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.

16. WHEN ARE HISTOGRAMS USED?
 Summarize large data sets graphically
 Compare measurements to specifications
 Communicate information to the team
 Assist in decision making

17. HISTOGRAM EQUALISATION

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.

19. Thank you !!!