A to digital converter which also leads to noise

A
Survey on Various Median Filtering Techniques for

Removal
of Impulse Noise from Digital Image

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ABSTRACT

One of the trending fields of image
processing is elimination of noise from a contaminated image. Image sequence
are often corrupted by noise, e.g. due to bad reception of signals over channel
through which they are transmitting. Some noise sources are located in sensor
of a camera and become active during image acquisition under bad lighting
conditions. Other noise sources are due to transmission over analogue channels
and thus need to use analog to digital converter which also leads to noise
contamination. In the most cases, it is very necessary to eliminate impulse noise
from image data. Median based filters are commonly used for the removal of such
impulse noise. However, there are different kind of median filters such as
weighted median filter, recursive median filter, iterative median filter,
directional median filter, adaptive median filter and switching median filter. Therefore,
this paper will survey different median filtering techniques for removal noise
from image.

Keywords—Adaptive
median filter, directional median filter, impulse noise, iterative median
filter, median filter, recursive median filter, switching median filter, weighted
median filter, threshold filter.

INTRODUCTION

Images
are vital and necessary for communication. Numerous images are produced,
translated and edited every day. However, as the variety of imaging devices,
the quality of images is unstable. Some information may be damaged during the
transition process between different devices. Thus, to remove noise and recover
meaningful information from noisy images is a vital problem.

 

Noise is the most annoying problem in
processing. It introduces random variations into image that fluctuate the
original values to some different values. One way to get rid of this problem is
the development of such a robust algorithm that can perform the processing
tasks in presence of noise. The other way is to design a filtration process to
eliminate the noise from images while preserving its features, edges and
details. The filters can be divided in two types: linear filter and non-linear
filter. Linear filters are like average filter or called averaging low pass
filter. But linear filter tends to blur edges and other details of image, which
may reduce the accuracy of output. On the other hand non-linear type filter
like median filter has better results than linear filter because median filter
remove the impulse noise without edge blurring. A median filter is an example
of a non-linear filter and it is very good at preserving image detail.
Generally different filters are used for eliminating different noises like Mean
filter is used to remove the impulse noise, median filtering is used to
eliminate salt and pepper noise and so on.  

 

The following three steps are used to
calculate median: 1. Process each pixel in the image 2. Select the size of
filtering window and sort the all pixels of window in order based upon their intensities.3.
Replace the central pixel with the median value of sorted pixels.

 

The performance of median filter also
depends on the size of window of filter. Smaller window preserves the details
but it will cause the reduction in noise suppression. Larger window has great
noise reduction capability but image details (edges, corners, fine lines)
preservation is limited. With the improvement in the standard median filters,
there were so many filters has designed like weighted median filter, centre
weighted median filter ,adaptive median filter, rank order median filter and
many other improved filters. Different median filters uses different sorting
algorithm like merge sort, quick sort, and heap sort to sort the elements of
window.

 

LITERATURE SURVEY

In
3,Al-Araji introduced an auto selection method for reduction of impulse noise
in wireless communication systems. The selection was made on  the basis of estimation of rate of impulse
noise present in the system. The impulse noise is detected by comparing it with
a fixed reference. Their system used subtraction-gating for low occurrence rate
and directly sends the incoming signal to the output. If impulse is absent. The
simulation results for QAM and FSK and real time implementation on an FPEG were
presented and the bit error rate was estimated.

In
4, Chen put forward the idea of an adaptive pixel correlation filter(ACPF) to
eliminate impulse noise, with an adaptive threshold  designed based on the correlations between a
pixel and its neighbour. The filter was designed with a novel adaptive working
window and weighted function for impulse noise detection and preserving
noise-free pixels based on the fact that both horizontal and vertical
correlations for a pixel are more significant than other orientations.

In
5, Chen proposed an adaptive working window to remove impulse noise. There was
a correlation between the neighbouring pixels. The pixel correlation was used
to determine the intensity of impulse noise . They introduced a simple rule for
impulse noise reduction for various median-based filter with adaptive working
window. The switching median filter and multi-state median filter are designed
with this simple rule and the simulations showed that this method enhances  the reliability.

In
6, Divyajyothi proposed a new integrated fuzzy filter for the reduction of
additive noise and impulse noise from digital color images. They used an
impulse noise detector in the filter to detect the presence of impulse. The
detector divides the set of pixels into affected points and clean points. A
selection filter module was employed to select the appropriate filter to match
the type of noise. The output of integrated filter contains the enhanced image
after noise removal. Their method combined the advantage of both additive noise
and impulse noise filters. Their results showed that the approach effectively
removed the integrated noise even at high noise levels.

In
7,Rezvanian presented an efficient method to reduce impulse noise in two
passes. During first pass impulse noise detection using ANFIS, and at the
second pass the impulse noise estimation, that corrupted noise pixel replaced
with new value based on neural networks. This method was experimented on
popular greyscale test images and compared with other methods using subjective
and objective measures. Their results showed that the method works efficiently
in reducing impulse noise.

In
8, Tao Chan proposed a tristate median filter for image denoising .This
preserves image details while effectively suppressing impulse noise. This
proposed filter outperforms other median filter by balancing the tradeoffs’
between noise reduction and detail preservation. To achieve better results, a
camera calibrations procedure may be placed before our systems. For different
values of noise density, optimum threshold range for yielding smallest MSE
values and good visual quality can be obtained through similar simulation
experiments.

In
9, Zhou Wang and David zhang proposed a new median based filter i.e. progressive
switching median filter to restore images corrupted by salt and pepper noise. This
algorithm consists of two main concepts: switching scheme based filter and progressive
method impulse detection and nose filtering procedures are progressively through
several iterations. The experiment results shows that the proposed algorithm is
better than  the existing median based
filters. This method is particularly useful for images, having corruption ranging
from 5% to 70%. The MSE is also effective than other median based methods, even
when noise ratios are high. It  eliminates
most of the noisy pixels along with preserving image details .

In
10 Tao Chen and Hong Ren vu introduced a novel adaptive median-filter that
consists the switching scheme based on the impulse detection mechanism. Centre
weighted median filters are used for  adaptive
impulse detection. The extensive simulations shows that the proposed filter
consistently works well in suppressing both fixed and random valued impulses with
different noise ratios. While, still possessing a computational structure.

In
11, Senk and Trpovski proposed a robust estimator of the variance, MAD, is
modified and used to efficiently separate noisy pixels from the image details
and therefore has no sensitivity to image contents. The complexity of proposed
algorithm is equivalent to that of the median filter. The pixel wise MAD concept
is straight forward, low in complexity and achieves high filtering performance.

In
12, Raymond Chan,Chen Hu and Mila Nikolova proposed a two stage iterative
method for removing random valued impulse noise. In the first phase, an
adaptive centre weighted median filter(ACWM)  is used to identify corrupted pixels . In the
latter phase, these noise candidates are restored using a detail preserving
regularization method which allows edges and noise free pixels to be preserved.
These two phases are applied alternatively. This simulation results indicate
that the proposed method is significantly better than those using just non
linear filters or regulization only. This can be done very fast. The timing can
be enhanced by better implementations of minimization routines for solving the
robust statistics with applications to early vision.

In
13, Hancheng Yu,Li Zhao and Haixian Wang proposed an efficent algorithm
method uses a statistics of rank ordered relative differences to identify
pixels which are likely to be corrupted .It consists of two methods 1.RORD
impulse detector into many existing techniques, allowing them to detect and
properly handle impulse like pixels. 2. A sample weighted mean filter (SWMF) by
using the RORD detector and the reference image to suppress impulse noise, while
preserving image details. Although our algorithm is applied iteratively much
faster, especially when the noise is high, the sample weighted filter offers
good filtering performance while its implementation complexity is lower than
others.

In
14, Francisco Estard, David Fleet and Allan Jepson proposed a probabilistic
algorithm for image noise removal.ie., Stochastic Image Denoising .These
proposed algorithm for image denoising based on simulated random walks on image
space and also very simple. Random walk produce stable estimates even for few
trials and the overall behaviour of the random walks approximate that of more
computationally expensive blur kernels. Stochastic denoising will become a
useful tool for noise removal.

In
15, Jain-Feng Cai,Raymond H.Chan and Mila Nikolova(2010) proposed a fast two –
phase image de blurring under impulse noise. This proposed algorithm consists of
a two- phase approach to restore image corrupted by blur and impulse noise,
which is much simpler. In the first phases, accurate detection of a location of
outlier using a median-type filter and second phase edge -preserving
restoration that de-blur using only those data samples that are note noise
candidates. The PSNR of the restoration by our method is about 1 to 3 dB higher
than that by the variation method. Even for blurred images corrupted by 55%
random valued noise, proposed method can give a very good restoration result.
Comparing the two –phase methods. Image De-blurring under impulse noise with
the two- phase methods for de-blurring images corrupted by impulse Gaussian
noise. The proposed method produces more computationally efficient and takes
only about 1/8 CPU.

In
16,P.Etrahanias and A.N.Venetsanopoulos (1993) proposed a Vector Directional
Filter-A New Class of Multichannel Image Processing Filters. These proposed
filters separate the processing of vector-valued signals into directional
processing and magnitude processing. This provide a link between single-channel
image processing, where only magnitude processing is essentially performed, and
multichannel image processing where both the direction and the magnitude of the
image vectors play an important role in the resulting image. VDF perform at
least as good and in most cases and also better chrominance estimate than VMF
and this justifies their employment in colour image processing. VDF can achieve
very good filtering results for various noise source models.

In
17, Yiqiu Dong, Raymond H, Chan and Shufang Xu (2007) proposed a Detection
Statistic for Random -Valued Impulse Noise. This proposed technique for
detecting random –valued impulse noise based on image statistic. By this
statistic, we can identify most of the noisy pixels in the corrupted images.
Combining it with an edge preserving regularization, we obtain a powerful two
-stage method for de-noising random valued impulse noise even for noise level
as high as 60%. These propose a new local image statistic ROLD, by which we can
identify more noisy pixels with less false-hits. We combine kit with the edge preserving
regularization in the two- stage iterative method.

In
18 , Yiqiu Dong and Shufang Xu proposed a New Directional weighted median
filter for removal of  RVIN. They
proposed a new impulse detector .based on difference between current pixels and
its neighbours aligned with four main directions. Then we combine it with the
weighted median filter to get a new directional weighted median filter. This
proposed method suppresses noise level and preserve image details too, including
thin lines.  In addition it can be
extended to restore color images corrupted by impulse noises.

In
19, Roman Garnett, timothy Huegerich and charles chui introduced a universal
noise removal algorithm, which consists of an impulse detector. This proposes a
local image statistics for finding corrupted pixels. The simulation result is
capable of reducing both Gaussian and impulse noise .This method is extended to
remove any mix of Gaussian and impulse noise.

In
20, vector directional filters (VDF) for multichannel image processing were introduced.
VDF separates the processing of vector valued signal into directional
processing and magnitude processing. This provides a link between single
channel image processing, where only magnitude processing is essentially
performed and multichannel image processing where both the direction and magnitude
of the image vectors play a vital role in the processed image. The experimental
result shows that in case of color images, VDF achieved good results  for various noise source models. 

In
21, shih-Chang Hsia proposed an efficient noise removal algorithm using an
adaptive digital image processing approach. Simulations have demonstrated that
the new adaptive algorithm could effectively reduce noise impulse even in
corrupted images. In order to achieve real time implementations, a cost
effective architecture is proposed using parallel structure and pipelined
processing. The proposed processor can achieve throughput rate of 45M pixels/s
using only 4k gates and two line buffers. Unlike median filtering chips, this
processor provides better filtering quality and its circuit is much less
complex.

In
22, Deepa Et Al proposed an efficient low cost VLSI architecture for the edge
preserving impulse noise removal technique. The architecture comprises of two
line buffers, register banks, impulse noise detector, edge oriented noise
filter and impulse arbiter. The proposed algorithm involves only fixed size
window instead of variable window size. The storage space required is a two
line buffer. Both, reduces storage requirement as well as computation
complexity. The implemented edge preserving algorithm results in better visual
quality and pipelined architecture results in better visual quality for
denoised image.

CONCLUSION

This
paper surveys different common median filtering techniques. Each technique has
its own advantages, and disadvantages. From literature, we found that most of
the recent median filtering based methods employ two or more than two of these
frame work in order to obtain an improved impulse noise reduction and enhancing
picture quality.