FPGA Based Non Uniform Illumination Correction in Image ...

Abhishek Acharya,Rajesh Mehra,Vikram Singh Takher, Int. J. Comp. Tech. Appl., Vol 2 (2), 349-358
ISSN:2229-6093
2. Real Time Constraints
In the case of real time Image processing
application such as automated
surveillance or radar system number of
image processing stages and the biggest
performance bottleneck is the time
involved in processing the images
captured by the camera. It is also during
this preprocessing phase, when a large
amount of data is being processed, that
the system seeks to enhance the quality
of the images captured by removing
noise or unbalanced Lighting. Meeting
such real-time constraints is not always
possible by relying solely on a software
based solution implemented on a general
purpose computer (PC). This is because
there are multiple constraints placed on
such computers by memory and
peripheral devices connected to it.
This leads to explore possible hardware
based alternatives such as FPGA. Field
Programmable Gate Arrays (FPGA) is a
reconfigurable device used to place
some or all of the system onto the
hardware. They allow rapid prototyping
of a system and offer an inexpensive
option to validate system requirements
[2]. Placing the functionality of image
processing applications onto hardware
allows faster processing as it is no longer
necessary to split the individual
instructions into fetch, decode and apply
cycle needed in the typical processing
unit of a computer [3]. Moreover, the
parallelism inherent in most low-level
image processing operations can now be
exploited to its full extent as they can be
partitioned into subsystems, all of which
can run concurrently with each other.
Hence the goal of our research will be to
implement the image processing
algorithms developed for our
surveillance system so that the
processing speed of the components of
the system is bounded within a specified
timing constraint considered typical of
such systems.
3. Non Uniform Illumination
Illumination is one of the most
important factors affecting the
appearance of an image. It often leads
to diminished structures or
inhomogeneous intensities of the image
due to different texture of the object
surface and the shadows cast from
different light source directions. On the
other hand, uneven background, also
known as background bias, background
intensity in-homogeneity, or nonuniform
background, is the problem that
an ideal image f is corrupted by an
uneven background signal b so that the
observed image I = f +b. Recovering f
from I is not an easy task when b is
non-uniform. In essence, both the
varying illumination and the uneven
background are inhomogeneous
intensity patterns that are either
multiplicative or additive.
A common issue irrespective of the type
of camera and method of microscope
attachment is uneven illumination at the
edges of the image, otherwise known as
vignetting. This may be attributed to
multiple factors from the illumination
filament, the design of the light path
between the camera and the
microscope, or the behavior of the
imaging device. Conventional digital
cameras—for example, are not designed
for microscopy imaging, and many of
their automatic functions can interfere
with the correct aperture and exposure
settings. Common problem irrespective
of the type of camera and method of
microscope attachment is uneven
illumination at the edges of the image,
known as vignetting (Figure 1, 2)
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