This document summarizes a seminar presentation on an image denoising method based on the curvelet transform. The presentation covered:
1) How image noise occurs and traditional denoising methods like linear filters and edge-preserving smoothing.
2) The curvelet transform process including sub-band decomposition, smooth partitioning, renormalization, and ridgelet analysis.
3) An image denoising algorithm that applies wavelet and curvelet transforms, then combines results using quad tree decomposition.
2017 Spring, UCF Medical Image Computing
CAVA: Computer Aided Visualization and Analysis
• CAD: Computer Aided Diagnosis
• Definitions and Terminologies
• Coordinate Systems
• Pre-Processing Images – Volume of Interest
– RegionofInterest
– IntensityofInterest – ImageEnhancement
• Filtering
• Smoothing
• Introduction to Medical Image Computing and Toolkits • Image Filtering, Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology
Fractal compression is a lossy compression method for digital images, based on fractals. The method is best suited for textures and natural images, relying on the fact that parts of an image often resemble other parts of the same image.[citation needed] Fractal algorithms convert these parts into mathematical data called "fractal codes" which are used to recreate the encoded image.
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.
Image Acquisition and Representation
A Simple Image Formation Model
Image Sampling and Quantization
Image Interpolation
Image quantization
Nearest Neighbor Interpolation
Talk @ ACM SF Bayarea Chapter on Deep Learning for medical imaging space.
The talk covers use cases, special challenges and solutions for Deep Learning for Medical Image Analysis using Tensorflow+Keras. You will learn about:
- Use cases for Deep Learning in Medical Image Analysis
- Different DNN architectures used for Medical Image Analysis
- Special purpose compute / accelerators for Deep Learning (in the Cloud / On-prem)
- How to parallelize your models for faster training of models and serving for inferenceing.
- Optimization techniques to get the best performance from your cluster (like Kubernetes/ Apache Mesos / Spark)
- How to build an efficient Data Pipeline for Medical Image Analysis using Deep Learning
- Resources to jump start your journey - like public data sets, common models used in Medical Image Analysis
Histogram equalization is a method in image processing of contrast adjustment using the image's histogram. Histogram equalization can be used to improve the visual appearance of an image. Peaks in the image histogram (indicating commonly used grey levels) are widened, while the valleys are compressed.
2017 Spring, UCF Medical Image Computing
CAVA: Computer Aided Visualization and Analysis
• CAD: Computer Aided Diagnosis
• Definitions and Terminologies
• Coordinate Systems
• Pre-Processing Images – Volume of Interest
– RegionofInterest
– IntensityofInterest – ImageEnhancement
• Filtering
• Smoothing
• Introduction to Medical Image Computing and Toolkits • Image Filtering, Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology
Fractal compression is a lossy compression method for digital images, based on fractals. The method is best suited for textures and natural images, relying on the fact that parts of an image often resemble other parts of the same image.[citation needed] Fractal algorithms convert these parts into mathematical data called "fractal codes" which are used to recreate the encoded image.
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.
Image Acquisition and Representation
A Simple Image Formation Model
Image Sampling and Quantization
Image Interpolation
Image quantization
Nearest Neighbor Interpolation
Talk @ ACM SF Bayarea Chapter on Deep Learning for medical imaging space.
The talk covers use cases, special challenges and solutions for Deep Learning for Medical Image Analysis using Tensorflow+Keras. You will learn about:
- Use cases for Deep Learning in Medical Image Analysis
- Different DNN architectures used for Medical Image Analysis
- Special purpose compute / accelerators for Deep Learning (in the Cloud / On-prem)
- How to parallelize your models for faster training of models and serving for inferenceing.
- Optimization techniques to get the best performance from your cluster (like Kubernetes/ Apache Mesos / Spark)
- How to build an efficient Data Pipeline for Medical Image Analysis using Deep Learning
- Resources to jump start your journey - like public data sets, common models used in Medical Image Analysis
Histogram equalization is a method in image processing of contrast adjustment using the image's histogram. Histogram equalization can be used to improve the visual appearance of an image. Peaks in the image histogram (indicating commonly used grey levels) are widened, while the valleys are compressed.
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).
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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.
Gracheva Inessa - Fast Global Image Denoising Algorithm on the Basis of Nonst...AIST
Gracheva Inessa, Kopylov Andrey, Krasotkina Olga,
(Tula State University, Tula, Russia) - Fast Global Image Denoising Algorithm on the Basis of Nonstationary Gamma-Normal Statistical Model
AIST 2015 Conference
Two Dimensional Image Reconstruction Algorithmsmastersrihari
Convolution Back-Projection (CBP) Algorithm was used for the reconstruction of the image. The performance was compared by implementing the algorithm by using RAM- LAK filter, Shepp- Logan filter and also No filter being used.
ER Publication,
IJETR, IJMCTR,
Journals,
International Journals,
High Impact Journals,
Monthly Journal,
Good quality Journals,
Research,
Research Papers,
Research Article,
Free Journals, Open access Journals,
erpublication.org,
Engineering Journal,
Science Journals,
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Non-Blind Deblurring Using Partial Differential Equation MethodEditor IJCATR
In this paper, a new idea for two dimensional image deblurring algorithm is introduced which uses basic concepts of PDEs... The various methods to estimate the degradation function (PSF is known in prior called non-blind deblurring) for use in restoration are observation, experimentation and mathematical modeling. Here, PDE based mathematical modeling is proposed to model the degradation and recovery process. Several restoration methods such as Weiner Filtering, Inverse Filtering [1], Constrained Least Squares, and Lucy -Richardson iteration remove the motion blur either using Fourier Transformation in frequency domain or by using optimization techniques. The main difficulty with these methods is to estimate the deviation of the restored image from the original image at individual points that is due to the mechanism of these methods as processing in frequency domain .Another method, the travelling wave de-blurring method is a approach that works in spatial domain.PDE type of observation model describes well several physical mechanisms, such as relative motion between the camera and the subject (motion blur), bad focusing (defocusing blur), or a number of other mechanisms which are well modeled by a convolution. In last PDE method is compared with the existing restoration techniques such as weiner filters, median filters [2] and the results are compared on the basis of calculated PSNR for various noises
ANALYSIS OF INTEREST POINTS OF CURVELET COEFFICIENTS CONTRIBUTIONS OF MICROS...sipij
This paper focuses on improved edge model based on Curvelet coefficients analysis. Curvelet transform is
a powerful tool for multiresolution representation of object with anisotropic edge. Curvelet coefficients
contributions have been analyzed using Scale Invariant Feature Transform (SIFT), commonly used to study
local structure in images. The permutation of Curvelet coefficients from original image and edges image
obtained from gradient operator is used to improve original edges. Experimental results show that this
method brings out details on edges when the decomposition scale increases.
Adaptive lifting based image compression scheme using interactive artificial ...csandit
This paper presents image compression method using Interactive Artificial Bee Colony (IABC) optimization algorithm. The proposed method reduces storage and facilitates data transmission by reducing transmission costs. To get the finest quality of compressed image, utilizing local search, IABC determines different update coefficient, and the best update coefficient is chosen
optimally. By using local search in the update step, we alter the center pixels with the coefficient in 8-different directions with a considerable window size, to produce the compressed image, expressed in terms of both PSNR and compression ratio. The IABC brings in the idea of
universal gravitation into the consideration of the affection between onlooker bees and the employed bees. By passing on different values of the control parameter, the universal gravitation involved in the IABC has various quantities of the single onlooker bee and employed bees. As a result when compared to existing methods, the proposed work gives better PSNR.
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.
Digital Signal and Image Processing - FAQ
BE -Sem 7, University of Mumbai
Frequently asked questions in BE Sem 7 examinations of University of Mumbai, with marks for each question, month and year of exam.
WAVELET BASED AUTHENTICATION/SECRET TRANSMISSION THROUGH IMAGE RESIZING (WA...sipij
The paper is aimed for a wavelet based steganographic/watermarking technique in frequency domain
termed as WASTIR for secret message/image transmission or image authentication. Number system
conversion of the secret image by changing radix form decimal to quaternary is the pre-processing of the
technique. Cover image scaling through inverse discrete wavelet transformation with false Horizontal and
vertical coefficients are embedded with quaternary digits through hash function and a secret key.
Experimental results are computed and compared with the existing steganographic techniques like WTSIC,
Yuancheng Li’s Method and Region-Based in terms of Mean Square Error (MSE), Peak Signal to Noise
Ratio (PSNR) and Image Fidelity (IF) which show better performances in WASTIR.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Performance Evaluation of Quarter Shift Dual Tree Complex Wavelet Transform B...IJECEIAES
In this paper, multifocus image fusion using quarter shift dual tree complex wavelet transform is proposed. Multifocus image fusion is a technique that combines the partially focused regions of multiple images of the same scene into a fully focused fused image. Directional selectivity and shift invariance properties are essential to produce a high quality fused image. However conventional wavelet based fusion algorithms introduce the ringing artifacts into fused image due to lack of shift invariance and poor directionality. The quarter shift dual tree complex wavelet transform has proven to be an effective multi-resolution transform for image fusion with its directional and shift invariant properties. Experimentation with this transform led to the conclusion that the proposed method not only produce sharp details (focused regions) in fused image due to its good directionality but also removes artifacts with its shift invariance in order to get high quality fused image. Proposed method performance is compared with traditional fusion methods in terms of objective measures.
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Narrated Business Proposal for the Philadelphia Eaglescamrynascott12
Slide 1:
Welcome, and thank you for joining me today. We will explore a strategic proposal to enhance parking and traffic management at Lincoln Financial Field, aiming to improve the overall fan experience and operational efficiency. This comprehensive plan addresses existing challenges and leverages innovative solutions to create a smoother and more enjoyable experience for our fans.
Slide 2:
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Slide 3:
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1. Seminar
on
“Image Denoising Method based on Curvelet
Transform”
Master of Engineering
(Electronics and Communication )
Year 2011-12.
Rajput Sandeep Kumar Jawaharlal (100370704036)
Prepared By: Guided By:
Rajput Sandeep J Prof. A.R. Yadav
ME (EC-213) Professor , EC Dept.
PIET, Limda. PIET, Limda.
2. Introduction
Image acquired through sensors charge coupled device (CCD)
cameras may be influenced by noise sources.
Image processing technique also corrupts image with noise,
leading to significant reduction in quality.
Traditionally,
Linear filters
Edge preserving smoothing algorithm
New Methods,
Non-linear techniques : Wavelet Transform
: Curvelet Transform
6. Smooth Partitioning
The windowing function w is a nonnegative smooth
function.
Partition of the intensity:
The intensity of certain pixel (x1,x2) is divided between
all sampling windows of the grid.
( ) 1,
21,
2211
2
≡−−∑kk
kxkxw
7. Ridgelet are an orthonormal set {ρλ} for L2
(R2
).
Ridgelet Analysis
2-s
2-2s
1
2-s
2s
radius 2s
2s
divisions
Ridge in Square It’s Fourier TransformRidge in Square
Ridgelet Tiling
Fourier Transform
within Tiling
8. Ridgelet Analysis
The ridgelet element in the frequency domain:
where,
ωi,l are periodic wavelets for [-π, π ).
i is the angular scale.
ψj,k are wavelets for R.
j is the ridgelet scale and k is the ridgelet location.
( ) ( ) ( ) ( ) ( )( )πθωψθωψρ likjlikjλ +⋅−+⋅=
−
,,,,2
1 ξˆξˆξξˆ 2
1
9. Curvelet Transform
The four stages of the Curvelet Transform were:
Sub-band decomposition
Smooth partitioning
Renormalization
Ridgelet analysis
( ) ,,, 210 fffPf ∆∆
fwh sQQ ∆⋅=
QQQ hTg
1−
=
( ) λQQ,λ ρgα ,=
10. Image Reconstruction
The Inverse of the Curvelet Transform:
Ridgelet Synthesis
Renormalization
Smooth Integration
Sub-band Recomposition
( ) λ
λ
Q,λQ ραg ⋅= ∑
QQQ gTh =
∑∈
⋅=∆
sQ
QQs hwf
Q
( ) ( )∑ ∆∆+=
s
ss ffPPf 00
11. Thresholding methods
Window Shrink Method
Set di, j is the parameter which is from curvelet transformed
noise image; choose a di, j centered window of n×n as the
processing subject.
3X 3 Window Shrink
The curvelet coefficients
to be thresholded
12. Set Symbolic function:
σ is the variance of Gaussian white noise in the image , then
shrinking processing parameter is
Then the thresholded parameter can be calculated as:
Thresholding methods
The sum of all the parameter’s square in the n×n window is
calculated.
13. Bayes Shrink method
Thresholding methods
In this method σ2
D
is the variance of an image containing
noise, σ2
is the variance of noise, and σ2
X
is the original image’s
variance.
Now, noise variance is:
The variance of original image is calculated by,
Setting Threshold is σ2
/ σ2
X
then begin the processing of
removing noise.
14. Combination of Window shrink and Bayes shrink
The variance σ2
X
is estimated of the original picture using Bayes
shrink theory, then η is calculated using σ2
X
instead of the noise
variance σ 2
,such as
At last shrink factors αi, j
are known and the noise coefficient is
filtered out by taking advantage of αi, j
.
Thresholding methods
x
17. Image denoising Algorithm
Quad tree Decomposition algorithm
Now, The Q(x,y) that define the matrix
of mxm image and S(vi) denote the
element of the Q(x,y) where vi denote
the number of decomposition required
for that element.
18. Image denoising Algorithm
Algorithm :
Denote result image of improved algorithm as R, this pixel
fusion based algorithm is described as follows.
Applying wavelet transform to obtain result image W.
Applying curvelet transform to obtain result image C.
Get quad tree matrix Q with applying quad tree decomposition
to C.
R(x, y) is calculated as
R(x, y) = cW(x, y) + dC(x, y)
Where,
22. Conclusion
To overcome the disadvantages of the wavelet
transform along the curves in the images the curvelet
transform is used and it gives high PSNR.
A new method of combination of the Window Shrink
and Bayes Shrink based on Curvelet transform is used to
remove noise from image. It has better PSNR. So the
image we get by this method is better and that of the
traditional wavelet methods.
23. References
i. Introduction to Wavelet: Bhushan D Patil PhD Research Scholar Department of Electrical Engineering Indian Institute
of Technology, Bombay.
ii. Pixel Fusion Based Curvelets and Wavelets Denoise Algorithm, Liyong Ma, Member, IAENG, Jiachen Ma and Yi
Shen Advance online publication: 16 May 2007
iii. The Curvelet Transform - Jean-Luc Starck, Emmanuel J. Candès, and David L. Donoho IEEE transactions on
image processing, vol. 11, no. 6, june 2002.
iv. Image denoising using wavelet transform: an approach for edge Preservation Received 03 March 2009; revised 24
November 2009; accepted 25 November 2009
v. Image Denoising Method Based on Curvelet Transform -University of Science and Technology, IEEE transactions on
image processing, vol. 11, no. 6, june 2008.
vi. New Method Based on Curvelet Transform for Image Denoising Donglei Li, Zhemin Duan, Meng Jia
vii. Department of Electronics and Information Northwestern Polytechnical University, China, 2010 International
Conference on Measuring Technology and Mechatronics Automation
viii. Improved Image Denoising Method based on Curvelet Transform Proceedings of the 2010 IEEE International
Conference on Information and Automation June 20 - 23, Harbin, China
ix. Image Denoising Based on Curvelet Transform and Continuous Threshold YUAN Ruihong TANG Liwei WANG Ping
YAO Jiajun Department of Artillery Engineering Ordnance Engineering College Shijiazhuang ,China, 2010 First
International Conference on Pervasive Computing, Signal Processing and Applications.