This document discusses image processing in the frequency domain using the Fourier transform. It explains that image enhancement can be performed by designing a transfer function in the frequency domain and multiplying it with the image's Fourier transform. Filtering an image corresponds to multiplying its Fourier transform by a filter transfer function. Common filters discussed include low-pass filters for smoothing and high-pass filters for sharpening. Ideal filters have abrupt cutoffs which cause ringing artifacts, while Butterworth and Gaussian filters provide smoother responses.
Basic Introduction about Image Restoration (Order Statistics Filters)
Median Filter
Max and Min Filter
MidPoint Filter
Alpha-trimmed Mean filter.
and Brief Introduction to Periodic Noise
Any Question contact kalyan.acharjya@gmail.com
Image processing, Noise, Noise Removal filtersKuppusamy P
Basics of images, Digital Images, Noise, Noise Removal filters
Reference:
Richard Szeliski, Computer Vision: Algorithms and Applications, Springer 2010
Basic Introduction about Image Restoration (Order Statistics Filters)
Median Filter
Max and Min Filter
MidPoint Filter
Alpha-trimmed Mean filter.
and Brief Introduction to Periodic Noise
Any Question contact kalyan.acharjya@gmail.com
Image processing, Noise, Noise Removal filtersKuppusamy P
Basics of images, Digital Images, Noise, Noise Removal filters
Reference:
Richard Szeliski, Computer Vision: Algorithms and Applications, Springer 2010
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.
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
Image Restoration And Reconstruction
Mean Filters
Order-Statistic Filters
Spatial Filtering: Mean Filters
Adaptive Filters
Adaptive Mean Filters
Adaptive Median 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.
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.
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
Image Restoration And Reconstruction
Mean Filters
Order-Statistic Filters
Spatial Filtering: Mean Filters
Adaptive Filters
Adaptive Mean Filters
Adaptive Median 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.
This presentation contains the concepts of frequency domain filtering of digital images. This includes the different kinds of filters used in frequency domain analysis,their characteristics and various phenomenon such as aliasing, inverse filtering etc. The contents are taken from variety of sources like Gonzalez image processing book, Pratt image processing book and some on-line resources.
Implementation and comparison of Low pass filters in Frequency domainZara Tariq
Demonstrating the application results of some low pass filters in a frequency domain.
Pictures and MATLAB code been used in the experiment are taken from the internet.
ppt on Time Domain and Frequency Domain Analysissagar_kamble
in this presentation, you will be able to know what is this freq. and time domain analysis.
At last one example is illustreted with video, which distinguishes these two analysis
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.
WEBINAR ON FUNDAMENTALS OF DIGITAL IMAGE PROCESSING DURING COVID LOCK DOWN by by K.Vijay Anand , Associate Professor, Department of Electronics and Instrumentation Engineering , R.M.K Engineering College, Tamil Nadu , India
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
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The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
How to Create Map Views in the Odoo 17 ERPCeline George
The map views are useful for providing a geographical representation of data. They allow users to visualize and analyze the data in a more intuitive manner.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
This is a presentation by Dada Robert in a Your Skill Boost masterclass organised by the Excellence Foundation for South Sudan (EFSS) on Saturday, the 25th and Sunday, the 26th of May 2024.
He discussed the concept of quality improvement, emphasizing its applicability to various aspects of life, including personal, project, and program improvements. He defined quality as doing the right thing at the right time in the right way to achieve the best possible results and discussed the concept of the "gap" between what we know and what we do, and how this gap represents the areas we need to improve. He explained the scientific approach to quality improvement, which involves systematic performance analysis, testing and learning, and implementing change ideas. He also highlighted the importance of client focus and a team approach to quality improvement.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
2. The concept of filtering is easier to visualize in the frequency domain.
Therefore, enhancement of image f(m,n) can be done in the frequency
domain, based on its DFT F(u,v) .
This is particularly useful, if the spatial extent of the point-spread
sequence h(m,n) is large. In this case, the convolution
g(m,n) = h(m,n)*f(m,n)
may be computationally unattractive.
2
Enhanced
Image
PSS
Given Image
3. We can therefore directly design a transfer function H(u,v) and
implement the enhancement in the frequency domain as follows:
G(u,v) = H(u,v)*F(u,v)
3
Enhanced
Image
Transfer Function
Given Image
4. Given a 1-d sequence s[k], k = {…,-1,0,1,2,…,}
Fourier transform
Fourier transform is periodic with 2
Inverse Fourier transform
4
5. How is the Fourier transform of a sequence s[k] related to the Fourier
transform of the continuous signal
Continuous-time Fourier transform
5
6. Given a 2-d matrix of image samples
s[m,n], m,n Z2
Fourier transform
Fourier transform is 2 -periodic both in x and y
Inverse Fourier transform
6
7. How is the Fourier transform of a sequence s[m,n] related to the
Fourier transform of the continuous signal
Continuous-space 2D Fourier transform
7
18. Image formed from magnitude
spectrum of Rice and phase
spectrum of Camera man
18
19. Image formed from magnitude
spectrum of Camera man and
phase spectrum of Rice
19
20. For discrete images of finite extent, the analogous Fourier transform is
the DFT.
We will first study this for the 1-D case, which is easier to visualize.
Suppose { f(0), f(1), …, f(N – 1)} is a sequence/ vector/1-D image
of length N. Its N-point DFT is defined as
Inverse DFT (note the normalization):
20
22. F(u) is complex even though f(n) is real. This is typical.
Implementing the DFT directly requires O(N2) computations, where N
is the length of the sequence.
There is a much more efficient implementation of the DFT using the
Fast Fourier Transform (FFT) algorithm. This is not a new transform (as
the name suggests) but just an efficient algorithm to compute the DFT.
22
23. The FFT works best when N = 2m (or is the power of some integer
base/radix). The radix-2 algorithm is most commonly used.
The computational complexity of the radix-2 FFT algorithm is Nlog(N)
adds and ½Nlog(N) multiplies. So it is an Nlog(N) algorithm.
In MATLAB, the command fft implements this algorithm (for 1-D
case).
23
24. The Fourier transform is suitable for continuous-domain images, which
maybe of infinite extent.
For discrete images of finite extent, the analogous Fourier transform is
the 2-D DFT.
24
25. Suppose f(m,n), m = 0,1,2,…M – 1, n = 0,1,2,…N – 1, is a discrete
N M image. Its 2-D DFT F(u,v) is defined as:
Inverse DFT is defined as:
25
26. For discrete images of finite extent, the analogous Fourier transform is
the 2-D DFT.
Note about normalization: The normalization by MN is different than
that in text. We will use the one above since it is more widely used. The
Matlab function fft2 implements the DFT as defined above.
26
27. Most often we have M=N (square image) and in that case, we define a
unitary DFT as follows:
We will refer to the above as just DFT (drop unitary) for simplicity.
27
29. 29
In matlab, if f and h are matrices representing two images,
conv2(f, h) gives the 2D-convolution of images f and h.
30. Linearity (Distributivity and Scaling): This holds inboth discrete and
continuous-domains.
o DFT of the sum of two images is the sum of their individual DFTs.
o DFT of a scaled image is the DFT of the original image scaled by the same
factor.
30
31. Spatial scaling (only for continuous-domain):
o If a, b > 1, image “shrinks” and the spectrum “expands.”
31
32. Periodicity (only for discrete case): The DFT and its inverse are
periodic (in both the dimensions), with period N.
F(u,v) = F(u+N,v) = F(u,v+N) = F(u+N,v+N)
o Similarly,
is also N-periodic in m and n.
32
33. Separability (both continuous and discrete): Decomposition of 2D DFT
into 1D DFTs
33
35. Convolution: In continuous-space, Fourier transform of the convolution
is the product of the Four transforms.
F[f(x,y)*h(x,y)] = F(u,v) H(u,v)
So if
g(x,y) = f(x,y)*h(x,y)
is the output of an LTI transformation with PSF h(x,y) to an input image
f(x,y), then
G(u,v) = F(u,v)*H(u,v)
35
36. o In other words, output spectrum G(u,v) is the product of the input
spectrum F(u,v) and the transfer function H(u,v).
o So the FT can be used as a computational tool to simplify the
convolution operation.
36
37. Correlation: In continuous-space, correlation between two images
f(x,y) and h(x,y) is defined as:
Therefore,
37
38. rff(x,y) is usually called the auto-correlation of image f(x,y) (with
itself) and rff(x,y) is called the crosscorrelation between f(x,y) and
h(x,y).
Roughly speaking, rfh(x,y) measures the degree of similarity between
images f(x,y) and h(x,y). Large values of rfh(x,y) would indicate that
the images are very similar.
38
39. This is usually used in template matching, where h(x,y) is a template
shape whose presence we want to detect in the image f(x,y).
Locations where rfh(x,y) is high (peaks of the crosscorrelation
function) are most likely to be the location of shape h(x,y) in image
f(x,y).
39
40. Convolution property for discrete images: Suppose
f(m,n), m = 0,1,2,…M–1, n = 0,1,2,…N–1 is an N M image and
h(m,n), m = 0,1,2,…K–1, n = 0,1,2,…L–1 is an N M image.
then
g(m,n) = f(m,n)*h(m,n) is a (M+K–1) (N+L–1) image.
40
41. So if we want a convolution property for discrete images --- something
like
g(m,n) = f(m,n)*h(m,n)
we need to have G(u, v) to be of size (M+K–1) (N+L–1) (since
g(m, n) has that dimension).
Therefore, we should require that F(u, v) and H(u, v) also have the
same dimension, i.e. (M+K–1) (N+L–1)
41
42. So we zero-pad the images f(m, n), h(m, n), so that they are of size
(M+K–1 ) (N+L–1). Let fe(m,n) and he(m,n) be the zero-padded
(or extended images).
Take their 2D-DFTs to obtain F(u, v) and H(u, v), each of size
(M+K–1) (N+L– 1). Then
Similar comments hold for correlation of discrete images as well.
42
43. Translation: (discrete and continuous case):
Note that
so f(m, n) and f(m–m0, n–n0) have the same magnitude spectrum
but different phase spectrum.
Similarly,
43
44. Conjugate Symmetry: If f(m, n) is real, then F(u, v) is conjugate
symmetric, i.e.
Therefore, we usually display F(u–N/2,v–N/2), instead of F(u, v),
since it is easier to visualize the symmetry of the spectrum in this case.
This is done in Matlab using the fftshift command.
44
45. Multiplication: (In continuous-domain) This is the dual of the
convolution property. Multiplication of two images corresponds to
convolving their spectra.
F[f(x,y)h(x,y)] = F(u,v) H(u,v)
45
48. Average value: The average pixel value in an image:
Notice that (substitute u = v = 0 in the definition):
48
49. Differentiation: (Only in continuous-domain): Derivatives are normally
used for detecting edged in an image. An edge is the boundary of an
object and denotes an abrupt change in grayvalue. Hence it is a region
with high value of derivative.
49
53. Edges and sharp transitions in grayvalues in an image contribute
significantly to high-frequency content of its Fourier transform.
Regions of relatively uniform grayvalues in an image contribute to low-
frequency content of its Fourier transform.
Hence, an image can be smoothed in the Frequency domain by
attenuating the high-frequency content of its Fourier transform.
This would be a lowpass filter!
53
55. For simplicity, we will consider only those filters that are real and
radially symmetric.
An ideal lowpass filter with cutoff frequency r0:
55
56. Note that the origin (0, 0) is at the center and not the corner of the
image (recall the “fftshift” operation).
The abrupt transition from 1 to 0 of the transfer function H(u,v) cannot
be realized in practice, using electronic components. However, it can
be simulated on a computer.
56
Ideal LPF with r0 = 57
59. Notice the severe ringing effect in the blurred images, which is a
characteristic of ideal filters. It is due to the discontinuity in the filter
transfer function.
59
60. The cutoff frequency r0 of the ideal LPF determines the amount of
frequency components passed by the filter.
Smaller the value of r0, more the number of image components
eliminated by the filter.
In general, the value of r0 is chosen such that most components of
interest are passed through, while most components not of interest are
eliminated.
Usually, this is a set of conflicting requirements. We will see some
details of this is image restoration
A useful way to establish a set of standard cut-off frequencies is to
compute circles which enclose a specified fraction of the total image
power.
60
61. Suppose
where is the total image power.
Consider a circle of radius =r0(a) as a cutoff frequency with respect to
a threshold a such that
We can then fix a threshold a and obtain an appropriate cutoff
frequency r0(a) .
61
62. A two-dimensional Butterworth lowpass filter has transfer function:
n: filter order, r0: cutoff frequency
62
65. Frequency response does not have a sharp transition as in the ideal
LPF.
This is more appropriate for image smoothing than the ideal LPF, since
this not introduce ringing.
65
71. The form of a Gaussian lowpass filter in two-dimensions is given by
where
is the distance from the origin in the frequency plane.
The parameter s measures the spread or dispersion of the Gaussian
curve. Larger the value of s, larger the cutoff frequency and milder the
filtering.
When s = D(u, v), the filter is down to 0.607 of its maximum value of
1.
71
22
, vuvuD
22
2,
, vuD
evuH
73. Edges and sharp transitions in grayvalues in an image contribute
significantly to high-frequency content of its Fourier transform.
Regions of relatively uniform grayvalues in an image contribute to low-
frequency content of its Fourier transform.
Hence, image sharpening in the Frequency domain can be done by
attenuating the low-frequency content of its Fourier transform. This
would be a highpass filter!
73
74. For simplicity, we will consider only those filters that are real and
radially symmetric.
An ideal highpass filter with cutoff frequency r0:
74
75. Note that the origin (0, 0) is at the center and not the corner of the
image (recall the “fftshift” operation).
The abrupt transition from 1 to 0 of the transfer function H(u,v) cannot
be realized in practice, using electronic components. However, it can
be simulated on a computer.
75
Ideal HPF with r0= 36
78. Notice the severe ringing effect in the output images, which is a
characteristic of ideal filters. It is due to the discontinuity in the filter
transfer function.
78
79. A two-dimensional Butterworth highpass filter has transfer function:
n: filter order, r0: cutoff frequency
79
81. Frequency response does not have a sharp transition as in the ideal
HPF.
This is more appropriate for image sharpening than the ideal HPF,
since this not introduce ringing
81
84. The form of a Gaussian lowpass filter in two-dimensions is given by
where
is the distance from the origin in the frequency plane.
The parameter s measures the spread or dispersion of the Gaussian
curve. Larger the value of s, larger the cutoff frequency and more
severe the filtering.
84
22
, vuvuD
22
2,
1, vuD
evuH