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Digital Image Processing

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Digital Image Processing Powered By Docstoc
					            Image
DEF:
 The term image refers to a 2-D light intensity function f (x, y), where
  (x, y) denote spatial coordinates and the values of intensity function,’f’
  at a point (x,y) are proportional to the brightness (gray level) of the
  image at that point.

 It is represented in matrix form where row and column indices identify
   a point in image and the corresponding matrix element identifies the
   brightness of the image at that point. These elements of the matrix are
   called as PIXELS or Picture Elements or Image Elements.

 The term digital image processing generally refers to the processing of
   2-D data in an array of real or complex numbers represented by a finite
   number of bits (binary digits).
        Image Enhancement

 Objective :-
  To process an image so that the result is more suitable than the original
  image for a specific application. There are several techniques used for
  enhancement of images. Some of the common image enhancement
  techniques are :-
           Techniques used for Enhancement

POINT OPERATIONS: -
     Contrast Stretching
     Clipping and Thresholding.
     Image Negatives
     Image Subtraction and Change Detection:-
     Histogram Modeling

SPATIAL OPERATIONS:-
    Smoothing Filters
    Median Filtering
    Sharpening Filters
    High Boost Spatial Filtering
    Derivative Filters
           Image Restoration

 Any image electronic means is likely to be degraded by the sensing
   environment. The degradations many be in the form of sensor noise,
   blur due to camera misfocus, relative object camera motion, random
   atmospheric turbulence and so on.
            Image Compression

    The term “data compression” refers to the process of reducing the
     amount of data required to represent a given quantity of information.
    In digital image compression, three basic data redundancies can be
     identified. They are:
1.       Coding redundancy.
2.       Inter pixel redundancy.
3.       Psycho visual redundancy.
    FIDELITY CRITERION
1.       Objective Fidelity Criteria
2.       Subjective Fidelity Criteria
            Image Compression Models

 A compression system consists of two distinct structural blocks, which
   are shown below:
                        1) Encoder. 2) Decoder.
 F(x,y) source         channel             channel    source
         Encoder encoder channel  decoder  decoder -f’(x,y)
Data Compression strategies:
1. Lossless Compression
2. Variable –length coding
Example:
          Original data stream:
 17 8 54     0 0 0 97 5 16 0 45 23 0 0 0 0 0 3 67 0 0           8 ..
          Run length Encoded data:
 17 8 54       03     97 5 16 0 1 45 23       05     3 67    02 8 ..
3. Huffman coding
4 Loosy compression
              Image Analysis

IMAGE SEGMENTATION
   The methods for segmentation are generally based on one of the two
    basic properties of the gray – level values.
1.  Discontinuity
2.  Similarity
APPLICATIONS:-
1.  Develops machine that could perform visual functions of living beings.
2.  Space image applications
3.  In archeology, physics and related fields high energy plasmas and electron
    microscopy.
4.  Radar and sonar images.
CONCLUSIONS:-
      They motivate, captivate, educate and inform more than words
      ever can.

				
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posted:7/17/2011
language:English
pages:8