforms. Histogram equalization is also described to improve contrast. Spatial filters include linear filters implemented using imfilter and non-linear filters like median filtering with ordfilt2 and medfilt2. Examples demonstrate applying these techniques to enhance images.
The Effectiveness and Efficiency of Medical Images after Special Filtration f...Editor IJCATR
There are many factors which have influences on the quality of medical images, so this paper gives a brief narration on the important techniques that produce acceptable quality to medical images. To ensure the validity of this techniques towards medical images, a questionnaire was designed and distributed to a number of doctors and professionals. The survey aims to assess the medical image specialists by regarding their point of views towards the impact of filtering medical images after processing using these techniques. MatLab package used to apply the techniques.
Multimedia content based retrieval in digital librariesMazin Alwaaly
This document provides an overview of content-based image retrieval (CBIR) systems. It discusses early CBIR systems and provides a case study of C-BIRD, a CBIR system that uses features like color histograms, color layout, texture analysis, and object models to perform image searches. It also covers quantifying search results, key technologies in current CBIR systems such as robust image features, relevance feedback, and visual concept search, and the role of users in interactive CBIR systems.
SpatialEnhancement of course CE7491 of NTUlyumingzhi
This document summarizes key concepts in image intensity transformations and filtering. It discusses two classes of spatial domain processing: point processing and spatial filtering. Point processing involves transformations that modify pixel intensities without regard to neighboring pixels, such as contrast stretching and histogram equalization. Spatial filtering computes new pixel values based on neighboring pixels using techniques like smoothing and sharpening filters. Specific filters covered include averaging, Gaussian, Laplacian, and median filters.
This document discusses various techniques for image enhancement, including point operations, mask operations, transform operations, and coloring operations. It provides details on techniques such as contrast stretching, histogram equalization, and histogram specification. Histogram equalization aims to produce an output image with a uniform histogram, while histogram specification allows specifying a desired output histogram. Both techniques involve transforming the input image using the cumulative distribution function of the input pixel values.
Fast Segmentation of Sub-cellular OrganellesCSCJournals
Segmentation and counting sub-cellular structure is a very challenging problem even for medical experts. A fast and efficient method for segmentation and counting of sub-cellular structure is proposed. The proposed method uses a hybrid combination of several image processing techniques and is effective in segmenting the sub-cellular structures in a fast and effective manner.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Linear contrast stretching uniformly expands the intensity values in an image to utilize the full range of available intensities. Histogram equalization spreads out intensity values to improve contrast by effectively stretching the most frequent values. Piecewise linear stretching uses different linear functions to enhance different intensity ranges differently. Logarithmic stretching compresses higher intensity values while expanding lower ones, enhancing dark areas. Contrast stretching techniques aim to improve poor contrast by modifying intensity distributions, but can increase noise and lose original brightness levels.
Here in the ppt a detailed description of Image Enhancement Techniques is given which includes topics like Basic Gray level Transformations,Histogram Processing.
Enhancement using Arithmetic/Logic Operations.
image averaging and image averaging methods.
Piecewise-Linear Transformation Functions
Setting the lower order bit plane to zero would have the effect of reducing the number of distinct gray levels by half. This would cause the histogram to become more peaked, with more pixels concentrated in fewer bins.
This document describes a project to develop an image printing program based on halftoning. Halftoning approximates grayscale images using patterns of black and white dots. The program implements a simple halftoning scheme with 10 shades of gray represented by 3x3 dot patterns. It reduces image resolution significantly. Testing showed the halftoned images have very low quality due to the coarse approximation and reduced resolution. More advanced halftoning methods are needed to produce higher quality halftoned images.
This document discusses various techniques for image enhancement in spatial domain. It defines image enhancement as improving visual quality or converting images for better analysis. Key techniques covered include noise removal, contrast adjustment, intensity adjustment, histogram equalization, thresholding, gray level slicing, and image rotation. Conversion methods like grayscale and different file formats are also summarized. Experimental results and applications in fields like medicine, astronomy, and security are mentioned.
This document discusses image enhancement techniques in digital image processing. It defines image enhancement as modifying image attributes to make an image more suitable for a given task. The main techniques discussed are spatial domain enhancement methods like noise removal, contrast adjustment, and histogram equalization. Examples are provided to demonstrate the effects of these enhancement methods on images.
This document provides an overview of various image enhancement techniques. It begins with an introduction to image enhancement and its objectives. It then outlines and describes several categories of enhancement methods, including spatial-frequency domain methods, point operations, histogram operations, spatial operations, and transform operations. Specific techniques discussed in detail include contrast stretching, clipping, thresholding, median filtering, unsharp masking, and principal component analysis for multispectral images. The document also covers color image enhancement and techniques for pseudocoloring.
The document discusses various techniques for digital image intensity transformations and histogram processing. It begins with an overview of intensity transformations versus geometric transformations. It then covers log transformations, power-law transformations, and piecewise linear transformations in detail. The document also discusses histogram equalization in depth, including its purpose, principles, and specific operations. Additionally, it compares histogram equalization to other enhancement methods like linear stretch and presents examples of when histogram equalization may fail. Finally, the document introduces fundamentals of spatial filtering, including linear spatial filtering operations using different sized box kernels.
This presentation describes briefly about the image enhancement in spatial domain, basic gray level transformation, histogram processing, enhancement using arithmetic/ logical operation, basics of spatial filtering and local enhancements.
COM2304: Intensity Transformation and Spatial Filtering – I (Intensity Transf...Hemantha Kulathilake
At the end of this lesson, you should be able to;
describe spatial domain of the digital image.
recognize the image enhancement techniques.
describe and apply the concept of intensity transformation.
express histograms and histogram processing.
describe image noise.
characterize the types of Noise.
describe concept of image restoration.
The document discusses digital image representation and processing. It covers:
1) How digital images are represented as 2D arrays of integer pixel values stored in computer memory.
2) The main types of digital images - binary, grayscale, and true color images - based on the number of possible values per pixel.
3) Common image processing techniques like segmentation, thresholding, and histograms that analyze and modify digital images.
4) Thresholding converts pixels to black/white based on a threshold and is often used in segmentation. Histograms show pixel value distributions to aid analysis.
Contrast enhancement using various statistical operations and neighborhood pr...sipij
This document proposes a novel contrast enhancement algorithm using various statistical operations and neighborhood processing. It begins with an overview of histogram equalization and some of its limitations. It then discusses related work on other histogram equalization techniques including classical histogram equalization, brightness preserving bi-histogram equalization, recursive mean separate histogram equalization, and background brightness preserving histogram equalization. The proposed method is then described, which applies statistical operations like mean and standard deviation within a neighborhood to locally enhance pixels. Pixels are replaced from an initially equalized image if their difference from the local mean exceeds a threshold. This aims to preserve local brightness features. Finally, metrics for evaluating image quality like PSNR, SSIM, and CNR are defined to analyze results
Study on Contrast Enhancement with the help of Associate Regions Histogram Eq...IJSRD
Histogram equalization is an uncomplicated and extensively used image distinction enhancement technique. The crucial drawback of histogram equalization is it transforms the brightness of the image. To overcome this drawback, different histogram Equalization methods have been projected. These methods protect the brightness on the result image but, do not have a usual look. Therefore this paper is an attempt to bridge the gap and results after the processed Associate regions are collected into one image. The mock-up result explains that the algorithm can not only improve image information successfully but also remain the imaginative image luminance well enough to make it likely to be used in video arrangement directly.
Intensity Transformation and Spatial filteringShajun Nisha
Dr. S. Shajun Nisha discusses intensity transformation and spatial filtering techniques in image processing. Intensity transformation functions modify pixel intensities based on a transformation function. Spatial filtering involves applying an operator over a neighborhood of pixels. Common intensity transformations include contrast stretching and logarithmic transforms. Histogram equalization is also described to improve contrast. Spatial filters include linear filters implemented using imfilter and non-linear filters like median filtering with ordfilt2 and medfilt2. Examples demonstrate applying these techniques to enhance images.
The Effectiveness and Efficiency of Medical Images after Special Filtration f...Editor IJCATR
There are many factors which have influences on the quality of medical images, so this paper gives a brief narration on the important techniques that produce acceptable quality to medical images. To ensure the validity of this techniques towards medical images, a questionnaire was designed and distributed to a number of doctors and professionals. The survey aims to assess the medical image specialists by regarding their point of views towards the impact of filtering medical images after processing using these techniques. MatLab package used to apply the techniques.
Multimedia content based retrieval in digital librariesMazin Alwaaly
This document provides an overview of content-based image retrieval (CBIR) systems. It discusses early CBIR systems and provides a case study of C-BIRD, a CBIR system that uses features like color histograms, color layout, texture analysis, and object models to perform image searches. It also covers quantifying search results, key technologies in current CBIR systems such as robust image features, relevance feedback, and visual concept search, and the role of users in interactive CBIR systems.
SpatialEnhancement of course CE7491 of NTUlyumingzhi
This document summarizes key concepts in image intensity transformations and filtering. It discusses two classes of spatial domain processing: point processing and spatial filtering. Point processing involves transformations that modify pixel intensities without regard to neighboring pixels, such as contrast stretching and histogram equalization. Spatial filtering computes new pixel values based on neighboring pixels using techniques like smoothing and sharpening filters. Specific filters covered include averaging, Gaussian, Laplacian, and median filters.
This document discusses various techniques for image enhancement, including point operations, mask operations, transform operations, and coloring operations. It provides details on techniques such as contrast stretching, histogram equalization, and histogram specification. Histogram equalization aims to produce an output image with a uniform histogram, while histogram specification allows specifying a desired output histogram. Both techniques involve transforming the input image using the cumulative distribution function of the input pixel values.
Fast Segmentation of Sub-cellular OrganellesCSCJournals
Segmentation and counting sub-cellular structure is a very challenging problem even for medical experts. A fast and efficient method for segmentation and counting of sub-cellular structure is proposed. The proposed method uses a hybrid combination of several image processing techniques and is effective in segmenting the sub-cellular structures in a fast and effective manner.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
3. Histogram Modification
Histogram modification performs a function
similar to gray level mapping, but works by
considering histogram’s shape and spread
Gray level histogram of an image is the
distribution of the gray levels in an image
Examination of the histogram is one of the most
useful tools for image enhancement, as it makes
easy to see the modifications that may improve
an image
4. The histogram can be modified by a mapping
function, which will stretch, shrink (compress),
or slide the histogram
Histogram stretching and histogram shrinking
are forms of gray scale modification, sometimes
referred to as histogram scaling
5.
6. Histogram stretch
• The mapping function equation is as follows:
where: I(r,c)MAX is the largest gray level value in
the image I(r,c), I(r,c)MIN is the smallest gray
level value in I(r,c) and
MAX and MIN correspond to the maximum and
minimum gray level values possible (for an 8-bit
image these are 0 and 255)
7. • This equation will take an image and stretch the
histogram across the entire gray level range,
which has the effect of increasing the contrast of
a low contrast image
• If most of the pixel values in an image fall within
a small range, it is useful to allow a small
percentage of the pixel values to be clipped at
the low and high end of the range (for an 8-bit
image this means truncating at 0 and 255)
8. a) Low-contrast image
c) Image (a) after histogram stretch
b) Histogram of image (a)
d) Histogram of image after stretch
Histogram Stretching
9. a) Original image b) Histogram of original image c) Image after histogram stretching
with out clipping
d) Histogram of image (c) e) Image after histogram stretching with
clipping 1% of the values at the high
and low ends
f) Histogram of image (e)
Histogram Stretching with Clipping
10. Histogram shrink
The mapping function equation is as follows:
where I(r,c)MAX is the largest gray level value in
the image I(r,c), I(r,c)MIN is the smallest gray
level value in I(r,c) and
ShrinkMAX and ShrinkMIN correspond to the
maximum and minimum desired in the
compressed histogram
11. • Decreases image contrast by compressing the
gray levels
• However this method may not be useful as an
image enhancement tool, but it is used in an
image sharpening algorithm (unsharp masking) as
a part of an enhancement technique
12. a) Original image b) Histogram of image
c) Image after shrinking the histogram
to the range [75,175]
d) Histogram of image (c)
Histogram Shrinking
13. Histogram slide
• Used to make an image either darker or lighter,
but retain the relationship between gray level
values
• Accomplished by simply adding or subtracting a
fixed number from all of the gray level values, as
follows:
where the OFFSET value is the amount to slide
the histogram
14. • In this equation we assume that any values slid
past the minimum and maximum values will be
clipped to the respective minimum or maximum
• A positive OFFSET value will increase the
overall brightness, while a negative OFFSET will
create a darker image
15. a) Resultant image from sliding the
histogram down by 50
b) Histogram of image (a)
c) Resultant image from sliding the
histogram up by 50
d) Histogram of image (c)
Histogram Slide
16. Histogram equalization
• A technique where the histogram of the resultant
image is as flat as possible
• The theoretical basis for histogram equalization
involves probability theory, where we treat the
histogram as the probability distribution of the
gray levels
• Its function is similar to that of a histogram
stretch but often provides more visually pleasing
results across a wider range of images
17. • Consists of four steps:
1. Find the running sum of the histogram
values
2. Normalize the values from step (1) by
dividing by the total number of pixels
3. Multiply the values from step (2) by the
maximum gray level value and round
4. Map the gray level values to the results
from step (3) using a one-to-one
correspondence
18. Example:
3-bits per pixel image – range is 0 to 7.
Given the following histogram:
Number of Pixels
Gray Level Value (Histogram values)
0 10
1 8
2 9
3 2
4 14
5 1
6 5
7 2
19. 1) Create a running sum of the histogram values.
This means the first value is 10, the second is
10+8=18, next 10+8+9=27, and so on. Here we
get 10, 18, 27, 29, 43, 44, 49, 51
2) Normalize by dividing by the total number of
pixels. The total number of pixels is:
10+8+9+2+14+1+5+0 = 51 (note this is the last
number from step 1), so we get: 10/51, 18/51,
27/51, 29/51, 43/51, 44/51, 49/51, 51/51
3) Multiply these values by the maximum gray
level values, in this case 7, and then round the
result to the closest integer. After this is done we
obtain: 1, 2, 4, 4, 6, 6, 7, 7
20. 4) Map the original values to the results from step
3 by a one-to-one correspondence. This is done
as follows:
Original Gray Histogram
Level Value Equalized Values
0 1
1 2
2 4
3 4
4 6
5 6
6 7
7 7
21. All pixels in the original image with gray level 0
are set to 1, values of 1 are set to 2, 2 set to 4, 3
set to 4, and so on. After the histogram
equalization values are calculated and can be
implemented efficiently with a look-up-table
(LUT), as discussed in Chapter 2
We can see the original histogram and the
resulting histogram equalized histogram in Fig.
8.2.14. Although the result is not flat, it is closer
to being flat than the original histogram
24. Input image Resultant image after histogram equalization
Histogram Equalization Examples (contd)
2.
Note: As can be seen histogram equalization provides similar results
regardless of the input image
25. • Histogram equalization of a digital image will not
typically provide a histogram that is perfectly flat,
but it will make it as flat as possible
• Histogram equalization may not always provide
the desired effect, since its goal is fixed – to
distribute the gray level values as evenly as
possible. To allow for interactive histogram
manipulation, the ability to specify the histogram
is necessary
26. Histogram specification
• Process of defining a histogram and modifying
the histogram of the original image to match the
histogram as specified
• Key concept is to picture the original image
being histogram equalized, and the specified
histogram being histogram equalized
28. • Histogram specification consists of following 5
steps:
1. Specify the desired histogram
2. Find the mapping table to histogram
equalize the image, Mapping Table 1,
3. Find the mapping table to histogram
equalize the values of the specified
histogram, Mapping Table 2
29. • Histogram specification steps (continued)
4. Use mapping Tables 1 & 2 to find the
mapping table to map the original values
to the histogram equalized values and
then to the specified histogram values
5. Use the table from step (4) to map the
original values to the specified histogram
values
30. EXAMPLE:
1) Specify the desired histogram:
Number of pixels
Gray Level Value in desired histogram
0 1
1 5
2 10
3 15
4 20
5 0
6 0
7 0
31. 2) For this we will use the image and mapping table from
the previous example, where the histogram equalization
mapping table (Mapping Table 1) is given by:
Original Gray Level Value Histogram Equalized
level values-OS equalized values-HS
0 1
1 2
2 4
3 4
4 6
5 6
6 7
7 7
33. 4) Use Mapping Tables 1 and 2 to find the final mapping
table by mapping the values first to the histogram
equalized values and then to the specified histogram
values. (Mapping Table 2, columns switched to match Fig. 8.2.16 – slide 57)
Mapping Table 1 Mapping Table 2
O H HS OS M
0 1 0 0 1
1 2 1 1 2
2 4 2 2 3
3 4 4 3 3
4 6 7 4 4
5 6 7 5 4
6 7 7 6 4
7 7 7 7 4
34. 5) Use the table from STEP 4 to perform the histogram
specification mapping. For this all we need are columns
O (or OS) and M:
O M
0 1
1 2
2 3
3 3
4 4
5 4
6 4
7 4
Now, all the 0’s get mapped to 1’s, the 1’s to 2’s, the 3’s
to 3’s and so on
35. • In practice, the desired histogram is often
specified by a continuous (possibly non-linear)
function, for example a sine or a log function.
• To obtain the numbers for the specified
histogram the function is sampled, the values
are normalized to 1, and then multiplied by the
total number of pixels in the image
40. Image Averaging
• A noisy image:
)
,
(
)
,
(
)
,
( y
x
n
y
x
f
y
x
g
• Averaging K different noisy images:
M
i
i y
x
g
K
y
x
g
1
)
,
(
1
)
,
(
41. Image Averaging
• As K increases, the variability of the pixel
values at each location decreases.
– This means that g(x,y) approaches f(x,y) as the
number of noisy images used in the averaging
process increases.
• Registering(aligned) of the images is
necessary to avoid blurring in the output
image.
46. Smoothing Filters
• Median filtering (nonlinear)
– Used primarily for noise reduction (eliminates
isolated spikes)
– The gray level of each pixel is replaced by the
median of the gray levels in the neighborhood of
that pixel (instead of by the average as before).
48. Median Filter
a) Image with added salt-and-pepper noise,
the probability for salt = probability
for pepper = 0.10
b) After median filtering with a 3x3
window,all the noise is not removed
49. Median Filter
c) After median filtering with a 5x5 window, all the noise is
removed, but the image is blurry acquiring the “painted”
effect
50. • The contra-harmonic mean filter works well for
images containing salt OR pepper type noise,
depending on the filter order, R:
• For negative values of R, it eliminates salt-type
noise, while for positive values of R, it eliminates
pepper-type noise
51.
52. • The geometric mean filter works best with
Gaussian noise, and retains detail information
better than an arithmetic mean filter
• It is defined as the product of the pixel values
within the window, raised to the 1/(N*N) power:
53.
54. • The harmonic mean filter fails with pepper
noise, but works well for salt noise
• It is defined as follows:
• This filter also works with Gaussian noise,
retaining detail information better than the
arithmetic mean filter
55.
56. • The Yp mean filter is defined as follows:
• This filter removes salt noise for negative values
of P, and pepper noise for positive values of P
59. • It simultaneously normalizes the brightness
across an image and increases contrast.
• Filtering is used to remove multiplicative noise
• Illumination and reflectance are not separable
• Illumination and reflectance combine
multiplicatively
• Components are made additive by taking the
logarithm of the image intensity
60. • Multiplicative components of the image can be separated linearly
in the frequency domain
• To make the illumination of an image more even, the high-
frequency components are increased and low-frequency
components are decreased
• High-frequency components are assumed to represent mostly the
reflectance in the scene (the amount of light reflected off the
object in the scene)
• Low-frequency components are assumed to represent mostly the
illumination in the scene
• High-pass filtering is used to suppress low frequencies and amplify
high frequencies, in the log-intensity domain
61. • The illumination component tends to vary slowly across
the image.
• The reflectance tends to vary rapidly, particularly at
junctions of dissimilar objects.
• Therefore, by applying a frequency domain filter of the
form we can reduce intensity variation across the
image while highlighting detail.
66. Enhancement of Color
Images
• Gray scale transforms and histogram
modification techniques can be applied by
treating a color image as three gray images
• Care must be taken in how this is done to
avoid color shifts
67. Histogram modification can be performed on
color images, but doing it on each color band
separately can create relative color changes
The relative color can be retained by applying
the gray scale modification technique to one of
the color bands, and then using the ratios from
the original image to find the other values
68. Histogram Equalization of Color Images
a) Original poor contrast image b) Histogram equalization based on
the red color band
69. Histogram Equalization of Color Images (contd)
c) Histogram equalization based on
the green color band
d) Histogram equalization based on
the blue color band
Note: In this case the red band gives the best results
This will depend on the image and the desired result
70. Typically the most important color band is
selected, and this choice is very much
application-specific and will not always provide
us with the desired result
Often, we really want to apply the gray scale
modification method to the image brightness
only, even with color images
71. Histogram modification on color images can be
performed in the following ways:
• Retain the RGB ratios and perform the
modification on one band only, then use the
ratios to get the other two bands’ values, or
• Perform a color transform, such as HSL, do the
modification on the lightness (brightness band),
then do the inverse color transform