2. Components of Digital image
Processing system
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3. Components of Digital image
Processing system
1. Image Sensors
• Image sensors senses the intensity, amplitude, co-ordinates
and other features of the images and passes the result to the
image processing hardware. It includes the problem domain.
• Two elements are required to acquire digital images, i.e to
get digital images.
• The first is the physical device that is sensitive to the energy
radiated by the object we wish to image (Sensor). Eg. Digital
video camera
• The second, called a digitizer, is a device for converting the
output of the physical sensing device into digital form.
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4. Components of Digital image
Processing system
2. Specialized Image Processing Hardware
• Usually consists of the digitizer plus hardware that performs other
primitive operations, such as an arithmetic logic unit (ALU), which
performs arithmetic and logical operations in parallel on entire
images.
• Image processing hardware is the dedicated hardware that is used
to process the instructions obtained from the image sensors. It
passes the result to general purpose computer.
• This type of hardware sometimes is called a front end subsystem,
and its most distinguishing characteristic is speed.
• In other words, this unit performs functions that require fast data
throughputs that the typical main computer cannot handle.
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5. Components of Digital image
Processing system
• 3. Computer
• The computer in an image processing system
is a general-purpose computer and can range
from a PC to a supercomputer.
• In dedicated applications, sometimes
specially designed computers are used to
achieve a required level of performance.
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6. Components of Digital image
Processing system
• 4. Image Processing Software
• Software for image processing consists of
specialized modules that perform specific
tasks.
• A well-designed package also includes the
facilty for the user to write code that, as a
minimum, utilizes the specialized modules.
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7. Components of Digital image
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5. Mass Storage Capability
• Since images require large amount of storage space, Mass
storage capability is very important in a image processing
applications.
• Eg. A image of sized 1024 * 1024 pixels requires one
megabyte of storage space , if the image is not compressed
(without compression).
• Digital storage for image processing applications falls into
three principal categories:
– 1. Short-term storage for use during processing.
– 2. on line storage for relatively fast recall
– 3. Archival storage, characterized by infrequent access
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8. Components of Digital image
Processing system
5. Types Mass Storage Capability
• Mass storage stores the pixels of the images during
the processing.
5.1. Short-term storage for use during processing.
• One method of providing short-term storage is computer
memory.
• Another is by specialized boards, called frame buffers,
that store one or more images and can be accessed
rapidly at video rates.
• The on-line storage method (using frame buffers) allows
virtually instantaneous image zoom, as well as scroll
(vertical shifts) and pan (horizontal shifts).
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9. Components of Digital image
Processing system
5.2. On- line storage for fast recall:
• On-line storage generally takes the form of
magnetic disks and optical-media storage.
• The key factor characterizing on-line storage is
frequent access to the stored data.
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10. Components of Digital image
Processing system
• 5.3 . Archival storage for infrequent access
• It requires large amount of storage space and
the stored data is accessed.
• Magnetic tapes and optical disk packed in juke
boxes provide this type of storage.
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11. Components of Digital image
Processing system
6. Image Displays
• The displays in use today are mainly color
(preferably flat screen) TV monitors.
• Monitors are driven by the outputs of the
image and graphics display cards that are an
integral part of a computer system.
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12. Components of Digital image
Processing system
7. Hardcopy devices
• Used for recording images, include laser
printers, film cameras, heat-sensitive devices,
inkjet units and digital units, such as optical
and CD-Rom disks.
• Once the image is processed then it is stored
in the hard copy device. It can be a pen drive
or any external ROM device.
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13. Components of Digital image
Processing system
• 8. Networking
• Network is the connection of all the above elements of
the image processing system.
• Is almost a default function in any computer system, in
use today.
• Because of the large amount of data inherent in image
processing applications the key consideration in image
transmission is bandwidth.
• In dedicated networks, this typically is not a problem, but
communications with remote sites via the internet are
not always as efficient.
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14. Advantages of Digital Image
Processing
• Image reconstruction (CT, MRI, SPECT, PET)
• Image reformatting (Multi-plane, multi-view
reconstructions)
• Fast image storage and retrieval
• Fast and high-quality image distribution.
• Controlled viewing (windowing, zooming)
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15. Advantages of Digital Image
Processing
• Improved image quality: Digital image processing
algorithms can improve the visual quality of images,
making them clearer, sharper, and more informative.
• Automated image-based tasks: object recognition,
pattern detection, and measurement.
• Increased efficiency: processing of images much faster
than humans, making it possible to analyze large
amounts of data in a short amount of time.
• Increased accuracy: Digital image processing algorithms
can provide more accurate results than humans,
especially for tasks that require precise measurements or
quantitative analysis.
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16. Disadvantages of Digital Image
Processing
• It is very much time-consuming.
• It is very much costly depending on the
particular system.
• Qualified persons can be used.
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17. Disadvantages of Digital Image
Processing:
• High computational cost: Some digital image processing
algorithms are computationally intensive and require
significant computational resources.
• Limited interpretability: difficult for humans to interpret,
especially for complex or sophisticated algorithms.
• Dependence on quality of input: Poor quality input images
can result in poor quality output.
• Limitations of algorithms: The difficulty of recognizing objects
in cluttered or poorly lit scenes, or the inability to recognize
objects with significant deformations or occlusions.
• Dependence on good training data: Poor quality training data
can result in poor performance of the algorithm
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