04-06-2012, 05:12 PM
Fuzzy Random Impulse Noise Removal
From Color Image Sequences
Fuzzy Random Impulse Noise Removal.pdf (Size: 1.13 MB / Downloads: 12)
Abstract
In this paper, a new fuzzy filter for the removal of
random impulse noise in color video is presented. By working with
different successive filtering steps, a very good tradeoff between
detail preservation and noise removal is obtained. One strong filtering
step that should remove all noise at once would inevitably
also remove a considerable amount of detail. Therefore, the noise
is filtered step by step. In each step, noisy pixels are detected by
the help of fuzzy rules, which are very useful for the processing of
human knowledge where linguistic variables are used.
INTRODUCTION
I MAGES and videos belong to the most important information
carriers in today’s world (e.g., traffic observations,
surveillance systems, autonomous navigation, etc.). However,
the images are likely to be corrupted by noise due to bad acquisition,
transmission or recording. Such degradation negatively influences
the performance of many image processing techniques
and a preprocessing module to filter the images is often required.
Second Filtering Step
In our aim to preserve the details as much as possible, the
noise is removed in successive steps. In this step, the noise is
detected based on the output of the previous step . Also in
this second filtering step, a degree to which a pixel component
is expected to be noise-free and a degree to which a pixel component
is expected to be noisy, is calculated. In the calculation
of those degrees, we now take into account information from the
other color bands.
A color component of a pixel is considered noise-free if the
difference between that pixel and the corresponding pixel in the
previous frame is not large in the given component and also not
large in one of the other two color components. It is also considered
noise-free if there are two neighbors for which the difference
in the given component and one of the other two components
are not large. So, the other color bands are used here as
a confirmation for the observations in the considered color band
to make those more reliable.
CONCLUSION
In this paper, we have presented a new filtering framework
for color videos corrupted with random valued impulse noise.
In order to preserve the details as much as possible, the noise is
removed step by step. The detection of noisy color components
is based on fuzzy rules in which information from spatial and
temporal neighbors as well as from the other color bands is used.
Detected noisy components are filtered based on blockmatching
where a noise adaptive mean absolute difference is used and
where the search region contains pixels blocks from both the
previous and current frame.