image processingprocessing digital images by means of a digitalcomputer.This includes:Analyze the imageManipulate itStore ...
Example of image processing  cropping an image:Create new image from part of original image.  Image Resizing:shrink / expa...
Example of image processing-cont   Image Negative:A negative image is a total inversion of a positive image,in which light...
Applications of image processing  Television  Signal Processing  Satellite Image Processing  Medical Image Processing  Rob...
Examples of applicationsOptical Character Recognition (OCR):Handwritten: sorting lettersPrinted texts: reading machinesBio...
Examples of applications-contDiagnostic systems :Medical diagnosis: X-Ray, EKG analysis.Machine diagnostics, waster detect...
Areas in image processing  Image acquisition :capturing an image in digital form Image enhancement :making an image look b...
Areas in image processing-cont  Image restoration :improving the appearance of any image objectively.  Image segmentation ...
Areas in image processing-cont  Image compression :reducing the stored and transmitted image data.  Representation and des...
Areas in image processing-cont   Recognition :Identifying an object based on its features and descriptors
Image Restorationestimated degradation, and restoring it to its originalappearance.used in photography or publishing where...
Figure of Image Restoration
Image noisemeaning of "noise” is "unwanted sound”Image noise is random variation of brightness or colorinformation in image.
Type of noise  Gaussian:give no overshoot to a step function input whileminimizing the rise and fall time.  Impulse:dynami...
example for Gaussian Noise      Original Image   Corrupted Image
IMPULSE NOISEImpulse noise is very common in digital images. Impulse noise is always independent and uncorrelated to theim...
models of impulse noise  Salt impulse noise:assumed to have the brightest gray level. Appears aswhite spot in the image  P...
Example for Impulse Noise     Original Image   Corrupted Image
Example for salt and pepper Noise and it’s Restoration by Justin et. el Filter
Filtering Techniques for Restoration   Mean Filter: Mean filter or average filter is windowed filter to linear class that ...
exampleMean Filter:Median Filter:
Proposed workTo Implement the following research papers from IEEE inMatlab and analyze it’s performance using various crit...
conclusionproposed software works for the restoration ofimages corrupted with almost all impulse noise levels.It will prod...
impulse noise filter
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impulse noise filter

  1. 1. image processingprocessing digital images by means of a digitalcomputer.This includes:Analyze the imageManipulate itStore itDisplay it
  2. 2. Example of image processing cropping an image:Create new image from part of original image. Image Resizing:shrink / expand an image from its original size.
  3. 3. Example of image processing-cont Image Negative:A negative image is a total inversion of a positive image,in which light areas appear dark and vice versa. Contrast Stretching:Contrast stretching is a simple image enhancementtechnique that attempts to improve the contrast in animage by `stretching the range of intensity values itcontains to span a desired range of values
  4. 4. Applications of image processing Television Signal Processing Satellite Image Processing Medical Image Processing Robot Control Visual Communications LawEnforcement
  5. 5. Examples of applicationsOptical Character Recognition (OCR):Handwritten: sorting lettersPrinted texts: reading machinesBiometrics:Finger prints recognition.Speech recognition.
  6. 6. Examples of applications-contDiagnostic systems :Medical diagnosis: X-Ray, EKG analysis.Machine diagnostics, waster detectionMilitary applications:Automated Target Recognition (ATR).Image segmentation and analysis.
  7. 7. Areas in image processing Image acquisition :capturing an image in digital form Image enhancement :making an image look better in a subjective way.
  8. 8. Areas in image processing-cont Image restoration :improving the appearance of any image objectively. Image segmentation :partitioning an image into its constituent parts orobjects.
  9. 9. Areas in image processing-cont Image compression :reducing the stored and transmitted image data. Representation and description :boundary representation vs. region representation.Boundary descriptors vs. region descriptors.
  10. 10. Areas in image processing-cont Recognition :Identifying an object based on its features and descriptors
  11. 11. Image Restorationestimated degradation, and restoring it to its originalappearance.used in photography or publishing where an image.
  12. 12. Figure of Image Restoration
  13. 13. Image noisemeaning of "noise” is "unwanted sound”Image noise is random variation of brightness or colorinformation in image.
  14. 14. Type of noise Gaussian:give no overshoot to a step function input whileminimizing the rise and fall time. Impulse:dynamic system is its output when presented with abrief input signal.
  15. 15. example for Gaussian Noise Original Image Corrupted Image
  16. 16. IMPULSE NOISEImpulse noise is very common in digital images. Impulse noise is always independent and uncorrelated to theimage pixels.unlike Gaussian noise, for an impulse noise corrupted image all theimage pixels are not noisy.
  17. 17. models of impulse noise Salt impulse noise:assumed to have the brightest gray level. Appears aswhite spot in the image Pepper impulse noise:darkest value of the gray level in the image. Appears asblack spot in the image
  18. 18. Example for Impulse Noise Original Image Corrupted Image
  19. 19. Example for salt and pepper Noise and it’s Restoration by Justin et. el Filter
  20. 20. Filtering Techniques for Restoration Mean Filter: Mean filter or average filter is windowed filter to linear class that smoothes signal(image). Median Filter: Nonlinear digital filtering technique, used to remove noise.
  21. 21. exampleMean Filter:Median Filter:
  22. 22. Proposed workTo Implement the following research papers from IEEE inMatlab and analyze it’s performance using various criteria 1. SrinivasanK.S and Ebenezer.D, “A new fast and efficient Decision – Based algorithm for removal of high - density impulse noises”,EEE signal processing letters, vol.14, no.3, 2007 2. Madhu S. Nair, Revathy.K and Rao Tatavarti, “An improved decision – based algorithm for impulse noise removal”, Congress on image and signal processing, 2008 3. Justin Varghese ”An Effective Filter for the Restoration of Highly Corrupted Digital Images” IEEE World Congress on Nature & Biologically Inspired Computing, 2009. NaBIC 2009. Publication Year: 2009 , Page(s): 1480 - 1485
  23. 23. conclusionproposed software works for the restoration ofimages corrupted with almost all impulse noise levels.It will produce patches free outputs from imagescorrupted by higher levels of impulse noise.Experimental analysis will be done to analyze theperformance of the filters.
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