2. What are the fundamental steps in Digital Image Processing?

2. What are the fundamental steps in Digital Image Processing?

   March 04, 2013       legend

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Fundamental Steps in Digital Image Processing:

Image acquisition is the first process shown in Fig.2. Note that acquisition could be as simple as
being given an image that is already in digital form. Generally, the image acquisition stage
involves preprocessing, such as scaling.

Image enhancement is among the simplest and most appealing areas of digital image processing.
Basically, the idea behind enhancement techniques is to bring out detail that is obscured, or
simply to highlight certain features of interest in an image. A familiar example of enhancement is
when we increase the contrast of an image because “it looks better.” It is important to keep in
mind that enhancement is a very subjective area of image processing.

Image restoration is an area that also deals with improving the appearance of an image.
However, unlike enhancement, which is subjective, image restoration is objective, in the sense
that restoration techniques tend to be based on mathematical or probabilistic models of image
degradation. Enhancement, on the other hand, is based on human subjective preferences
regarding what constitutes a “good” enhancement result.

Color image processing is an area that has been gaining in importance because of the significant
increase in the use of digital images over the Internet.

Wavelets are the foundation for representing images in various degrees of resolution.
Compression, as the name implies, deals with techniques for reducing the storage required to
save an image, or the bandwidth required to transmit it. Although storage technology has
improved significantly over the past decade, the same cannot be said for transmission capacity.
This is true particularly in uses of the Internet, which are characterized by significant pictorial
content. Image compression is familiar (perhaps inadvertently) to most users of computers in the
form of image file extensions, such as the jpg file extension used in the JPEG (Joint
Photographic Experts Group) image compression standard

Morphological processing deals with tools for extracting image components that are useful in the
representation and description of shape.

Segmentation procedures partition an image into its constituent parts or objects. In general,
autonomous segmentation is one of the most difficult tasks in digital image processing. A rugged
segmentation procedure brings the process a long way toward successful solution of imaging
problems that require objects to be identified individually. On the other hand, weak or erratic
segmentation algorithms almost always guarantee eventual failure. In general, the more accurate
the segmentation, the more likely recognition is to succeed.

Representation and description almost always follow the output of a segmentation stage, which
usually is raw pixel data, constituting either the boundary of a region (i.e., the set of pixels
separating one image region from another) or all the points in the region itself. In either case,
converting the data to a form suitable for computer processing is necessary. The first decision
that must be made is whether the data should be represented as a boundary or as a complete
region. Boundary representation is appropriate when the focus is on external shape
characteristics, such as corners and inflections. Regional representation is appropriate when the
focus is on internal properties, such as texture or skeletal shape. In some applications, these
representations complement each other. Choosing a representation is only part of the solution for
transforming raw data into a form suitable for subsequent computer processing. A method must
also be specified for describing the data so that features of interest are highlighted. Description,
also called feature selection, deals with extracting attributes that result in some quantitative
information of interest or are basic for differentiating one class of objects from another

Recognition is the process that assigns a label (e.g., “vehicle”) to an object based on its
descriptors. We conclude our coverage of digital image processing with the development of
methods for recognition of individual objects.


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