Image Processing: it is any form of signal processing for which the input is an image, such as a photograph or video frame; the output of image processing may be either an image or, a set of characteristics or parameters related to the image. Image Compression: The objective of image compression is to reduce irrelevance and redundancy of the image data in order to be able to store or transmit data in an efficient form. Broadly Image Compression is of two types. Lossless and lossy compression are terms that describe whether or not, in the compression of a file, all original data can be recovered when the file is uncompressed. Image Restoration: t is the area that deals with improving the appearance of an image. The main purpose of restoration is to remove the noise. Image restoration is the operation of taking a corrupted/noisy image and estimating the clean original image.

1. IMAGE PROCESSING
AND APPLICATIONS
CHARU
9910103541

2. IMAGE PROCESSING
 It is any form of signal processing for which the
input is an image, such as a photograph or video
frame; the output of image processing may be
either an image or, a set of characteristics or
parameters related to the image
 Image processing basically includes the following
three steps.
 Importing the image.
 Analyzing and manipulating the image which includes
data compression etc.
 Output is the last stage in which result can be altered
image or report that is based on image analysis.

3. IMAGE COMPRESSION
 The objective of image compression is to reduce
irrelevance and redundancy of the image data in
order to be able to store or transmit data in an
efficient form.
 Compression is useful because it helps reduce the
consumption of expensive resources, such as hard
disk space or transmission bandwidth.
 Broadly Image Compression is of two types.
Lossless and lossy compression are terms that
describe whether or not, in the compression of a
file, all original data can be recovered when the file
is uncompressed.

4. IMAGE RESTORATION
 Image restoration is the area that deals with
improving the appearance of an image.
 The main purpose of restoration is to remove the
noise.
 Image restoration is the operation of taking a
corrupted/noisy image and estimating the clean
original image.

5. DATA COMPRESSION
 Compression is useful because it helps reduce the consumption of expensive resources,
such as hard disk space or transmission bandwidth. Lossless and lossy compression are
terms that describe whether or not, in the compression of a file, all original data can be
recovered when the file is uncompressed. With lossless compression, every single bit of
data that was originally in the file remains after the file is uncompressed. This is generally
the technique of choice for text or spreadsheet files, where losing words or financial data
could pose a problem. On the other hand, lossy compression reduces a file by
permanently eliminating certain information, especially redundant information. When the
file is uncompressed, only a part of the original information is still there (although the user
may not notice it). Lossy compression is generally used for video and sound, where a
certain amount of information loss will not be detected by most users. The JPEG image
file, commonly used for photographs and other complex still images on the Web, is an
image that has lossy compression.

6. SUMMARY
 Digitized images usually suffer from poor image quality, particularly presence
of noise, due to low light photos, slow shutter speed or very high sensitivity
modes. Because some features are hardly detectable by eye in an image, we
often transform images before display. Image processing methods do their
best to improve image vision and make the image adapt to be processed by
any system. Upon carefully studying and surveying customer views on
eliminating noise, image size reduction and secure image transfer, I came to
the conclusion that a tool which performs all the above mentioned functions
among others can prove very beneficial for various causes.
The tool essentially provides the following features :
 Image compression
 Image Denoising
 Image Encryption
And many others.

7. PROBLEM STATEMENT
 Image Compression: Image Compression of images will be achieved
by implementing algorithm given in [1], with a few enhancement (in
matching criteria).
 Image Denoising: Reduction of noise through fractal denosing will be
implemented using Mean and median denoising .
 Public Key Cryptography using Mandelbrot Sets: Cryptography
algorithms will be applied with the help of Mandelbrot sets to
generate complex public and private keys so that cryptanalysis will
be infeasible.
 To complement the tool many other options like, Increase/ Decrease
the brightness, Cropping the image, Sharpen the image will also be
implemented.

8. OVERVIEW
 The solution strategy includes application of various image
processing techniques namely compression, denoising and
encryption. Fractals will also be generated. The Images will be
effectively stored and efficiently transmitted. File size
reduction remains the single most significant benefit of image
compression.

9. IMAGE COMPRESSION
 The image is divided into a number of block domains with arbitrary size (ranging from 2x2to
16x16, or more). Then, the image is divided again into block ranges with size less than that of the
block domain. The selected reference blocks are used to formulate the reference block domain
pool. The image is divided again into block ranges. Then a search is performed in the reference
block domain pool for the best match with each range block. The only transmitted or stored data
are the indices of the selected reference block for each range block, instead of the range itself. If
there is no matched reference block, according to a certain threshold, the average value of the
range block is transmitted instead of the block itself. We use the absolute difference to determine
the similarity between blocks.
D(1,1) D(1,2) D(1,3) D(1,4) …
D(2,1) D(2,3) D(2,3) D(2,4) …
D(3,1) D(3,2) D(3,3) D(3,4) …
D(4,1) D(4,2) D(4,3) D(4,4) …
D(5,1) D(5,2) D(5,3) D(5,4) …
D(6,1) D(6,2) D(6,3) D(6,4) …
D(7,1) D(7,2) D(7,3) D(7,4) …
… … … … …
Block segmentation and block reference searching.
… … …
RB(1,1) RB(1,2) …
… … …
… … …
RB(2,1) RB(2,2) …
…. … …
Reference blocks in each region.

10. NOISE IN IMAGE
 Gaussian Noise
 Gaussian noise is evenly distributed over the signal. This means that each pixel in the
noisy image is the sum of the true pixel value and a random Gaussian distributed noise
value.
 Salt and Pepper Noise
 Salt and pepper noise is an impulse type of noise, which is also referred to as intensity
spikes. This is caused generally due to errors in data transmission. It has only two
possible values, a and b. The probability of each is typically less than 0.1. The corrupted
pixels are set alternatively to the minimum or to the maximum value, giving the image a
“salt and pepper” like appearance. Unaffected pixels remain unchanged.
Fig: 2.3 : Salt and Pepper Noise

11. IMAGE DENOISING
 Mean Filter
 A mean filter acts on an image by smoothing it; that is, it reduces the intensity variation
between adjacent pixels. The mean filter is nothing but a simple sliding window spatial
filter that replaces the center value in the window with the average of all the neighboring
pixel values including itself. By doing this, it replaces pixels, that are unrepresentative of
their surroundings. The mean or average filter works on the shift-multiply-sum principle.
The averaging filter works like a low pass filter, and it does not allow the high frequency
components present in the noise to pass through.
 Median Filter
 The median of the pixel values in the window is computed, and the center pixel of the
window is replaced with the computed median. Median filtering is done by, first sorting all
the pixel values from the surrounding neighborhood into numerical order and then
replacing the pixel being considered with the middle pixel value. Since the median value
must actually be the value of one of the pixels in the neighborhood, the median filter does
not create new unrealistic pixel values when the filter straddles an edge. For this reason
the median filter is much better at preserving sharp edges than the mean filter.

12. ENCRYPTION-DECRYPTION
 RSA algorithm generates public key and private key
pairs. The algorithm uses 2 complex prime
numbers p and q.
 Private key= (d,n)
 Public key=(e,n)
 Where n=p*q
 Encryption equation C=M^e modn
 Decryption equation M=C^d mod n

13. DESCRIPTION OF THE TOOL
 Microsoft Visual Studio is an integrated
development environment (IDE) from Microsoft. It
can be used to develop console and graphical user
interface applications along with Windows
Forms applications, web sites, web applications,
and web services in both native code together
with managed code for all platforms supported
by Microsoft Windows, Windows Mobile, Windows
CE, .NET Framework, .NET Compact
Framework etc.

14. CONCLUSION
 After applying the various image processing algorithms we
conclude that the compression ratio for PNG images is the
best as compared to BMP, GIF and PNG. While the PSNR
ratio for JPG is the best as compared to the others. But on an
average Fractal Image Compression algorithm has the best
effect on BMP images. The denoising algorithms are also
successful in reducing noise upto a great extent. Median Filter
works better for Salt and Pepper & Gaussian noises. Public
Key Cryptography using Mandelbrot fractals is highly secure
as it uses complex equations.