At the end of this lecture, you should be able to;
describe the fundamentals of spatial filtering.
generating spatial filter masks.
identify smoothing via linear filters and non linear filters.
apply smoothing techniques for problem solving.
Image Enhancement: Introduction to Spatial Filters, Low Pass Filter and High Pass Filters. Here Discussed Image Smoothing and Image Sharping, Gaussian Filters
Digital Image Processing denotes the process of digital images with the use of digital computer. Digital images are contains various types of noises which are reduces the quality of images. Noises can be removed by various enhancement techniques. Image smoothing is a key technology of image enhancement, which can remove noise in images.
Image Enhancement: Introduction to Spatial Filters, Low Pass Filter and High Pass Filters. Here Discussed Image Smoothing and Image Sharping, Gaussian Filters
Digital Image Processing denotes the process of digital images with the use of digital computer. Digital images are contains various types of noises which are reduces the quality of images. Noises can be removed by various enhancement techniques. Image smoothing is a key technology of image enhancement, which can remove noise in images.
its very useful for students.
Sharpening process in spatial domain
Direct Manipulation of image Pixels.
The objective of Sharpening is to highlight transitions in intensity
The image blurring is accomplished by pixel averaging in a neighborhood.
Since averaging is analogous to integration.
Prepared by
M. Sahaya Pretha
Department of Computer Science and Engineering,
MS University, Tirunelveli Dist, Tamilnadu.
its very useful for students.
Sharpening process in spatial domain
Direct Manipulation of image Pixels.
The objective of Sharpening is to highlight transitions in intensity
The image blurring is accomplished by pixel averaging in a neighborhood.
Since averaging is analogous to integration.
Prepared by
M. Sahaya Pretha
Department of Computer Science and Engineering,
MS University, Tirunelveli Dist, Tamilnadu.
Digital image processing is the use of computer algorithms to perform image processing on digital images. As a subcategory or field of digital signal processing, digital image processing has many advantages over analog image processing.
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.
This slides about brief Introduction to Image Restoration Techniques. How to estimate the degradation function, noise models and its probability density functions.
COM2304: Intensity Transformation and Spatial Filtering – III Spatial Filters...Hemantha Kulathilake
At the end of this lecture, you should be able to;
describe sharpening through spatial filters.
Identify usage of derivatives in Image Processing.
discuss edge detection techniques.
compare 1st & 2nd order derivatives used for sharpening.
Apply sharpening techniques for problem solving.
At the end of this lesson, you should be able to;
define segmentation.
Describe edge based in segmentation.
describe thresholding and its properties.
apply edge detection and thresholding as segmentation techniques.
At the end of this lecture, you should be able to;
describe the importance of morphological features in an image.
describe the operation of erosion, dilation, open and close operations.
identify the practical advantage of the morphological operations.
apply morphological operations for problem solving.
At the end of this lesson, you should be able to;
describe spatial resolution
describe intensity resolution
identify the effect of aliasing
describe image interpolation
describe relationships among the pixels
At the end of this lesson, you should be able to;
describe Connected Components and Contours in image segmentation.
discuss region based segmentation method.
discuss Region Growing segmentation technique.
discuss Morphological Watersheds segmentation.
discuss Model Based Segmentation.
discuss Motion Segmentation.
implement connected components, flood fill, watershed, template matching and frame difference techniques.
formulate possible mechanisms to propose segmentation methods to solve problems.
COM2304: Introduction to Computer Vision & Image Processing Hemantha Kulathilake
At the end of this lesson, you should be able to;
Describe image.
Describe digital image processing and computer vision.
Compare and Contrast image processing and computer vision.
Describe image sources.
Identify the array of imaging application under the EM Image source.
Describe the components of Image processing system and computer vision system.
When Discrete Optimization Meets Multimedia Security (and Beyond)Shujun Li
Invited talk at the FoT-RSS: Faculty of Technology Research Seminar Series, De Montfort University, UK, co-sponsored by the IEEE UK & Ireland Signal Processing Chapter, 25 May 2016
Abstract:
Selective encryption has been widely used for image and video encryption due to many practical reasons such as to achieve format compliance and perceptual encryption, to avoid negative impact on compression efficiency, and to make the multimedia processing pipeline more modular and thus reconfigurable. The seminar will present research on modelling recovery of missing information with different structures in digital images as a discrete optimization problem. In the context of selective encryption, the structure of missing information is defined by the underlying selective encryption algorithm, where the selectively encrypted information is considered missing from an attacker's point of view. Experimental results showed that the new approach can significantly improve the performance of error-concealment attacks compared to the state of the art in terms of visual quality of the recovered images. The approach can be applied to other areas of multimedia security and multimedia processing in general where the structure of missing information in digital signals is known. An example of adapting the model to self-recovery image authentication watermarking will be shown.
At the end of this lesson, you should be able to;
describe the energy and the EM spectrum.
describe image acquisition methods.
discuss image formation model.
express sampling and quantization.
define dynamic range and image representation.
At the end of this lecture students should be able to;
Define the C standard functions for managing file input output.
Apply taught concepts for writing programs.
At the end of this lecture students should be able to;
Define the declaration C strings.
Compare fixed length and variable length string.
Apply strings for functions.
Define string handling functions.
Apply taught concepts for writing programs.
At the end of this lecture students should be able to;
Define, initialize and access to the C stuctuers.
Develop programs using structures in arrays and functions.
Use structures within structures and structures as pointers.
Define, initialize and access to the C unions.
Compare and contrast C structures and unions.
Define memory allocation and de-allocation methods in C.
Develop programs using memory allocation functions.
Apply taught concepts for writing programs.
At the end of this lecture students should be able to;
Define the C standard functions for managing input output.
Apply taught concepts for writing programs.
At the end of this lecture students should be able to;
Define the C pointers and its usage in computer programming.
Describe pointer declaration and initialization.
Apply C pointers for expressions.
Experiment on pointer operations.
Identify NULL pointer concept.
Experiment on pointer to pointer, pointer arrays, arrays with pointers and functions with pointers.
Apply taught concepts for writing programs.
At the end of this lecture students should be able to;
Describe the C arrays.
Practice the declaration, initialization and access linear arrays.
Practice the declaration, initialization and access two dimensional arrays.
Apply taught concepts for writing programs.
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
DERIVATION OF MODIFIED BERNOULLI EQUATION WITH VISCOUS EFFECTS AND TERMINAL V...Wasswaderrick3
In this book, we use conservation of energy techniques on a fluid element to derive the Modified Bernoulli equation of flow with viscous or friction effects. We derive the general equation of flow/ velocity and then from this we derive the Pouiselle flow equation, the transition flow equation and the turbulent flow equation. In the situations where there are no viscous effects , the equation reduces to the Bernoulli equation. From experimental results, we are able to include other terms in the Bernoulli equation. We also look at cases where pressure gradients exist. We use the Modified Bernoulli equation to derive equations of flow rate for pipes of different cross sectional areas connected together. We also extend our techniques of energy conservation to a sphere falling in a viscous medium under the effect of gravity. We demonstrate Stokes equation of terminal velocity and turbulent flow equation. We look at a way of calculating the time taken for a body to fall in a viscous medium. We also look at the general equation of terminal velocity.
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
COM2304: Intensity Transformation and Spatial Filtering – II Spatial Filtering and Smoothing
1. COMPUTER GRAPHICS & IMAGE PROCESSING
COM2304
Intensity Transformation and Spatial Filtering – II
Spatial Filtering and Smoothing
K.A.S.H.Kulathilake
B.Sc. (Hons) IT (SLIIT), MCS (UCSC), M.Phil (UOM), SEDA(UK)
Rajarata University of Sri Lanka
Faculty of Applied Sciences
Department of Physical Sciences
2. Learning Outcomes
COM2304 - Computer Graphics & Image
Processing
• At the end of this lecture, you should be
able to;
– describe the fundamentals of spatial filtering.
– generating spatial filter masks.
– identify smoothing via linear filters and non
linear filters.
– apply smoothing techniques for problem
solving.
3. Filters in Image Processing
• Filter can accept or reject certain frequency
components.
• In image processing filters are mainly used to
suppress either the high frequencies in the
image, i.e. smoothing the image, or the low
frequencies, i.e. enhancing/ sharpening or
detecting edges in the image.
• An image can be filtered either in the
frequency or in the spatial domain.
COM2304 - Computer Graphics & Image
Processing
4. Fundamentals of Frequency
Domain Filtering
• Frequency domain filtering involves ;
– transforming the image into the
frequency domain,
– multiplying it with the frequency
filter function and
– re-transforming the result into the
spatial domain.
• The filter function is shaped so as to
attenuate some frequencies and
enhance others.
• For example, a simple lowpass function
is 1 for frequencies smaller than the
cut-off frequency and 0 for all others.
COM2304 - Computer Graphics & Image
Processing
5. Fundamentals of Spatial Filtering
• Spatial filter contains two components;
– MASK/ CONVOLUTION MATRIX/ KERNEL: A neighborhood
(typically small rectangle:)
– FILTER FUNCTION: A predefined operation that is performed
on the image pixels encompassed by the neighborhood.
• Spatial filtering is the method of choice in situations when only
additive random noise is present.
• Filtering creates a new pixel value with coordinates equal to the
coordinates of the center of the neighborhood, and whose value
is the result of the filtering operation.
COM2304 - Computer Graphics & Image
Processing
7. Fundamentals of Spatial Filtering
(Cont….)
COM2304 - Computer Graphics & Image
Processing
If the operation
performed on the image
pixels is linear, then the
filter is called a linear
spatial filter, otherwise,
the filter is non-linear.
8. Fundamentals of Spatial Filtering
(Cont….)
• Spatial filtering of an image of size M×N with a filter of size
m×n is given by the expression;
• Where w is the mask and f is the image function, x and y
are varied so that each pixel in w visits every pixel in f.
• m = 2a+1 and n = 2b+1, where a and b are positive
integers.
• This concept is known as correlation.
COM2304 - Computer Graphics & Image
Processing
9. Fundamentals of Spatial Filtering
(Cont….)
• There are two closely related concepts that
must be understood clearly when performing
linear spatial filtering
– Correlation
• Correlation is the process of moving a filter mask over
the image and computing the sum of products at each
location exactly as explained previously.
– Convolution
• The mechanics of convolution are the same as
correlation except that the filter is first rotated by 180
degrees.
COM2304 - Computer Graphics & Image
Processing
10. Fundamentals of Spatial Filtering
(Cont….)
COM2304 - Computer Graphics & Image
Processing
If the filter mask is symmetric, correlation and convolution
yield the same result
11. Mask/Kernel/ Convolution Matrix
• In image processing, a kernel, convolution
matrix, or mask is a small matrix.
• It is useful for blurring, sharpening,
embossing, edge detection, and more.
• This is accomplished by means of convolution
between a kernel and an image.
COM2304 - Computer Graphics & Image
Processing
12. Mask/Kernel/ Convolution Matrix
(Cont…)
• Important Features of
Kernel
– Size: 3×3, 5×5, 9×9,
21×21
– Shape: rectangle, cross,
strip, circular, diamond
or user defined.
– Coefficients / Values:
set based on the
operation.
– Anchor point: mostly in
middle
COM2304 - Computer Graphics & Image
Processing
0 -1 0
-1 5 -1
0 -1 0
3×3 Kernel
Anchor
Values for
sharpening
operation
15. Convolution
• Convolution is a simple mathematical
operation which is fundamental to many
common image processing operators.
• This can be used in image processing to
implement operators whose output pixel
values are simple linear combinations of
certain input pixel values.
COM2304 - Computer Graphics & Image
Processing
16. Convolution (Cont…)
COM2304 - Computer Graphics & Image
Processing
Each kernel position
corresponds to a single
output pixel, the value of
which is calculated by
multiplying together the
kernel value and the
underlying image pixel
value for each of the cells
in the kernel, and then
adding all these numbers
together.
The convolution is
performed by sliding the
kernel over the image,
generally starting at the
top left corner.
18. Generating Spatial Filter Masks
• Generating an m×n linear spatial filter requires that we
specify mn mask coefficients.
• These coefficients are selected based on what the filter
is supposed to do.
• There are different types of filters available, some of
them are;
– Linear filters - In signal processing, a linear filter is a filter
whose output is a linear function of its input.
– Continuous function based filters – e.g. Gaussian
smoothing (it’s a kind of linear filter).
– Non linear filters - In signal processing, a non-linear filter is
a filter whose output is not a linear function of its input.
COM2304 - Computer Graphics & Image
Processing
19. Generating Spatial Filter Masks (cont…)
• Linear filters
– Implement sum of products
– ex: filters used for smoothing
• Continuous function based filters
– ex: Gaussian function of two variables as shown in the following
equation;
– Where σ is the standard deviation and coordinates x and y are
the positive integers.
– Recall that 2D Gaussian function has bell shape and the σ
control the tightness of the bell.
COM2304 - Computer Graphics & Image
Processing
20. Generating Spatial Filter Masks (cont…)
• Non linear/ Order static filters
– Non linear filters require to specify the size of the
filter and the operation(s) to be performed on the
image pixel contained in the filter.
• ex: filter 5×5 filter centered at an arbitrary point and
perform max operation.
– Non linear filters are quiet powerful in some
applications, than the linear filters.
COM2304 - Computer Graphics & Image
Processing
21. Smoothing Spatial Filters
• Smoothing filters are used for blurring and
noise reduction.
• Blurring is used in preprocessing tasks, such as
removal of small details from an image prior
to object extraction, and bridging of small
gaps in lines or curves.
• Noise reduction can be accomplished by
blurring with a linear filter and also by
nonlinear filtering.
COM2304 - Computer Graphics & Image
Processing
22. Smoothing Linear Filters
• Average /Mean Filter
– The output is simply the average of the pixels
contained in the neighborhood of the filter mask.
– In smoothing filter, it replaces the value of every pixel
in an image by the average of the intensity levels in
the neighborhood defined by the filter mask.
– This process result in an image with reduced “sharp”
transitions in intensities.
– Most obvious application of smoothing is noise
reduction because random noise typically consists of
sharp transitions in intensity levels.
COM2304 - Computer Graphics & Image
Processing
24. Smoothing Linear Filters (Cont…)
– Averaging filters blur edges, because edges
represent the sharp intensity transition.
– Average blurring smoothes the false contours that
result from using an insufficient number of
intensity levels.
– A major use of averaging filters is in the reduction
of “irrelevant” detail (pixel regions that are small
with respect to the size of the filter mask) in an
image.
COM2304 - Computer Graphics & Image
Processing
25. Smoothing Linear Filters (Cont…)
COM2304 - Computer Graphics & Image
Processing
Observe
blurring
occur during
smoothing
operation
26. Smoothing Linear Filters (Cont…)
– Let Sx,y represent the set of coordinates in neighbourhood
of size m×n, centered at point (x, y).
– The arithmetic mean filter computes the average value of
the corrupted image g( x, y) in the area defined by Sx,y.
– The value of the restored image f’ at point ( x, y) is simply
the arithmetic mean computed using the pixels in the
region defined by Sx,y.
COM2304 - Computer Graphics & Image
Processing
xySts
tsg
mn
yxf
),(
),(
1
),(ˆ
27. Smoothing Linear Filters (Cont…)
• There are different kinds of mean filters all of
which exhibit slightly different behaviour:
• Geometric Mean:
– Achieves similar smoothing to the arithmetic
mean, but tends to lose less image detail
COM2304 - Computer Graphics & Image
Processing
mn
Sts xy
tsgyxf
1
),(
),(),(ˆ
28. Smoothing Linear Filters (Cont…)
• Harmonic Mean:
– Works well for salt noise, but fails for pepper
noise.
– Also does well for other kinds of noise such as
Gaussian noise
COM2304 - Computer Graphics & Image
Processing
xySts tsg
mn
yxf
),( ),(
1
),(ˆ
29. Smoothing Linear Filters (Cont…)
• Contraharmonic Mean:
– Q is the order of the filter and adjusting its value
changes the filter’s behaviour.
– Positive values of Q eliminate pepper noise.
– Negative values of Q eliminate salt noise.
– If Q =0 it represents the arithmetic filter.
COM2304 - Computer Graphics & Image
Processing
xy
xy
Sts
Q
Sts
Q
tsg
tsg
yxf
),(
),(
1
),(
),(
),(ˆ
30. Smoothing Linear Filters (Cont…)
Original
Image
Image
Corrupted
By Gaussian
Noise
After A 3*3
Geometric
Mean Filter
After A 3*3
Arithmetic
Mean Filter
COM2304 - Computer Graphics & Image
Processing
31. Smoothing Linear Filters (Cont…)
Image
Corrupted
By Pepper
Noise
Result of
Filtering Above
With 3*3
Contraharmonic
Q=1.5
COM2304 - Computer Graphics & Image
Processing
32. Smoothing Linear Filters (Cont…)
Image
Corrupted
By Salt
Noise
Result of
Filtering Above
With 3*3
Contraharmonic
Q=-1.5
COM2304 - Computer Graphics & Image
Processing
33. Smoothing Linear Filters (Cont…)
Choosing the wrong value for Q when using the
contraharmonic filter can have drastic results
COM2304 - Computer Graphics & Image
Processing
34. Smoothing Linear Filters (Cont…)
• A spatial averaging filter in which all
coefficients are equal is called a Box
Filter.
• The basic strategy behind weighting
the center point the highest and then
reducing the value of the coefficients
as a function of increasing distance
from the origin in spatial averaging
filter is simply an attempt to reduce
blurring in the smoothing process.
COM2304 - Computer Graphics & Image
Processing
35. Smoothing Linear Filters (Cont…)
• Gaussian Smoothing:
– The Gaussian filter is a non-uniform low pass filter.
– The kernel coefficients diminish with increasing distance from
the kernel’s centre.
– Central pixels have a higher weighting than those on the
periphery.
– Larger values of σ produce a wider peak (greater blurring).
– Kernel size must increase with increasing σ to maintain the
Gaussian nature of the filter (`bell-shaped') .
– Gaussian kernel coefficients depend on the value of σ.
– At the edge of the mask, coefficients must be close to 0.
– The kernel is rotationally symmetric with no directional bias.
– Gaussian filters might not preserve image brightness.
COM2304 - Computer Graphics & Image
Processing
36. Smoothing Linear Filters (Cont…)
• The Gaussian distribution
in 1-D has the form:
• Where σ is the standard
deviation of the
distribution.
• We have also assumed
that the distribution has a
mean of zero (i.e. it is
centered on the line x=0).
COM2304 - Computer Graphics & Image
Processing
1-D Gaussian distribution with
mean 0 and σ=1
37. Smoothing Linear Filters (Cont…)
• In 2-D, an isotropic
(i.e. circularly symmetric)
Gaussian has the form:
COM2304 - Computer Graphics & Image
Processing
2-D Gaussian distribution with
mean (0,0) and σ=1
Discrete approximation to Gaussian
function with σ=1
The std. dev σ of the Gaussian
determines the amount of
smoothing.
39. Smoothing Order Static (Nonlinear)
Filters
• The response of the order static or nonlinear
filters 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 determine by the ranking
result.
• Useful nonlinear filters include;
– Median filter
– Max and min filter
– Midpoint filter
COM2304 - Computer Graphics & Image
Processing
40. Smoothing Order Static (Nonlinear)
Filters (Cont…)
• Median Filter:
– It can reduce certain typed of random noise with
less blurring than the linear smoothing filters of
similar size.
– Provides excellent results when applying to reduce
salt and pepper noise.
COM2304 - Computer Graphics & Image
Processing
)},({),(ˆ
),(
tsgmedianyxf
xySts
43. Smoothing Order Static (Nonlinear)
Filters (Cont…)
• Median filter smoothes ("washes") all edges
and boundaries and may "erase" all details
whose size is about n/2× m/2, where n × m is
a window size.
• As a result, an image becomes "fuzzy".
• Median filter is not so efficient for additive
Gaussian noise removal, it yields to linear
filters.
COM2304 - Computer Graphics & Image
Processing
44. Smoothing Order Static (Nonlinear)
Filters (Cont…)
• Max Filter:
• Min Filter:
–Max filter is useful for finding the brightest points in an
image and min filter for finding the darkest points in an
image.
–Max filter is good for pepper noise and min filter is good for
salt noise
COM2304 - Computer Graphics & Image
Processing
)},({max),(ˆ
),(
tsgyxf
xySts
)},({min),(ˆ
),(
tsgyxf
xySts
45. Smoothing Order Static (Nonlinear)
Filters (Cont…)
• Midpoint Filter:
– Good for random Gaussian and uniform noise
COM2304 - Computer Graphics & Image
Processing
)},({min)},({max
2
1
),(ˆ
),(),(
tsgtsgyxf
xyxy StsSts
46. Smoothing Order Static (Nonlinear)
Filters (Cont…)
• Alpha-trimmed Mean Filter:
– We can delete the d/2 lowest and d/2 highest grey
levels.
– So g(s, t) represents the remaining mn – d pixels.
– When d= 0 : arithmetic mean filter.
– Good for combination of salt and pepper and
Gaussian noise.
COM2304 - Computer Graphics & Image
Processing
xySts
tsg
dmn
yxf
),(
),(
1
),(ˆ
48. Removing Periodic Noise
• Removing periodic noise form an image
involves removing a particular range of
frequencies from that image.
• Band reject filters can be used for this
purpose.
Out of the scope of this course -
COM2304 - Computer Graphics & Image
Processing
50. Learning Outcomes Revisit
• Now, you should be able to;
– describe the fundamentals of spatial filtering.
– generating spatial filter masks.
– identify smoothing via linear filters and non
linear filters.
– apply smoothing techniques for problem
solving.
COM2304 - Computer Graphics & Image
Processing
51. QUESTIONS ?
Next Lecture – Intensity Transformation and Spatial Filtering – III
COM2304 - Computer Graphics & Image
Processing