14-05-2014, 11:45 AM
A Spatial Median Filter for Noise Removal in Digital Images
A Spatial Median Filter for Noise.pdf (Size: 170.2 KB / Downloads: 82)
Abstract
In this paper, six different image filtering algorithms are
compared based on their ability to reconstruct noise-
affected images. The purpose of these algorithms is to
remove noise from a signal that might occur through the
transmission of an image. A new algorithm, the Spatial
Median Filter, is introduced and compared with the cur-
rent image smoothing techniques. Experimental results
demonstrate that the proposed algorithm is comparable
to popular image smoothing algorithms. In addition, a
modification to this algorithm is introduced to achieve
more accurate reconstructions over other popular tech-
niques.
Smoothing Algorithms
The inexpensiveness and simplicity of point-and-
shoot cameras, combined with the speed at which bud-
ding photographers can send their photos over the Inter-
net to be viewed by the world, makes digital photogra-
phy a popular hobby. With each snap of a digital pho-
tograph, a signal is transmitted from a photon sensor
to a memory chip embedded inside a camera. Trans-
mission technology is prone to a degree of error, and
noise is added to each photograph. Significant work has
been done in both hardware and software to improve the
signal-to-noise ratio in digital photography.
In software, a smoothing filter is used to remove
noise from an image. Each pixel is represented by three
scalar values representing the red, green, and blue chro-
matic intensities. At a pixel studied, a smoothing fil-
ter takes into account the pixels surrounding it in order
to make a determination of a more accurate version of
this pixel.
Spatial Median Filter for Smoothing Im-
ages
When transferring an image, sometimes transmis-
sion problems cause a signal to spike, resulting in one
of the three point scalars transmitting a incorrect value.
This type of transmission error is called “salt and pep-
per” noise due to the bright and dark spots that appear
on the image as a result of the noise. The ratio of incor-
rectly transmitted points to the total number of points
is referred to as the noise composition of the image.
The goal of a noise removal filter is to take a corrupted
image as input and produce an estimation of the origi-
nal with no foreknowledge of the characteristics of the
noise nor the noise composition of the image
Conclusions
In this paper I have introduced two new filters for
removing impulse noise from images and shown how
they compare to four other well-known techniques for
noise removal. First, four common noise filtering algo-
rithms were discussed. Next, a Spatial Median Filter
was proposed based on a combination of work on the
Vector Median Filter and the Spatial Median quantile
order statistic. Seeing that the order statistic could be
utilized in order to make a judgment as to whether a
point in the signal is considered noise or not, a Modi-
fied Spatial Median Statistic is proposed. The Modified
Spatial Median Filter requires two parameters: A win-
dow size and a threshold T of the estimated non-noisy
pixels under a mask.