processing digital images by means of a digitalcomputer.

This includes:Analyze the imageManipulate it Store itDisplay it

image processing

cropping an image:Create new image from part of original image.

Image Resizing: shrink / expand an image from its original size.

Example of image processing

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 enhancement technique that attempts to improve the contrast in an image by `stretching' the range of intensity values it contains to span a desired range of values

Example of image processing-cont

Television Signal Processing Satellite Image Processing Medical Image Processing Robot Control Visual Communications LawEnforcement

Applications of image processing

Optical Character Recognition (OCR): Handwritten: sorting letters Printed texts: reading machines

Biometrics: Finger prints recognition. Speech recognition.

Examples of applications

Diagnostic systems : Medical diagnosis: X-Ray, EKG analysis. Machine diagnostics, waster detection

Military applications: Automated Target Recognition (ATR). Image segmentation and analysis.

Examples of applications-cont

Image acquisition : capturing an image in digital form

Image enhancement : making an image look better in a subjective way.

Areas in image processing

Image restoration : improving the appearance of any image objectively.

Image segmentation : partitioning an image into its constituent parts or objects.

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.

Areas in image processing-cont

Recognition : Identifying an object based on its features and descriptors

Areas in image processing-cont

estimated degradation, and restoring it to its original appearance.

used in photography or publishing where an

image.

Image Restoration

Figure of Image Restoration

meaning of "noise” is "unwanted sound”

Image noise is random variation of brightness or color information in image.

Image noise

Gaussian:give no overshoot to a step function input while minimizing the rise and fall time.

Impulse:dynamic system is its output when presented with a brief input signal.

Type of noise

example for Gaussian Noise

Original Image Corrupted Image

Impulse noise is very common in digital images.

Impulse noise is always independent and uncorrelated to the image pixels.

unlike Gaussian noise, for an impulse noise corrupted image all the image pixels are not noisy.

IMPULSE NOISE

Salt impulse noise: assumed to have the brightest gray level. Appears as white spot in the image Pepper impulse noise:darkest value of the gray level in the image. Appears as black spot in the image

models of impulse noise

Example for Impulse Noise

Original Image Corrupted Image

Example for salt and pepper Noise and it’s Restoration by Justin et. el Filter

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.

Filtering Techniques for Restoration

Mean Filter:

Median Filter:

example

To Implement the following research papers from IEEE in Matlab 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

Proposed work

proposed software works for the restoration of images corrupted with almost all impulse noise levels.

It will produce patches free outputs from images corrupted by higher levels of impulse noise.

Experimental analysis will be done to analyze the performance of the filters.

conclusion