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Tools for image processing and applications

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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.

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Tools for image processing and applications

  1. 1. IMAGE PROCESSING AND APPLICATIONS CHARU 9910103541
  2. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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.

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