The document discusses denoising signals using wavelet transforms. It begins with an overview of denoising and its goal of reconstructing a signal from a noisy one. It then compares denoising using wavelets to other methods like Fourier filtering and spline methods. The key advantages of wavelets are their ability to localize properties and concentrate a signal's energy. The document outlines the basic denoising process using wavelet transforms which involves decomposition, thresholding, and reconstruction. It also discusses different thresholding methods and commonly used thresholds like VisuShrink.
Images may contain different types of noises. Removing noise from image is often the first step in image processing, and remains a challenging problem in spite of sophistication of recent research. This ppt presents an efficient image denoising scheme and their reconstruction based on Discrete Wavelet Transform (DWT) and Inverse Discrete Wavelet Transform (IDWT).
Image Processing involves the immense utilisation of Wavelet Transforms, and to apply on images require the knowledge of its application two dimensions.
Images may contain different types of noises. Removing noise from image is often the first step in image processing, and remains a challenging problem in spite of sophistication of recent research. This ppt presents an efficient image denoising scheme and their reconstruction based on Discrete Wavelet Transform (DWT) and Inverse Discrete Wavelet Transform (IDWT).
Image Processing involves the immense utilisation of Wavelet Transforms, and to apply on images require the knowledge of its application two dimensions.
EEG Based Classification of Emotions with CNN and RNNijtsrd
Emotions are biological states associated with the nervous system, especially the brain brought on by neurophysiological changes. They variously cognate with thoughts, feelings, behavioural responses, and a degree of pleasure or displeasure and it exists everywhere in daily life. It is a significant research topic in the development of artificial intelligence to evaluate human behaviour that are primarily based on emotions. In this paper, Deep Learning Classifiers will be applied to SJTU Emotion EEG Dataset SEED to classify human emotions from EEG using Python. Then the accuracy of respective classifiers that is, the performance of emotion classification using Convolutional Neural Network CNN and Recurrent Neural Networks are compared. The experimental results show that RNN is better than CNN in solving sequence prediction problems. S. Harshitha | Mrs. A. Selvarani "EEG Based Classification of Emotions with CNN and RNN" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30374.pdf Paper Url :https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/30374/eeg-based-classification-of-emotions-with-cnn-and-rnn/s-harshitha
There are several ways to detect emotion. We can briefly list them here:
EEG + BCI
ECG + Cardiovascular signals
Electrodermal activity
Speech + Voice intonation
Facial expressions
Body language
Now we can take a look at their applications!
Lec-17: Sparse Signal Processing & Applications [notes]
Sparse signal processing, recovery of sparse signal via L1 minimization. Applications including face recognition, coupled dictionary learning for image super-resolution.
EEG Based Classification of Emotions with CNN and RNNijtsrd
Emotions are biological states associated with the nervous system, especially the brain brought on by neurophysiological changes. They variously cognate with thoughts, feelings, behavioural responses, and a degree of pleasure or displeasure and it exists everywhere in daily life. It is a significant research topic in the development of artificial intelligence to evaluate human behaviour that are primarily based on emotions. In this paper, Deep Learning Classifiers will be applied to SJTU Emotion EEG Dataset SEED to classify human emotions from EEG using Python. Then the accuracy of respective classifiers that is, the performance of emotion classification using Convolutional Neural Network CNN and Recurrent Neural Networks are compared. The experimental results show that RNN is better than CNN in solving sequence prediction problems. S. Harshitha | Mrs. A. Selvarani "EEG Based Classification of Emotions with CNN and RNN" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30374.pdf Paper Url :https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/30374/eeg-based-classification-of-emotions-with-cnn-and-rnn/s-harshitha
There are several ways to detect emotion. We can briefly list them here:
EEG + BCI
ECG + Cardiovascular signals
Electrodermal activity
Speech + Voice intonation
Facial expressions
Body language
Now we can take a look at their applications!
Lec-17: Sparse Signal Processing & Applications [notes]
Sparse signal processing, recovery of sparse signal via L1 minimization. Applications including face recognition, coupled dictionary learning for image super-resolution.
IEEE 2015 Matlab projects,ME Matlab projects bangalore,BE Matlab projects,ME Final year MATLAB Projects,BE Final year MATLAB Projects,IEEE 2015 BE MATLAB projects bangalore,IEEE 2015 BE MATLAB projects bangalore,IEEE 2015 ME MATLAB projects bangalore
Image Denoising using Spatial Domain Filters: A Quantitative StudyAnmol Sharma
o Presented the paper at IEEE International Congress on Image and Signal Processing & BioMedical Engineering and Informatics 2013 (CISP-BMEI 2013) held at Hangzhou, China, 16-18th December 2013 being the first author. Paper will soon appear on IEEE XPLORE online library. More details available on request.
Introduction to Digital Image Processing Using MATLABRay Phan
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You can access the images and code that I created and used here: https://www.dropbox.com/sh/s7trtj4xngy3cpq/AAAoAK7Lf-aDRCDFOzYQW64ka?dl=0
WAVELET THRESHOLDING APPROACH FOR IMAGE DENOISINGIJNSA Journal
The original image corrupted by Gaussian noise is a long established problem in signal or image processing .This noise is removed by using wavelet thresholding by focused on statistical modelling of wavelet coefficients and the optimal choice of thresholds called as image denoising . For the first part, threshold is driven in a Bayesian technique to use probabilistic model of the image wavelet coefficients that are dependent on the higher order moments of generalized Gaussian distribution (GGD) in image processing applications. The proposed threshold is very simple. Experimental results show that the proposed method is called BayesShrink, is typically within 5% of the MSE of the best soft-thresholding benchmark with the image. It outperforms Donoho and Johnston Sure Shrink. The second part of the paper is attempt to claim on lossy compression can be used for image denoising .thus achieving the image compression & image denoising simultaneously. The parameter is choosing based on a criterion derived from Rissanen’s minimum description length (MDL) principle. Experiments show that this compression & denoise method does indeed remove noise significantly, especially for large noise power.
In this paper we discuss the speckle reduction in images with the recently proposed Wavelet Embedded Anisotropic Diffusion (WEAD) and Wavelet Embedded Complex Diffusion (WECD). Both these methods are improvements over anisotropic and complex diffusion by adding wavelet based bayes shrink in its second stage. Both WEAD and WECD produce excellent results when compared with the existing speckle reduction filters.
IVR - Chapter 2 - Basics of filtering I: Spatial filters (25Mb) Charles Deledalle
Moving averages. Finite differences and edge detectors. Gradient, Sobel and Laplacian. Linear translations invariant filters, cross-correlation and convolution. Adaptive and non-linear filters. Median filters. Morphological filters. Local versus global filters. Sigma filter. Bilateral filter. Patches and non-local means. Applications to image denoising.
In this paper a PDE based hybrid method for image denoising is introduced. The method is a bi-stage filter with anisotropic diffusion followed by wavelet based bayesian shrinkage. Here efficient denoising is achieved by reducing the convergence time of anisotropic diffusion.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
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Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
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Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
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Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
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Learn about:
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2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
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Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
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State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
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The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
2. In today’s show
Denoising – definition
Denoising using wavelets vs. other methods
Denoising process
Soft/Hard thresholding
Known thresholds
Examples and comparison of denoising methods
using WL
Advanced applications
2 different simulations
Summary
July 25, 2012 2
3. In today’s show
Denoising – definition
Denoising using wavelets vs. other methods
Denoising process
Soft/Hard thresholding
Known thresholds
Examples and comparison of denoising methods
using WL
Advanced applications
2 different simulations
Summary
July 25, 2012 3
4. Denoising
Denosing is the process with which we reconstruct a signal from a
noisy one.
original
denoised
July 25, 2012 4
5. In today’s show
Denoising – definition
Denoising using wavelets vs. other methods
Denoising process
Soft/Hard thresholding
Known thresholds
Examples and comparison of denoising methods
using WL
Advanced applications
2 different simulations
Summary
July 25, 2012 5
6. Old denoising methods
What was wrong with existing methods?
Kernel estimators / Spline estimators
Do not resolve local structures well enough. This is necessary when
dealing with signals that contain structures of different scales and
amplitudes such as neurophysiological signals.
July 25, 2012 6
7. Fourier based signal processing
we arrange our signals such that the signals and any
noise overlap as little as possible in the frequency
domain and linear time-invariant filtering will
approximately separate them.
This linear filtering approach cannot separate noise
from signal where their Fourier spectra overlap.
overlap
July 25, 2012 7
8. Motivation
Non-linear method
The spectra can overlap.
The idea is to have the amplitude, rather than the
location of the spectra be as different as possible
for that of the noise.
This allows shrinking of the amplitude of the
transform to separate signals or remove noise.
July 25, 2012 8
10. Fourier filtering –
Spline method - suppresses
leaves features sharp
noise, by broadening,
but doesn’t really
erasing certain features suppress the noise
July 25, 2012 10
denoised
11. Here we use Haar-basis
shrinkage method
original
July 25, 2012 11
12. Wh y wavelets?
The Wavelet transform performs a correlation
analysis, therefore the output is expected to be
maximal when the input signal most resembles the
mother wavelet.
If a signal has its energy concentrated in a small
number of WL dimensions, its coefficients will be
relatively large compared to any other signal or noise
that its energy spread over a large number of
coefficients
Localizing properties +
July 25, 2012
concentration 12
13. This means that shrinking the WL transform will
remove the low amplitude noise or undesired
signal in the WL domain, and an inverse wavelet
transform will then retrieve the desired signal with
little loss of details
Usually the same properties that make a system
good for denoising or separation by non linear
methods makes it good for compression, which is
also a nonlinear process
July 25, 2012 13
14. In today’s show
Denoising – definition
Denoising using wavelets vs. other methods
Denoising process
Soft/Hard thresholding
Known thresholds
Examples and comparison of denoising methods
using WL
Advanced applications
2 different simulations
Summary
July 25, 2012 14
15. Noise (especially white
one)
Wavelet denoising works for additive noise since wavelet transform is linear
W ( a , b ) [ f + η ;ψ ] = W ( a, b ) [ f ;ψ ] + W ( a, b )[ η; ψ]
White noise means the noise values are not correlated in time
Whiteness means noise has equal power at all frequencies.
Considered the most difficult to remove, due to the fact that it
affects every single frequency component over the whole
length of the signal.
July 25, 2012 15
17. Goal : recover x
In the Transformation Domain:
where: Wy = Y (W transform matrix).
ˆ ˆ
X estimate of X from Y , x estimate of x from y
Define diagonal linear projection: ˆ
X = ∆Y
July 25, 2012 17
18. We define the risk measure :
ˆ
R ( X , X ) = E[|| x − x ||2 ] =
ˆ 2
ˆ ˆ
E[|| W −1 ( X − X ) ||2 ] = E[|| X − X ||2 ] =
2 2
|| Yi − X i ||2 =|| N i ||2 ,
2 2 X i >ε
E[|| ∆Y − X ||2 ] =
2
|| 0 − X i ||2 =|| X i ||2 ,
2 2 X i <ε
δi =1xi >ε
N
Rid ( X , X ) = ∑min( X 2 ,ε 2 )
ˆ
n=1
Is the lower limit of l 2 error
18 July 25, 2012
19. 3 step general method
1. Decompose signal using DWT;
Choose wavelet and number of decomposition levels.
Compute Y=Wy
2. Perform thresholding in the Wavelet domain.
Shrink coefficients by thresholding (hard /soft)
3. Reconstruct the signal from thresholded DWT
coefficients
Compute
July 25, 2012 19
20. Questions
Which thresholding method?
Which threshold?
Do we pick a single threshold or pick different
thresholds at different levels?
July 25, 2012 20
21. In today’s show
Denoising – definition
Denoising using wavelets vs. other methods
Denoising process
Soft/Hard thresholding
Known thresholds
Examples and comparison of denoising methods
using WL
Advanced applications
2 different simulations
Summary
July 25, 2012 21
24. Sof t Thresholding
sgn ( x( t ) ) ⋅ ( x(t ) − δ ), | x(t ) |> δ
ysoft (t ) =
0, | x(t ) |< δ
July 25, 2012 24
25. Sof t Or Hard threshold?
It is known that soft thresholding provides smoother
results in comparison with the hard thresholding.
More visually pleasant images, because it is
continuous.
Hard threshold, however, provides better edge
preservation in comparison with the soft one.
Sometimes it might be good to apply the soft
threshold to few detail levels, and the hard to the rest.
July 25, 2012 25
27. Edges aren’t kept.
However, the noise
was almost fully
suppressed
Edges are kept, but the
noise wasn’t fully
suppressed
July 25, 2012 27
28. In today’s show
Denoising – definition
Denoising using wavelets vs. other methods
Denoising process
Soft/Hard thresholding
Known thresholds
Examples and comparison of denoising methods
using WL
Advanced applications
2 different simulations
Summary
July 25, 2012 28
29. Known sof t thresholds
VisuShrink (Universal Threshold)
Donoho and Johnstone developed this method
Provides easy, fast and automatic thresholding.
Shrinkage of the wavelet coefficients is calculated using the formula
No need to calculate λ
foreach level (sub-band)!!
σ is the standard deviation of the noise of the noise level
n is the sample size.
July 25, 2012 29
30. The rational is to remove all wavelet coefficients that are smaller than the
expected maximum of an assumed i.i.d normal noise sequence of sample
size n.
It can be shown that if the noise is a white noise zi i.i.d N(0,1)
Probablity {maxi |zi| >(2logn)1/2} 0, n ∞
July 25, 2012 30
31. SureShrink
A threshold level is assigned to each resolution level of the
wavelet transform. The threshold is selected by the
principle of minimizing the Stein Unbiased Estimate of
Risk (SURE).
min
where d is the number of elements in the noisy data
vector and xi are the wavelet coefficients. This procedure
is smoothness-adaptive, meaning that it is suitable for
denoising a wide range of functions from those that have
many jumps to those that are essentially smooth.
July 25, 2012 31
32. If the unknown function contains jumps, the
reconstruction (essentially) does also;
if the unknown function has a smooth piece, the
reconstruction is (essentially) as smooth as the
mother wavelet will allow.
The procedure is in a sense optimally smoothness-
adaptive: it is near-minimax simultaneously over a
whole interval of the Besov scale; the size of this
interval depends on the choice of mother wavelet.
July 25, 2012 32
33. Estimating the Noise Level
In the threshold selection methods it may be
necessary to estimate the standard deviation σ of the noise from the
wavelet coefficients. A common estimator is shown below:
where MAD is the median of the absolute values of the
.wavelet coefficients
July 25, 2012 33
34. In today’s show
Denoising – definition
Denoising using wavelets vs. other methods
Denoising process
Soft/Hard thresholding
Known thresholds
Examples and comparison of denoising methods
using WL
Advanced applications
2 different simulations
Summary
July 25, 2012 34
38. Denoised
signals
Soft threshold
38 July 25, 2012
39. The reconstructions have two properties:
1. The noise has been almost entirely suppressed
2. Features sharp in the original remain sharp in
reconstruction
July 25, 2012 39
40. Why it works (I)
Data compression
Here we use Haar-basis shrinkage method
July 25, 2012 40
41. The Haar transform of the noiseless object Blocks
compresses the l2 energy of the signal into a very
small number of consequently) very large
coefficients.
On the other hand, Gaussian white noise in any one
orthogonal basis is again a white noise in any other.
In the Haar basis, the few nonzero signal coefficients
really stick up above the noise
the thresholding kills the noise while not killing the
signal
July 25, 2012 41
42. Formal:
Data: di = θi + εzi , i=1,…,n
zi standard white noise
Goal : recovering θi
Ideal diagonal projector : keep all coefficients
where θi is larger in amplitude than ε and ‘kill’ the
rest.
The ideal is unattainable since it requires
knowledge on θ which we don’t know
July 25, 2012 42
43. The ideal mean square error is
Define the “compression number“ cn as follows.
number
With |θ|(k) = k-th largest amplitude in vector θi set
This is a measure of how well the vector θi can
approximated by a vector with n nonzero entries.
July 25, 2012 43
44. Setting
so this ideal risk is explicitly a measure of the
extent to which the energy is compressed into a
few big coefficients.
July 25, 2012 44
45. We will see the extend to which the different orthogonal basses
compress the objects
db
db fourier
Haar
Haar fourier
July 25, 2012 45
46. Another aspect -
Vanishing Moments
The m moment of a wavelet is defined as
th ∫ t mψ (t )dt
If the first M moments of a wavelet are zero, then all
polynomial type signals of the form x(t ) = ∑cmt m
0 <m <M
have (near) zero wavelet / detail coefficients.
Why is this important? Because if we use a wavelet with
enough number of vanishing moments, M, to analyze a
polynomial with a degree less than M, then all detail
coefficients will be zero excellent compression ratio.
All signals can be written as a polynomial when expanded
into its Taylor series.
This is what makes wavelets so successful in compression!!!
July 25, 2012 46
47. Why it works?(II)
Unconditional basis
A very special feature of wavelet bases is that they
serve as unconditional bases, not just of L2, but of
a wide range of smoothness spaces, including
Sobolev and HÖlder classes.
As a consequence, “shrinking" the coefficients of
an object towards zero, as with soft thresholding,
acts as a “smoothing operation" in any of a wide
range of smoothness measures.
Fourier basis isn’t such basis
July 25, 2012 47
48. Original singal
Denoising using the 100
biggest WL coefficients
Denoising using the 100
biggest Fourier
coefficients
48 Worst MSE+ visual artifacts!! July 25, 2012
49. In today’s show
Denoising – definition
Denoising using wavelets vs. other methods
Denoising process
Soft/Hard thresholding
Known thresholds
Examples and comparison of denoising methods
using WL
Advanced applications
2 different simulations
Summary
July 25, 2012 49
50. Advanced applications
Discrete inverse problems
Assume : yi = (Kf)(ti) + εzi
Kf is a transformation of f (Fourier transformation,
laplace transformation or convolution)
Goal : reconstruct the singal ti
Such problems become problems of recovering
wavelets coefficients in the presence of non-white
noise
July 25, 2012 50
51. Example :
we want to reconstruct the discrete signal (xi)i=0..n-1, given
the noisy data :
White gaussian noise
We may attempt to invert this relation, forming the differences :
yi = di – di-1, y0 = d0
This is equivalent to observing
yi = xi + σ(zi – zi-1) (non white noise)
July 25, 2012 51
52. Solution : reconstructing xi in three-step process, with
level-dependent threshold.
The threshold is much larger at high resolution levels
than at low ones (j0 is the coarse level. J is the finest)
Motivation : the variance of the noise in level j grows roughly
like 2j
The noise is heavily damped, while the main structure of the
object persists
July 25, 2012 52
53. 53 WL denoising method supresses the noise!! 2012
July 25,
55. In today’s show
Denoising – definition
Denoising using wavelets vs. other methods
Denoising process
Soft/Hard thresholding
Known thresholds
Examples and comparison of denoising methods
using WL
Advanced applications
2 different simulations
Summary
July 25, 2012 55
56. Monte Carlo simulation
The Monte Carlo method (or simulation) is a
statistical method for finding out the answer to a
problem that is too difficult to solve analytically,
or for verifying the analytical solution.
It Randomly generates values for uncertain
variables over and over to simulate a model
It is called Monte Carlo because of the gambling
casinos in that city, and because the Monte Carlo
method is related to rolling dice.
July 25, 2012 56
57. We will describe a variety of wavelet and
wavelet packet based denoising methods and
compare them with each other by applying
them to a simulated, noised signal
f is a known signal. The noise is a free
parameter
The results help us choose the best wavelet,
best denoising method and a suitable denoising
threshold in pratictical applications.
July 25, 2012 57
58. A noised singal ƒi i=0,…,2jmax-1
Wavelet
Wavelet pkt
July 25, 2012 58
59. Denoising methods
Linear – Independent on the size of the signal coefficients.
Therefore the coefficient size isn’t taken into account, but the
scale of the coefficient. It is based on the assumption that signal
noise can be found mainly in fine scale coefficients and not in
coarse ones. Therefore we will cut off all coefficients with a
scale finer that a certain scale threshold S0.
WL
July 25, 2012 59
60. In packet wavelets, fine scaled signal structures can
be represented not only by fine scale coefficients but
also by coarse scale coefficients with high
frequency. Therefore, it is necessary to eliminate not
only fine scale coefficients through linear denoising,
but also coefficients of a scale and frequency
combination which refer to a certain fine scale
structure.
PL
July 25, 2012 60
61. Non linear – cutting of the coefficients (hard or soft), threshold = λ
July 25, 2012 61
63. Choosing the best
threshold and basis
Using Monte Carlo simulation DB with 3 vanishing
moments has been chosen for PNLS method.
Min Error
July 25, 2012 63
64. Threshold – universal soft threshold
For normally distibuted noise, λu = 0.008
However, it seems that λu lies above the optimal
. threshold
Using monte carlo to evaluate the ‘best’ threshold
for PNLS, 0.003 is the best
July 25, 2012 Min error 64
65. For each method a best basis and an optimal
threshold is collected using Monte Carlo
simulations.
Now we are ready to compare!
The comparison reveals that WNLH has the best
denoising performance.
We would expect wavelet packets method to have
the best performance. It seems that for this specific
signal, even with Donoho best cost function, this
method isn’t the optimal.
July 25, 2012 65
66. !!Best
DJ WP
close
to the
!!Best
66 July 25, 2012
67. Improvements
Even with the minimal denoising error, there are small
artifacts.
Original Denoised
July 25, 2012 67
68. • Solution : the artifacts live only on fine scales,
we can adapt λ to the scale j λ j = λ * µj
Most coarse scale Artifacts have disappeared!
Finest scale
68 July 25, 2012
69. Thresholds experiment
In this experiment, 6 known signals were taken at
n=1024 samples.
Additive white noise (SNR = 10dB)
The aim – to compare all thresholds performance
in comparison to the ideal thresholds.
RIS, VIS – global threshold which depends on n.
SUR – set for each level
WFS, FFS – James thresholds (WL, Fourier)
IFD, IWD – ideal threshold (if we knew noise
level)
July 25, 2012 69
74. Results
Surprising, isn’t it?
VIS is the worst for all the signals.
Fourier is better?
What about the theoretical claims of optimality
and generality?
We use SNR to measure error rates
Maybe should it be judged visually by the human
eye and mind?
July 25, 2012 74
75. [DJ] In this case, VIS performs best.
July 25, 2012 75
76. Denoising Implementation
in Matlab
First,
analyze the
signal with
appropriate
wavelets
Hit
Denoise
July 25, 2012 76
77. Choose
thresholding
method
Choose
noise type
Choose
thresholds
Hit
Denoise
July 25, 2012 77
79. In today’s show
Denoising – definition
Denoising using wavelets vs. other methods
Denoising process
Soft/Hard thresholding
Known thresholds
Examples and comparison of denoising methods
using WL
Advanced applications
2 different simulations
Summary
July 25, 2012 79
80. Summary
We learn how to use wavelets for denoising
We saw different denoising methods and their
results
We saw other uses of wavelets denoising to solve
discrete problems
We saw experiments and results
July 25, 2012 80
82. Bibliogr aphy
Nonlinear Wavelet Methods for Recovering Signals, Images, and
Densities from indirect and noisy data [D94]
Filtering (Denoising) in the Wavelet Transform Domain Yousef M.
Hawwar, Ali M. Reza, Robert D. Turney
Comparison and Assessment of Various Wavelet and Wavelet Packet
based Denoising Algorithms for Noisy Data F. Hess, M. Kraft, M.
Richter, H. Bockhorn
De-Noising via Soft-Thresholding, Tech. Rept., Statistics, Stanford,
1992.
Adapting to unknown smoothness by wavelet shrinkage, Tech. Rept.,
Statistics, Stanford, 1992. D. L. Donoho and I. M. Johnstone
Denoising by wavelet transform [Junhui Qian]
Filtering denoising in the WL transform domain[Hawwr,Reza,Turney]
The What,how,and why of wavelet shrinkage denoising[Carl Taswell,
2000]
July 25, 2012 82