This document summarizes techniques for least mean square filtering and geometric transformations. It discusses minimum mean square error (Wiener) filtering, constrained least squares filtering, and geometric mean filtering for noise removal. It also covers spatial transformations, nearest neighbor gray level interpolation, and bilinear interpolation for geometric correction of distorted images. Examples are provided to demonstrate geometric distortion, nearest neighbor interpolation, and bilinear transformation.
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 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.
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.
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
This slides about brief Introduction to Image Restoration Techniques. How to estimate the degradation function, noise models and its probability density functions.
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.
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
This slides about brief Introduction to Image Restoration Techniques. How to estimate the degradation function, noise models and its probability density functions.
Image Restitution Using Non-Locally Centralized Sparse Representation ModelIJERA Editor
Sparse representation models uses a linear combination of a few atoms selected from an over-completed
dictionary to code an image patch which have given good results in different image restitution applications. The
reconstruction of the original image is not so accurate using traditional models of sparse representation to solve
degradation problems which are blurring, noisy, and down-sampled. The goal of image restitution is to suppress
the sparse coding noise and to improve the image quality by using the concept of sparse representation. To
obtain a good sparse coding coefficients of the original image we exploit the image non-local self similarity and
then by centralizing the sparse coding coefficients of the observation image to those estimates. This non-locally
centralized sparse representation model outperforms standard sparse representation models in all aspects of
image restitution problems including de-noising, de-blurring, and super-resolution.
Design Approach of Colour Image Denoising Using Adaptive WaveletIJERD Editor
International Journal of Engineering Research and Development is an international premier peer reviewed open access engineering and technology journal promoting the discovery, innovation, advancement and dissemination of basic and transitional knowledge in engineering, technology and related disciplines.
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.
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.
For more information, visit-www.vavaclasses.com
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.
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.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
The Art Pastor's Guide to Sabbath | Steve ThomasonSteve Thomason
What is the purpose of the Sabbath Law in the Torah. It is interesting to compare how the context of the law shifts from Exodus to Deuteronomy. Who gets to rest, and why?
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
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.
1. LEAST MEAN SQUARE
AND
GEOMETRIC TRANFORMATION
PRESENTED BY,
K.LALITHAMBIGA,
II- M.Sc (CS & IT),
Nadar Saraswathi College of
Arts and Science, Theni.
3. MINIMUM MEAN SQUARE ERROR (WIENER)
FILTERING
In most images, adjacent pixels are highly correlated, while the
gray level of widely separated pixels are only loosely correlated.
Therefore, the autocorrelation function of typical images
generally decreases away from the origin.
Power spectrum of an image is the Fourier transform of its
autocorrelation function, therefore we can argue that the power
spectrum of an image generally decreases with frequency.
Typical noise sources have either a flat power spectrum or one
that decreases with frequency more slowly than typical image
power spectrum.
Therefore, the expected situation is for the signal to dominate the
spectrum at low frequencies, while the noise dominates the high
frequencies.
4. MINIMUM MEAN SQUARE ERROR (WIENER)
FILTERING
The estimate ƒ of the uncorrupted image ƒ such that the
mean square error between them is minimized .
The minimum of the error function is given in the
frequency domain by the expression
e²=E{(ƒ-ƒ )²}ˆ
),(
),(/),(),(
),(
),(
1
),(
),(/),(),(
),(*
),(
),(),(),(
),(),(*
),(ˆ
2
2
2
2
vuG
vuSvuSvuH
vuH
vuH
vuG
vuSvuSvuH
vuH
vuG
vuSvuHvuS
vuSvuH
vuF
f
f
f
f
ˆ
8. WIENER FILTERING - PROBLEMS
The power spectra of the under graded image and noise
must be known.
Weights all errors equally regardless of their location in the
image, while the eye is considerably more tolerant of errors
in the dark areas and high-gradient areas in the image.
In minimizing the mean square error, Wiener filter also
smooth the image more than the eye would prefer
9. CONSTRAINED LEAST SQUARES
FILTERING
Only the mean and variance of the noise is required
g-vector by using the image elements in first row of g(x,y)
-dimensions
H –The matrix H then has dimensions MNX MN
The degradation model in vector-matrix form
The objective function
Subject to the constraint
111 MNMNMNMNMN ηfHg
21
0
1
0
2
)],([
M
x
N
y
yxfC
ηHfg
ηf,
22
ηHfg
10. CONSTRAINED LEAST SQUARES
FILTERING
The frequency domain solution to this optimization
problem
The Fourier transform of the function
),(
),(),(
),(*
),(ˆ
2
vuG
vuPvuH
vuH
vuF
010
141
010
),( yxp
12. Low noise: Wiener and CLS generate
equal results.
High noise: CLS outperforms Wiener if λ
is properly selected.
It is easier to select the scalar value for λ
than to approximate the SNR which is
seldom constant
13. GEOMETRIC MEAN FILTER
The geometric mean filter is a member of a set of
nonlinear filters that are used to remove Gaussian noise.
It operates by replacing each pixel by the geometric mean of
the values in its neighborhood.
),(
),(
),(
),(ˆ
2
),(
),(*
1
2),(
),(*
vuG
vuS
vuS
vuF
f
vuH
vuH
vuH
vuH
14. GEOMETRIC MEAN FILTER
and being positive, real constants.
The geometric mean filter consists of the 2 expressions in
brackets raised to the powers and 1-
=1-this filter reduces to the inverse filter.
=0-this filter raised to the same power.
Its is also called parametric wiener filter.
=½ and =1 this filter also called as spectrum
equalization filter.
15. GEOMETRIC TRANSFORMATIONS
The geometric transformations modify the spatial
relationships between pixels in an image.
The geometric transformations often are called rubber-
sheet transformations
Geometric transformation consists of 2 basic operations:
1. A Spatial Transformations –rearrangement of pixels on
the image plane.
2. Gray-level interpolation –assignment of gray levels to
pixels in the spatially transformed image.
16. SPATIAL TRANSFORMATION
Assume the original image f(x,y) is subject to geometric
distortion yielding g(x’,y’)
Spatial transformation &
Coordination transformation
The most frequently to overcome this difficulty is to formulate
the spatial relocation of pixels by the use of tiepoints,
Subset of pixels whose location
Input – Distorted
Output – Corrected
• Function need 8 or more points
to find {ci; 1 i 8}
x´=r(x,y) y´=s(x,y)
8765
4321
),('
),('
cxycycxcyxsy
cxycycxcyxrx
17. GRAY LEVEL INTERPOLATION
Spatial transformations establish a correspondence between
a point (x’, y’) in the distorted image g(x’,y’) and original
image f(x,y).
To correct the geometric transformation, one needs to
estimate gray values of f(x,y),
If x’ and y’ are integers, then
If x’ and y’ are fraction numbers, but fall within the b order
of the original image, then interpolation will be needed to
find
The gray-level interpolation is based on a nearest neighbor
approach. The method is called zero order interpolation.
),(ˆ yxf
)','(),(ˆ yxgyxf
),(ˆ yxf
19. BILINEAR INTERPOLATION
Estimate the value of =g(x’,y’)) using
four nearest neighbors when x’ and y’ are
fractional numbers.
Substitute g(x1,y1), g(x1,y2), g(x2,y1), g(x2,y2)
into above equation and solve for a, b, c, d.
It’s 4 equations and 4 unknowns.
(x1,y2)
(x1,y1)
(x2,y2)
(x2,y1)
dycxbyaxyxg
yyyyy
xxxxx
'''')','(and
'''
'''
21
21
(x’,y’)
),(ˆ yxf
20. a. An image with 25 regularly spaced
tiepoints.
b. Geometric distortion by
rearranging the tiepoints
c. Distorted image, nearest neighbor
interpolation
d. Restored image, NN
e. Distorted image, bilinear
transformation
f. Restored image, BT
EXAMPLES
21. EXAMPLES
a. Original image
b. Distorted image using bilinear
transform
c. Difference between a and b
d. Geometrically restored image
using bilinear transform for
gray level interpolation