This document discusses contrast enhancement of satellite images using discrete wavelet transform and singular value decomposition. It provides background on contrast and techniques like histogram equalization. It then describes discrete wavelet transform and singular value decomposition, their applications, advantages, and uses. The document concludes that a new technique was proposed combining DWT and SVD for image equalization, which showed better results than conventional techniques in experiments.
Satellite Image Resolution Enhancement Technique Using DWT and IWTEditor IJCATR
Now a days satellite images are widely used In many applications such as astronomy and
geographical information systems and geosciences studies .In this paper, We propose a new satellite image
resolution enhancement technique which generates sharper high resolution image .Based on the high
frequency sub-bands obtained from the dwt and iwt. We are not considering the LL sub-band here. In this
resolution-enhancement technique using interpolated DWT and IWT high-frequency sub band images and the
input low-resolution image. Inverse DWT (IDWT) has been applied to combine all these images to generate
the final resolution-enhanced image. The proposed technique has been tested on satellite bench mark images.
The quantitative (peak signal to noise ratio and mean square error) and visual results show the superiority of
the proposed technique over the conventional method and standard image enhancement technique WZP.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Satellite Image Resolution Enhancement Technique Using DWT and IWTEditor IJCATR
Now a days satellite images are widely used In many applications such as astronomy and
geographical information systems and geosciences studies .In this paper, We propose a new satellite image
resolution enhancement technique which generates sharper high resolution image .Based on the high
frequency sub-bands obtained from the dwt and iwt. We are not considering the LL sub-band here. In this
resolution-enhancement technique using interpolated DWT and IWT high-frequency sub band images and the
input low-resolution image. Inverse DWT (IDWT) has been applied to combine all these images to generate
the final resolution-enhanced image. The proposed technique has been tested on satellite bench mark images.
The quantitative (peak signal to noise ratio and mean square error) and visual results show the superiority of
the proposed technique over the conventional method and standard image enhancement technique WZP.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Discrete cosine transform (DCT) is a widely used tool in image and video compression applications. Recently, the high-throughput DCT designs have been adopted to fit the requirements of real-time application.
Operating the shifting and addition in parallel, an error-compensated adder-tree (ECAT) is proposed to deal with the truncation errors and to achieve low-error and high-throughput discrete cosine transform (DCT) design. Instead of the 12 bits used in previous works, 9-bit distributed arithmetic. DA-based DCT core with an error-compensated adder-tree (ECAT). The proposed ECAT operates shifting and addition in parallel by unrolling all the words required to be computed. Furthermore, the error-compensated circuit alleviates the truncation error for high accuracy design. Based on low-error ECAT, the DA-precision in this work is chosen to be 9 bits instead of the traditional 12 bits. Therefore, the hardware cost is reduced, and the speed is improved using the proposed ECAT.
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
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.
Comparative Analysis of Dwt, Reduced Wavelet Transform, Complex Wavelet Trans...ijsrd.com
Image denoising is the process to remove the noise from the image naturally corrupted by the noise. The wavelet method is one among various methods for recovering infinite dimensional objects like curves, densities, images, etc. The wavelet techniques are very effective to remove the noise because of their ability to capture the energy of a signal in few energy transform values. Though the wavelet transform have the best bases when it represents target functions which has dot singularity, it can hardly get the best bases when it present the singularity of line and hyper-plane. This makes the traditional two-dimensional wavelet transform in dealing with the image have some limitations. To overcome the above-mentioned shortcomings of Wavelet transform the theory of Curvelet transform was promoted.
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.
Discrete cosine transform (DCT) is a widely used tool in image and video compression applications. Recently, the high-throughput DCT designs have been adopted to fit the requirements of real-time application.
Operating the shifting and addition in parallel, an error-compensated adder-tree (ECAT) is proposed to deal with the truncation errors and to achieve low-error and high-throughput discrete cosine transform (DCT) design. Instead of the 12 bits used in previous works, 9-bit distributed arithmetic. DA-based DCT core with an error-compensated adder-tree (ECAT). The proposed ECAT operates shifting and addition in parallel by unrolling all the words required to be computed. Furthermore, the error-compensated circuit alleviates the truncation error for high accuracy design. Based on low-error ECAT, the DA-precision in this work is chosen to be 9 bits instead of the traditional 12 bits. Therefore, the hardware cost is reduced, and the speed is improved using the proposed ECAT.
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
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.
Comparative Analysis of Dwt, Reduced Wavelet Transform, Complex Wavelet Trans...ijsrd.com
Image denoising is the process to remove the noise from the image naturally corrupted by the noise. The wavelet method is one among various methods for recovering infinite dimensional objects like curves, densities, images, etc. The wavelet techniques are very effective to remove the noise because of their ability to capture the energy of a signal in few energy transform values. Though the wavelet transform have the best bases when it represents target functions which has dot singularity, it can hardly get the best bases when it present the singularity of line and hyper-plane. This makes the traditional two-dimensional wavelet transform in dealing with the image have some limitations. To overcome the above-mentioned shortcomings of Wavelet transform the theory of Curvelet transform was promoted.
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.
Signal and image processing on satellite communication using MATLABEmbedded Plus Trichy
Basic Explanations about satellite imaging and signal processing with the help of MATLAB.
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Fourier Transform : Its power and Limitations – Short Time Fourier Transform – The Gabor Transform - Discrete Time Fourier Transform and filter banks – Continuous Wavelet Transform – Wavelet Transform Ideal Case – Perfect Reconstruction Filter Banks and wavelets – Recursive multi-resolution decomposition – Haar Wavelet – Daubechies Wavelet.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Image compression using embedded zero tree waveletsipij
Compressing an image is significantly different than compressing raw binary data. compressing images is
used by this different compression algorithm. Wavelet transforms used in Image compression methods to
provide high compression rates while maintaining good image quality. Discrete Wavelet Transform (DWT)
is one of the most common methods used in signal and image compression .It is very powerful compared to
other transform because its ability to represent any type of signals both in time and frequency domain
simultaneously. In this paper, we will moot the use of Wavelet Based Image compression algorithm-
Embedded Zerotree Wavelet (EZW). We will obtain a bit stream with increasing accuracy from ezw
algorithm because of basing on progressive encoding to compress an image into . All the numerical results
were done by using matlab coding and the numerical analysis of this algorithm is carried out by sizing
Peak Signal to Noise Ratio (PSNR) and Compression Ratio (CR) for standard Lena Image .Experimental
results beam that the method is fast, robust and efficient enough to implement it in still and complex images
with significant image compression.
Volumetric Medical Images Lossy Compression using Stationary Wavelet Transfor...Omar Ghazi
Abstract: The aim of the study is to reduce the size required for storage along with decreasing the bitrate and the
bandwidth for the process of sending and receiving the image. It also aims to decrease the time required for the
process as much as possible. This study proposes a novel system for efficient lossy volumetric medical image
compression using Stationary Wavelet Transform and Linde-Buzo-Gray for Vector Quantization. The system makes
use of a combination of Linde-Buzo-Gray vector quantization technique for lossy compression along with
Arithmetic coding and Huffman coding for lossless compression. The system proposed uses Stationary Wavelet
Transform and then compares the results obtained to Discrete Wavelet Transform, Lifting Wavelet Transform and
Discrete Cosine Transform at three decomposition levels. The system also compares the results obtained using
transforms with only Arithmetic Coding and Huffman Coding for Lossless Compression.The results show that the
system proposed outperforms the others.
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.
Design and Implementation of EZW & SPIHT Image Coder for Virtual ImagesCSCJournals
The main objective of this paper is to designed and implemented a EZW & SPIHT Encoding Coder for Lossy virtual Images. .Embedded Zero Tree Wavelet algorithm (EZW) used here is simple, specially designed for wavelet transform and effective image compression algorithm. This algorithm is devised by Shapiro and it has property that the bits in the bit stream are generated in order of importance, yielding a fully embedded code. SPIHT stands for Set Partitioning in Hierarchical Trees. The SPIHT coder is a highly refined version of the EZW algorithm and is a powerful image compression algorithm that produces an embedded bit stream from which the best reconstructed images. The SPIHT algorithm was powerful, efficient and simple image compression algorithm. By using these algorithms, the highest PSNR values for given compression ratios for a variety of images can be obtained. SPIHT was designed for optimal progressive transmission, as well as for compression. The important SPIHT feature is its use of embedded coding. The pixels of the original image can be transformed to wavelet coefficients by using wavelet filters. We have anaysized our results using MATLAB software and wavelet toolbox and calculated various parameters such as CR (Compression Ratio), PSNR (Peak Signal to Noise Ratio), MSE (Mean Square Error), and BPP (Bits per Pixel). We have used here different Wavelet Filters such as Biorthogonal, Coiflets, Daubechies, Symlets and Reverse Biorthogonal Filters .In this paper we have used one virtual Human Spine image (256X256).
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Image Compression using Combined Approach of EZW and LZWIJERA Editor
Image Processing is most popular and widely used technique. In this paper we had proposed a technique for image compression. Here the user can return the processed image to the original image without any loss. In this proposed technique the test images are processed through the image compression algorithm. Here we apply combined approach of EZW and LZW. This technique is implemented on different types of images like .bmp, .tiff,.dcm (medical images),binary images. Performance of the proposed technique can be evaluated compared to LBG techniques by the parameters like PSNR, Compression ratio and MSE
A Comprehensive lossless modified compression in medical application on DICOM...IOSR Journals
ABSTRACT : In current days, Digital Imaging and Communication in Medicine (DICOM) is widely used for
viewing medical images from different modalities, distribution and storage. Image processing can be processed
by photographic, optical and electronic means, because digital methods are precise, fast and flexible, image
processing using digital computers are the most common method. Image Processing can extract information,
modify pictures to improves and change their structure (image editing, composition and image compression
etc.). Image compression is the major entities of storage system and communication which is capable of
crippling disadvantages of data transmission and image storage and also capable of reducing the data
redundancy. Medical images are require to stored for future reference of the patients and their hospital findings
hence, the medical image need to undergo the process of compression before storing it. Medical images are
much important in the field of medicine, all these Medical image compression is necessary for huge database
storage in Medical Centre and medical data transfer for the purpose of diagnosis. Presently Discrete cosine
transforms (DCT), Run Length Encoding Lossless compression technique, Wavelet transforms (DWT), are the
most usefully and wider accepted approach for the purpose of compression. On basis of based on discrete
wavelet transform we present a new DICOM based lossless image compression method. In the proposed
method, each DICOM image stored in the data set is compressed on the basis of vertically, horizontally and
diagonally compression. We analyze the results from our study of all the DICOM images in the data set using
two quality measures namely PSNR and RMSE. The performance and comparison was made over each images
stored in the set of data set of DICOM images. This work is presenting the performance comparison between
input images (without compression) and after compression results for each images in the data set using DWT
method. Further the performance of DWT method with HAAR process is compared with 2D-DWT method using
the quality metrics of PSNR & RMSE. The performance of these methods for image compression has been
simulated using MATLAB.
Keywords: JPEG, DCT, DWT, SPIHT, DICOM, VQ, Lossless Compression, Wavelet Transform, image
Compression, PSNR, RMSE
A Review on Image Compression using DCT and DWTIJSRD
Image Compression addresses the matter of reducing the amount of data needed to represent the digital image. There are several transformation techniques used for data compression. Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) is mostly used transformation. The Discrete cosine transform (DCT) is a method for transform an image from spatial domain to frequency domain. DCT has high energy compaction property and requires less computational resources. On the other hand, DWT is multi resolution transformation. The research paper includes various approaches that have been used by different researchers for Image Compression. The analysis has been carried out in terms of performance parameters Peak signal to noise ratio, Bit error rate, Compression ratio, Mean square error. and time taken for decomposition and reconstruction.
Image compression using Hybrid wavelet Transform and their Performance Compa...IJMER
Images may be worth a thousand words, but they generally occupy much more space in hard disk, or
bandwidth in a transmission system, than their proverbial counterpart. Compressing an image is significantly
different than compressing raw binary data. Of course, general purpose compression programs can be used to
compress images, but the result is less than optimal. This is because images have certain statistical properties
which can be exploited by encoders specifically designed for them. Also, some of the finer details in the image
can be sacrificed for the sake of saving a little more bandwidth or storage space. Compression is the process of
representing information in a compact form. Compression is a necessary and essential method for creating
image files with manageable and transmittable sizes. The data compression schemes can be divided into
lossless and lossy compression. In lossless compression, reconstructed image is exactly same as compressed
image. In lossy image compression, high compression ratio is achieved at the cost of some error in reconstructed
image. Lossy compression generally provides much higher compression than lossless compression.
A High Performance Modified SPIHT for Scalable Image CompressionCSCJournals
In this paper, we present a novel extension technique to the Set Partitioning in Hierarchical Trees (SPIHT) based image compression with spatial scalability. The present modification and the preprocessing techniques provide significantly better quality (both subjectively and objectively) reconstruction at the decoder with little additional computational complexity. There are two proposals for this paper. Firstly, we propose a pre-processing scheme, called Zero-Shifting, that brings the spatial values in signed integer range without changing the dynamic ranges, so that the transformed coefficient calculation becomes more consistent. For that reason, we have to modify the initialization step of the SPIHT algorithms. The experiments demonstrate a significant improvement in visual quality and faster encoding and decoding than the original one. Secondly, we incorporate the idea to facilitate resolution scalable decoding (not incorporated in original SPIHT) by rearranging the order of the encoded output bit stream. During the sorting pass of the SPIHT algorithm, we model the transformed coefficient based on the probability of significance, at a fixed threshold of the offspring. Calling it a fixed context model and generating a Huffman code for each context, we achieve comparable compression efficiency to that of arithmetic coder, but with much less computational complexity and processing time. As far as objective quality assessment of the reconstructed image is concerned, we have compared our results with popular Peak Signal to Noise Ratio (PSNR) and with Structural Similarity Index (SSIM). Both these metrics show that our proposed work is an improvement over the original one.
Image Resolution Enhancement Using Undecimated Double Density Wavelet TransformCSCJournals
In this paper, an undecimated double density wavelet based image resolution enhancement technique is proposed. The critically sampled discrete wavelet transform (DWT) suffers from the drawbacks of being shift-variant and lacking the capacity to process directional information in images. The double density wavelet transform (DDWT) is an approximately shift-invariant transform capturing directional information. The undecimated double density wavelet transform (UDDWT) is an improvement of the DDWT, making it exactly shift-invariant. The method uses a forward and inverse UDDWT to construct a high resolution (HR) image from the given low resolution (LR) image. The results are compared with state-of-the-art resolution enhancement methods.
A Survey on Implementation of Discrete Wavelet Transform for Image Denoisingijbuiiir1
Image Denoising has been a well studied problem in the field of image processing. Images are often received in defective conditions due to poor scanning and transmitting devices. Consequently, it creates problems for the subsequent process to read and understand such images. Removing noise from the original signal is still a challenging problem for researchers because noise removal introduces artifacts and causes blurring of the images. There have been several published algorithms and each approach has its assumptions, advantages, and limitations. This paper deals with using discrete wavelet transform derived features used for digital image texture analysis to denoise an image even in the presence of very high ratio of noise. Image Denoising is devised as a regression problem between the noise and signals, therefore, Wavelets appear to be a suitable tool for this task, because they allow analysis of images at various levels of resolution.
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Satellite image contrast enhancement using discrete wavelet transform
1. SATELLITE IMAGE CONTRAST
ENHANCEMENT USING DISCRETE
WAVELET TRANSFORM AND SINGULAR
VALUE DECOMPOSITION
Project guide Presented by
SATHYANARAYANA G.Divya
J.Tejas
D.Harishwar Reddy
A.Navya sree
2. INTRODUCTION
SATELLITE images are used in many applications such as
geosciences studies, astronomy, and geographical information
systems.
One of the most important quality factors in satellite images
comes from its contrast. Contrast enhancement is frequently
referred to as one of the most important issues in image
processing.
Contrast is created by the difference in luminance reflected
from two adjacent surfaces. In visual perception, contrast is
determined by the difference in the color and brightness of an
object with other objects.
Our visual system is more sensitive to contrast than absolute
luminance; therefore, we can perceive the world similarly
regardless of the considerable changes in illumination
conditions . If the contrast of an image is highly concentrated
on a specific range, the information may be lost in those areas
which are excessively and uniformly concentrated.
3. The problem is to optimize the contrast of an image in order
to represent all the information in the input image.
There have been several techniques to overcome this issue
such as general histogram equalization (GHE) and local
histogram equalization(LHE).
In this letter, we are comparing our results with two state-of-
the-art techniques, namely, brightness preserving dynamic
histogram equalization (BPDHE) and our previously
introduced singular value equalization (SVE).
In many image processing applications, the GHE technique is
one of the simplest and most effective primitives for contrast
enhancement which attempts to produce an output histogram
that is uniform.
One of the disadvantages of GHE is that the information laid
on the histogram or probability distribution function (PDF) of
the image will be lost.
Demirel and Anbarjafari showed that the PDF of face images
can be used for face recognition; hence, preserving the shape
of the PDF of an image is of vital importance. Techniques
such as BPDHE or SVE are preserving the general pattern of
the PDF of an image. BPDHE is obtained from dynamic
histogram specification which generates the specified
histogram dynamically from the input image.
4. WHAT IS MATLAB ……?
It is a technical computer language which has a capacity
to convert and implement the signals,graphs,images and
videos in mathematical expressions.
5. WHAT IS AN IMAGE?
An image is represented as a two dimensional function
f(x, y), where x and y are spatial co-ordinates and the
amplitude of ‘f’ at any pair of coordinates (x, y) is called the
intensity of the image at that point.
6. THE TOOLBOX SUPPORTS FOUR TYPES OF
IMAGES
Intensity images : An intensity image is a data matrix whose
values have been scaled to represent intentions.
Binary images : Binary images have a very specific
meaning in MATLAB.A binary image is a logical array 0s
and1s.Thus, an array of 0s and 1s whose values are of
data class
Indexed images :
R G B images:
7. DISCRETE WAVELET TRANSFORM
The Discrete Wavelet Transform (DWT), which is based on
sub-band coding is found to yield a fast computation of
Wavelet Transform.
It is easy to implement and reduces the computation time and
resources required.
The foundations of DWT go back to 1976 when techniques to
decompose discrete time signals were devised. Similar work
was done in speech signal coding which was named as sub-
band coding.
In 1983, a technique similar to sub-band coding was
developed which was named pyramidal coding. Later many
improvements were made to these coding schemes which
resulted in efficient multi-resolution analysis schemes.
8. APPLICATIONS OF DWT
There is a wide range of applications for Wavelet Transforms.
They are applied in different fields ranging from image
processing to biometrics, and the list is still growing.
One of the prominent applications is in the FBI fingerprint
compression standard.
Wavelet Transforms are used to compress the fingerprint
pictures for storage in their data bank.
The previously chosen Discrete Cosine Transform (DCT) did
not perform well at high compression ratios. It produced
severe blocking effects which made it impossible to follow the
ridge lines in the fingerprints after reconstruction. This did not
happen with Wavelet Transform due to its property of
retaining the details present in the data.
9. Contd…………
In DWT, the most prominent information in the signal appears
in high amplitudes and the less prominent information appears
in very low amplitudes.
Data compression can be achieved by discarding these low
amplitudes.
The wavelet transforms enables high compression ratios with
good quality of reconstruction.
At present, the application of wavelets for image compression
is one the hottest areas of research. Recently, the Wavelet
Transforms have been chosen for the JPEG 2000 compression
standard.
10. SINGULAR VALUE DECOMPOSITION
Singular value decomposition (SVD) can be calculated
mainly by the three mutually compatible points of view.
On the one hand, we can view it as a method for transforming
World Academy of Science, Engineering and Technology 55
2011 36 correlated variables into a set of uncorrelated ones
that better expose the various relationships among the original
data items.
At the same time, SVD is a method for identifying and
ordering the dimensions along which data points exhibit the
most variation.
His ties in to the third way of viewing SVD,
which is that once we have identified where the most variation
is present, then it is possible to find the best approximation of
the original data points using fewer dimensions.
Hence, SVD can be seen as a method for data reduction and
mostly for feature extraction as well as for the enhancement of
the low contrast images.
11. WHEN SVD WILL BE MORE EFFICIENT?
When ringing artifacts or blocking artifacts need to be
avoided, SVD is a good choice.
When only general shape is needed to save memory, SVD can
compress to contour with high compression ratio.
When compression speed is considered, SVD is a good choice.
Granted, the image quality of SVD is mostly worse than
Fourier transform and Wavelet transform. However, there are
some exceptions.
When the image itself is not full-rank, then the last few singular
values are zero and the last few terms in the outer product
expansion can be truncated with no cost but save memory.
Also, as stated in the special topic in the image compression
section, we know that SVD is a good image compression
technique when we apply it to an image that is near to a lower
rank image. when the image are near to a lower rank image or
possesses certain symmetry, SVD gives low error ratio.
12. ADVANTAGES OF SVD
Compression speed in SVD is also high.
Small change in the input results in small change in the singular matrix
so it is more stable
Unlike the fourier transform that uses fixed 8 block based
8
method, SVD allows the use of other sizes of block so we can decide the
optimal size of block to be used. Therefore, it allows us to perform
optimization on memory reduction.
Unlike fourier transform and wavelet transform, SVD does not lead to
the ringing artifacts. The ringing artifacts appear as the spurious signals
near sharp transitions in a signal. In image, bands or "ghosts" appear
near edges of objects
Mostly, the compression methods work better for the gray one
then the colour one. SVD has an opposite behaviour; it works
better for the colour image than for the gray one.
Wavelet and FFT stop compressing the image beyond a certain
compression degree, but SVD can still compress a lot. It also
compresses up to contour). When only general shape is
required, SVD does good job and at the same time save a lot of
memory.
13. APPLICATIONS
Engineering
Arts
Wireless communications
Compression of videos and images
Representation of the range and null space of a matrix
Pseudo inverse
Numerical linear algebra
Low-rank matrix approximation
Solving Homogeneous linear equations
Total least squares minimization
Principal Component Analysis
14. HISTOGRAM:-
A Histogram is a vertical bar chart that depicts the
distribution of a set of data. Unlike Run Charts or
Control Charts, which are discussed in other
modules
A Histogram does not reflect process performance
over time. It's helpful to think of a Histogram as
being like a snapshot, while a Run Chart or Control
Chart is more like a movie.
15.
16. WHEN ARE HISTOGRAMS USED?
Summarize large data sets graphically
Compare measurements to specifications
Communicate information to the team
Assist in decision making
18. CONCLUSION
In this work, a new image equalization technique based
on SVD and DWT was proposed.
The proposed technique converted the image from
spatial domain into the DWT domain and after
equalizing the singular value matrix of the LL sub band
image, it reconstructed the image in the spatial domain
by using IDWT.
The technique was compared with the GHE, LHE, DHE
and SVE techniques.
The experimental results were showing the superiority of
the proposed method over the conventional and the
state-of-art techniques.
This technque is used in Satellite Imaging
Corporation, Automatic Weather Stations Project, and
Antarctic Meteorological Research Centre for providing
the satellite images for research purposes.