Image denoising is the basic problem in digital image processing. Removing Noise from the image is the main task to denoise the image. Salt & pepper (Impulse) noise and the additive white Gaussian noise and blurredness are the types of noise that occur during transmission and capturing. To remove these types of noise we have many filters like mean filter, median filter, inverse filter, wiener filter. No single one filter can remove both types of noise. So I design a hybrid filter which can be used to denoise these both types of noises from the image.
Speckle Noise Reduction in Ultrasound Images using Adaptive and Anisotropic D...Md. Shohel Rana
US Imaging Technique less cost. Nonlinear and Anisotropic filter for removing speckle noise can be removed from US images. Proposed a modified Anisotropic filter which reduces speckle noises.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Reduction of types of Noises in dental ImagesEditor IJCATR
-This paper presents a filter for restoration of Dental images that are highly corrupted by salt and pepper noise and
speckle noise, Poisson noise. After detecting and correcting the noisy pixel, the proposed filter is able to suppress noise level.
In this paper for each noise proposed different type of filter and compare these three types of filter with their PSNR value and
MSE value and SNR value. After filtering stage maximum detected noise pixels will be filtered and simulation results show
the filtered image.
PERFORMANCE ANALYSIS OF UNSYMMETRICAL TRIMMED MEDIAN AS DETECTOR ON IMAGE NOI...ijistjournal
This Paper Analyze the performance of Unsymmetrical trimmed median, which is used as detector for the detection of impulse noise, Gaussian noise and mixed noise is proposed. The proposed algorithm uses a fixed 3x3 window for the increasing noise densities. The pixels in the current window are arranged in sorting order using a improved snake like sorting algorithm with reduced comparator. The processed pixel is checked for the occurrence of outliers, if the absolute difference between processed pixels is greater than fixed threshold. Under high noise densities the processed pixel is also noisy hence the median is checked using the above procedure. if found true then the pixel is considered as noisy hence the corrupted pixel is replaced by the median of the current processing window. If median is also noisy then replace the corrupted pixel with unsymmetrical trimmed median else if the pixel is termed uncorrupted and left unaltered. The proposed algorithm (PA) is tested on varying detail images for various noises. The proposed algorithm effectively removes the high density fixed value impulse noise, low density random valued impulse noise, low density Gaussian noise and lower proportion of mixed noise. The proposed algorithm is targeted on Xc3e5000-5fg900 FPGA using Xilinx 7.1 compiler version which requires less number of slices, optimum speed and low power when compared to the other median finding architectures.
Speckle Noise Reduction in Ultrasound Images using Adaptive and Anisotropic D...Md. Shohel Rana
US Imaging Technique less cost. Nonlinear and Anisotropic filter for removing speckle noise can be removed from US images. Proposed a modified Anisotropic filter which reduces speckle noises.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Reduction of types of Noises in dental ImagesEditor IJCATR
-This paper presents a filter for restoration of Dental images that are highly corrupted by salt and pepper noise and
speckle noise, Poisson noise. After detecting and correcting the noisy pixel, the proposed filter is able to suppress noise level.
In this paper for each noise proposed different type of filter and compare these three types of filter with their PSNR value and
MSE value and SNR value. After filtering stage maximum detected noise pixels will be filtered and simulation results show
the filtered image.
PERFORMANCE ANALYSIS OF UNSYMMETRICAL TRIMMED MEDIAN AS DETECTOR ON IMAGE NOI...ijistjournal
This Paper Analyze the performance of Unsymmetrical trimmed median, which is used as detector for the detection of impulse noise, Gaussian noise and mixed noise is proposed. The proposed algorithm uses a fixed 3x3 window for the increasing noise densities. The pixels in the current window are arranged in sorting order using a improved snake like sorting algorithm with reduced comparator. The processed pixel is checked for the occurrence of outliers, if the absolute difference between processed pixels is greater than fixed threshold. Under high noise densities the processed pixel is also noisy hence the median is checked using the above procedure. if found true then the pixel is considered as noisy hence the corrupted pixel is replaced by the median of the current processing window. If median is also noisy then replace the corrupted pixel with unsymmetrical trimmed median else if the pixel is termed uncorrupted and left unaltered. The proposed algorithm (PA) is tested on varying detail images for various noises. The proposed algorithm effectively removes the high density fixed value impulse noise, low density random valued impulse noise, low density Gaussian noise and lower proportion of mixed noise. The proposed algorithm is targeted on Xc3e5000-5fg900 FPGA using Xilinx 7.1 compiler version which requires less number of slices, optimum speed and low power when compared to the other median finding architectures.
This presentation contains concepts of different image restoration and reconstruction techniques used nowadays in the field of digital image processing. Slides are prepared from Gonzalez book and Pratt book.
A NOVEL ALGORITHM FOR IMAGE DENOISING USING DT-CWT sipij
This paper addresses image enhancement system consisting of image denoising technique based on Dual Tree Complex Wavelet Transform (DT-CWT) . The proposed algorithm at the outset models the noisy remote sensing image (NRSI) statistically by aptly amalgamating the structural features and textures from it. This statistical model is decomposed using DTCWT with Tap-10 or length-10 filter banks based on
Farras wavelet implementation and sub band coefficients are suitably modeled to denoise with a method which is efficiently organized by combining the clustering techniques with soft thresholding - softclustering technique. The clustering techniques classify the noisy and image pixels based on the
neighborhood connected component analysis(CCA), connected pixel analysis and inter-pixel intensity variance (IPIV) and calculate an appropriate threshold value for noise removal. This threshold value is used with soft thresholding technique to denoise the image .Experimental results shows that that the
proposed technique outperforms the conventional and state-of-the-art techniques .It is also evaluated that the denoised images using DTCWT (Dual Tree Complex Wavelet Transform) is better balance between smoothness and accuracy than the DWT.. We used the PSNR (Peak Signal to Noise Ratio) along with
RMSE to assess the quality of denoised images.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Removing noise from the Medical image is still a challenging problem for researchers. Noise added is not easy to remove from the images. There have been several published algorithms and each approach has its assumptions, advantages, and limitations. This paper summarizes the major techniques to denoise the medical images and finds the one is better for image denoising. We can conclude that the Multiwavelet technique with Soft threshold is the best technique for image denoising.
International Journal of Research in Engineering and Science is an open access peer-reviewed international forum for scientists involved in research to publish quality and refereed papers. Papers reporting original research or experimentally proved review work are welcome. Papers for publication are selected through peer review to ensure originality, relevance, and readability.
A Decision tree and Conditional Median Filter Based Denoising for impulse noi...IJERA Editor
Impulse noise is often introduced into images during acquisition and transmission. Even though so many denoising techniques are existing for the removal of impulse noise in images, most of them are high complexity methods and have only low image quality. Here a low cost, low complexity VLSI architecture for the removal of random valued impulse noise in highly corrupted images is introduced. In this technique a decision- tree- based impulse noise detector is used to detect the noisy pixels and an efficient conditional median filter is used to reconstruct the intensity values of noisy pixels. The proposed technique can improve the signal to noise ratio than any other technique.
Image Denoising is an important part of diverse image processing and computer vision problems. The
important property of a good image denoising model is that it should completely remove noise as far as
possible as well as preserve edges. One of the most powerful and perspective approaches in this area is
image denoising using discrete wavelet transform (DWT). In this paper, comparison of various Wavelets at
different decomposition levels has been done. As number of levels increased, Peak Signal to Noise Ratio
(PSNR) of image gets decreased whereas Mean Absolute Error (MAE) and Mean Square Error (MSE) get
increased . A comparison of filters and various wavelet based methods has also been carried out to denoise
the image. The simulation results reveal that wavelet based Bayes shrinkage method outperforms other
methods.
This presentation contains concepts of different image restoration and reconstruction techniques used nowadays in the field of digital image processing. Slides are prepared from Gonzalez book and Pratt book.
A NOVEL ALGORITHM FOR IMAGE DENOISING USING DT-CWT sipij
This paper addresses image enhancement system consisting of image denoising technique based on Dual Tree Complex Wavelet Transform (DT-CWT) . The proposed algorithm at the outset models the noisy remote sensing image (NRSI) statistically by aptly amalgamating the structural features and textures from it. This statistical model is decomposed using DTCWT with Tap-10 or length-10 filter banks based on
Farras wavelet implementation and sub band coefficients are suitably modeled to denoise with a method which is efficiently organized by combining the clustering techniques with soft thresholding - softclustering technique. The clustering techniques classify the noisy and image pixels based on the
neighborhood connected component analysis(CCA), connected pixel analysis and inter-pixel intensity variance (IPIV) and calculate an appropriate threshold value for noise removal. This threshold value is used with soft thresholding technique to denoise the image .Experimental results shows that that the
proposed technique outperforms the conventional and state-of-the-art techniques .It is also evaluated that the denoised images using DTCWT (Dual Tree Complex Wavelet Transform) is better balance between smoothness and accuracy than the DWT.. We used the PSNR (Peak Signal to Noise Ratio) along with
RMSE to assess the quality of denoised images.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Removing noise from the Medical image is still a challenging problem for researchers. Noise added is not easy to remove from the images. There have been several published algorithms and each approach has its assumptions, advantages, and limitations. This paper summarizes the major techniques to denoise the medical images and finds the one is better for image denoising. We can conclude that the Multiwavelet technique with Soft threshold is the best technique for image denoising.
International Journal of Research in Engineering and Science is an open access peer-reviewed international forum for scientists involved in research to publish quality and refereed papers. Papers reporting original research or experimentally proved review work are welcome. Papers for publication are selected through peer review to ensure originality, relevance, and readability.
A Decision tree and Conditional Median Filter Based Denoising for impulse noi...IJERA Editor
Impulse noise is often introduced into images during acquisition and transmission. Even though so many denoising techniques are existing for the removal of impulse noise in images, most of them are high complexity methods and have only low image quality. Here a low cost, low complexity VLSI architecture for the removal of random valued impulse noise in highly corrupted images is introduced. In this technique a decision- tree- based impulse noise detector is used to detect the noisy pixels and an efficient conditional median filter is used to reconstruct the intensity values of noisy pixels. The proposed technique can improve the signal to noise ratio than any other technique.
Image Denoising is an important part of diverse image processing and computer vision problems. The
important property of a good image denoising model is that it should completely remove noise as far as
possible as well as preserve edges. One of the most powerful and perspective approaches in this area is
image denoising using discrete wavelet transform (DWT). In this paper, comparison of various Wavelets at
different decomposition levels has been done. As number of levels increased, Peak Signal to Noise Ratio
(PSNR) of image gets decreased whereas Mean Absolute Error (MAE) and Mean Square Error (MSE) get
increased . A comparison of filters and various wavelet based methods has also been carried out to denoise
the image. The simulation results reveal that wavelet based Bayes shrinkage method outperforms other
methods.
Adaptive denoising technique for colour imageseSAT Journals
Abstract
In digital image processing noise removal or noise filtering plays an important role, because for meaningful and useful processing images should not be corrupted by noises. In recent years, high quality televisions have become very popular but noise often affects TV broadcasts. Impulse noise corrupts the video during transmission and acquisition of signals. A number of denoising techniques have been introduced to remove impulse noise from images . Linear noise filtering technique does not work well when the noise is non-adaptive in nature and hence a number of non-linear filtering technique where introduced. In non-linear filtering technique, median filters and its modifications where used to remove noise but it resulted in blurring of images. Therefore here we propose an adaptive digital signal processing approach that can efficiently remove impulse noise from colour image. This algorithm is based on threshold which is adaptive in nature. This algorithm replaces the pixel only if it is found to be noisy pixel otherwise the original pixel is retained thus it results a better filtering technique when compared to median filters and its modified filters.
Keywords: impulse noise, Adaptive threshold, Noise detection, colour video
Images of different body organs play very important role in medical diagnosis. Images can be taken
by using different techniques like x-rays, gamma rays, ultrasound etc. Ultrasound images are widely used
as a diagnosis tool because of its non invasive nature and low cost. The medical images which uses the
principle of coherence suffers from speckle noise, which is multiplicative in nature. Ultrasound images are
coherent images so speckle noise is inherited in ultrasound images which occur at the time of image
acquisition. There are many factors which can degrade the quality of image but noise present in ultrasound
image is a prime factor which can negatively affect result while autonomous machine perception. In this
paper we will discuss types of noises and speckle reduction techniques. In the end, study about speckle
reduction in ultrasound of various researchers will be compared.
Performance analysis of image filtering algorithms for mri imageseSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Performance Assessment of Several Filters for Removing Salt and Pepper Noise,...IJEACS
Digital images are prone to a variety of noises. De-noising of image is a crucial fragment of image reconstruction procedure. Noise gets familiarized in the course of reception and transmission, acquisition and storage & recovery processes. Hence de-noising an image becomes a fundamental task for correcting defects produced during these processes. A complete examination of the various noises which corrupt an image is included in this paper. Elimination of noises is done using various filters. To attain noteworthy results various filters have been anticipated to eliminate these noises from Images and finally which filter is most suitable to remove a particular noise is seen using various measurement parameters.
PERFORMANCE ANALYSIS OF UNSYMMETRICAL TRIMMED MEDIAN AS DETECTOR ON IMAGE NOI...ijistjournal
This Paper Analyze the performance of Unsymmetrical trimmed median, which is used as detector for the detection of impulse noise, Gaussian noise and mixed noise is proposed. The proposed algorithm uses a fixed 3x3 window for the increasing noise densities. The pixels in the current window are arranged in sorting order using a improved snake like sorting algorithm with reduced comparator. The processed pixel is checked for the occurrence of outliers, if the absolute difference between processed pixels is greater than fixed threshold. Under high noise densities the processed pixel is also noisy hence the median is checked using the above procedure. if found true then the pixel is considered as noisy hence the corrupted pixel is replaced by the median of the current processing window. If median is also noisy then replace the corrupted pixel with unsymmetrical trimmed median else if the pixel is termed uncorrupted and left unaltered. The proposed algorithm (PA) is tested on varying detail images for various noises. The proposed algorithm effectively removes the high density fixed value impulse noise, low density random valued impulse noise, low density Gaussian noise and lower proportion of mixed noise. The proposed algorithm is targeted on Xc3e5000-5fg900 FPGA using Xilinx 7.1 compiler version which requires less number of slices, optimum speed and low power when compared to the other median finding architectures.
Study and Analysis of Impulse Noise Reduction Filterssipij
In this paper, a new Decision Based median filtering algorithm is presented for the removal of impulse noise from digital images. Here, we replace the impulse noise corrupted pixel by the median of the pixel scanned in four directions.The signal restoration scheme of this filter adapts to the varied impulse noise ratios while determining an appropriate signal restorer from a reliable neighbourhood. The experimental results of this filter applied on various images corrupted with almost all ratios of impulse noise favour the filter in terms of objectivity and subjectivity than many of the other prominent impulse noise filters.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
A fast and effective impulse noise filterIJRES Journal
In order to eliminate the high density salt and pepper noise effectively in the image, this paper
proposes a new algorithm that can eliminate the noise .Other similar algorithms need to adjust the filtering
window in the image which is polluted by different concentration of noise constantly. The proposed algorithm
use the fixed small scale of filtering window only, at the same time of filter, it can reserve the detail of the image
features well. The proposed algorithm extracted the noise points from the contaminated image firstly, according
to the relationship between the gray value of signal points and noise points, then determine which is the real
noise. The experimental results show us that the proposed algorithm achieved satisfactory result in filter out
noise, especially in the treatment of the images that have high levels of noise pollution, and it is better than
other algorithm.
The Performance Analysis of Median Filter for Suppressing Impulse Noise from ...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
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The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
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Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
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1. Council for Innovative Research International Journal of Data & Network Security
www.cirworld.com Volume 1 No.1, Aug , 2012
10 | P a g e w w w . c i r w o r l d . c o m
IMAGE DENOISING USING HYBRID FILTER
Vinod Kumar Anil Kumar Pushpraj Pal
Deenbandhu Chhotu Ram DCTM Palwal SRM Global
University of Science &Technology University of Science & Technology Institute of Engg @ Technology
Haryana, India Haryana, India Haryana, India
ABSTRACT
Image denoising is the basic problem in digital image processing.
Removing Noise from the image is the main task to denoise the image.
Salt & pepper (Impulse) noise and the additive white Gaussian noise and
blurredness are the types of noise that occur during transmission and
capturing. To remove these types of noise we have many filters like mean
filter, median filter, inverse filter, wiener filter. No single one filter can
remove both types of noise. So I design a hybrid filter which can be used
to denoise these both types of noises from the image.
Keywords
Denoising; Salt & Pepper (Impulse) noise; Median filter; Wiener
filter.
1.INTRODUCTION
To denoise the image we have various filters. The main purpose of
denoising is to make the image noise less. The noise may be generated in
any form or at any time. The salt & pepper noise is generated during
analog to digital image conversions or due to the dead pixels in the
image. The additive white gaussian noise is added during the
transmission of images. Also the images are blurred during capturing of
the photos due to the cameras displacement or object movement. So we
have to denoise the images from the noise. The various filters are used to
denoise the images which are contaminated these types of noise which
are explained below:
2. MEDIAN FILTER
Median filtering is a nonlinear operation used in image processing to
reduce "salt and pepper" noise. Also Mean filter is used to remove the
impulse noise. Mean filter replaces the mean of the pixels values but it
does not preserve image details. Some details are removes with the mean
filter. In the median filter, we do not replace the pixel value with the
mean of neighboring pixel values, we replaces with the median of those
values. The median is calculated by first sorting all the pixel values from
the surrounding neighborhood into numerical order and then replacing the
pixel being considered with the middle pixel value. (If the neighboring
pixel which is to be considered contains an even number of pixels, than
the average of the two middle pixel values is used.) Fig.1.1 illustrates an
example calculation.
Fig.1:Exp. of median filtering
The median filter gives best result when the impulse noise percentage is
less than 0.1 %. When the quantity of impulse noise is increased the
median filter not gives best result.
3. WIENER FILTER
The main purpose of the Wiener filter is to filter out the noise that has
corrupted a signal. Wiener filter is based on a statistical approach. Mostly
filters are designed for a desired frequency response. The Wiener filter
deals with the filtering of image from a different point of view. One
method is to assume that, we have knowledge of the spectral properties of
the original signal and the noise, and one deals with the Linear Time
Invarient filter whose output comes to be as closed as to the original
signal as possible [1]. Wiener filters are characterized by the following
assumption:
a. signal and (additive white gaussian noise) noise are stationary linear
random processes with known spectral characteristics.
b. Requirement: the filter must be physically realizable, i.e. causal (this
requirement can be dropped, resulting in a non-causal solution).
c. Performance criteria of wiener filter: minimum mean-square error.
4. WIENER FILTER IN THE FOURIER
DOMAIN
The wiener filter is given by following transfer
Function:
G (u, v) =
Dividing the equation by Ps makes its behaviour easier to explain:
G (u, v) =
Where
H (u, v) = Degradation function
H*(u, v) = Complex conjugate of degradation function
Pn (u, v) = Power Spectral Density of Noise
Ps (u, v) = Power Spectral Density of un-degraded image.
The term Pn /Ps is the reciprocal of the signal-to-noise ratio.
5.IMAGE NOISE
Image noise is the degradation of the quality of the image. Image noise is
produced due to the random variation of the brightness or the color
information in images that is produced by the sensor’s and the circuitry of
the scanner or digital cameras. Image noise can also originate in film
grain and in the unavoidable shot noise of an ideal photon detector. Image
noise is generally regarded as an undesirable by-product of image
capture. The types of Noise are following:-
Additive White Gaussian noise
Salt-and-pepper noise
Blurredness
6. ADDITIVE WHITE GAUSSIAN NOISE
The Additive White Gaussian noise to be present in images is
independent at each pixel and signal intensity. In color cameras where
2. Council for Innovative Research International Journal of Data & Network Security
www.cirworld.com Volume 1 No.1, Aug , 2012
11 | P a g e w w w . c i r w o r l d . c o m
more amplification is used in the blue color channel than in the green or
red channel, there can be more noise in the blue channel.
7. SALT-AND-PEPPER NOISE
The image which has salt-and-pepper noise present in image will show
dark pixels in the bright regions and bright pixels in the dark regions. [2].
The salt & pepper noise in images can be caused by the dead pixels, or
due to analog-to-digital conversion errors, or bit errors in the
transmission, etc. This all can be eliminated in large amount by using the
technique dark frame subtraction and by interpolating around dark/bright
pixels.
8. BLURREDNESS
The blurredness of the image is depend on the point spread function (psf).
The psf may circular or linear. The image is blurred due to the camera
movement or the object displacement.
9. HYBRID FILTER
This hybrid filter is the combination of Median and wiener filter. When
we arrange these filters in series we get the desired output. First we
remove the impulse noise and then pass the result to the wiener filter.
The wiener filter removes the additive white noise or blurring effect from
the image.
Fig. 1.2 Hybrid filter structure
10. MSE & PSNR
The term MSE (mean square error) is the difference between the original
image and the recovered image and it should be as minimum as possible.
The term peak signal-to-noise ratio, PSNR, is the ratio between the
maximum possible power of a signal and the power of corrupting noise
signal.
MSE=
The PSNR is defined as:
PSNR = 10 .
=20. where,
MAXI is the maximum possible pixel value of the image.
11. SIMULATION RESULT
We perform the result on the original lena image. We assume that three
types of noise are randomly added in to the images. We have to remove
these noise from the images. We pass the image from our hybrid filter
and calculate the MSE & PSNR parameters of our output image. The
MSE is minimum when the blurring effect is less and PSNR is maximum.
The following diagram shows the results of the operations:
Original Lena Image Blurred,Gaussian ,Impulse Noisy Image
Blurring LEN=40,THETA=40
Input Of Hybrid Filter
ImpulseNoisepercentage=0.01
MSE=0.019 , PSNR=65.379
Output of hybrid Filter
Blurring LEN=40,THETA=40
Input Of Hybrid Filter
ImpulseNoisepercentage=0.1
MSE=0.054 , PSNR =60.127
Output of hybrid Filter
Blurring LEN=21,THETA=21
Input Of Hybrid Filter
ImpulseNoisepercentage=0.01
MSE=0.019 , PSNR=65.457
Output of hybrid Filter
Blurring LEN=5,THETA=5
Input Of Hybrid Filter
ImpulseNoisepercentage=0.01
MSE=0.009 , PSNR=68.804
Output of hybrid Filter
Fig. 1: Original lena image. Fig.2: Blurred, Gaussian, Impulse Noisy
Image Fig.3: Noisy Image with Blurring Length=40,Blurring Angle=40
& Impulse Noise =0.01%. Fig.4 :Output of Hybrid filter corresponding to
the input of fig 3 Fig. 5: Noisy Image with Blurring length=40,Blurring
Angle=40, Impulse Noise=0.1%. Fig 6 Output of Hybrid Filter
Corresponding to Fig 5 Fig.7 Noisy Image with Blurring
Length=21,Blurring Angle=11,Impulse Noise =0.01%.Fig.8 Output of
Hybrid filter corresponding to the input of fig 7. Fig 9 Noisy Image with
Blurring Length=5,Blurring Angle=5 & Impulse Noise =0.01% Fig 10
Output of Hybrid filter corresponding to the input of fig 9.
Now we have to calculate the mean square error for
the different conditions to check the performance of
our filter.
The Table 1 shows that when the blurredness of the image vary with
angle and length and the percentage of impulse noise is constant.
Table.1
Blurr
ed
length
Blurrin
g
Angle
Percentag
e of
impulse
noise( %)
Mean
square
error
Peak
Signal to
Noise
ratio
45 45 0.01 0.019 65.379
3. Council for Innovative Research International Journal of Data & Network Security
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12 | P a g e w w w . c i r w o r l d . c o m
35 35 0.01 0.018 65.641
25 25 0.01 0.017 65.903
15 15 0.01 0.014 66.608
5 5 0.01 0.009 68.804
From the above table we conclude that when the blurring effect is varying
and impulse noise is fixed the MSE is decreasing and PSNR is increasing.
Next Table 2 shows the results of MSE & PSNR when the blurredness of
the image is same and the percentage of the impulse noise is increased,
then the following results are obtained:
Table.2
Blurr
ed
length
Blurrin
g
Angle
Percentag
e of
impulse
noise( %)
Mean
square
error
Peak
Signal to
Noise
ratio
40 40 0.01 0.019 65.379
40 40 0.03 0.023 64.619
40 40 0.05 0.035 62.715
40 40 0.07 0.037 62.522
40 40 0.09 0.053 60.954
40 40 0.1 0.054 60.127
From the above table we conclude that when the percentage of impulse
noise is increasing the PSNR is decreasing. More is the noise less is the
signals strength. We cannot exactly recover the original image.
From the above MSE & PSNR we conclude that the image details are
recovered back exactly when the noise percentage is less. The MSE is
minimum and PSNR is maximum for the better results. We check from
the above data that PSNR is increasing when the impulse noise
percentage is decreasing.
Next when the blurredness and impulse noise is
Simultaneously varying means both the parameters are varying in that
case , we get the following results
Table. 3
Blurr
ed
length
Blurrin
g
Angle
Percentag
e of
impulse
noise( %)
Mean
square
error
Peak
Signal to
Noise
ratio
21 11 0.01 0.019 65.457
15 09 0.02 0.022 64.676
10 05 0.01 0.011 67.819
10 05 0.03 0.018 65.654
05 03 0.01 0.010 68.031
05 03 0.04 0.018 65.726
12. CONCLUSION
We used the Lena image in .jpg format. Adding three noise (impulse
noise, gaussian noise, blurredness) and apply the noisy image to hybrid
filter. The final filtered image is depending upon the blurring angle and
the blurring length and the percentage of the impulse noise. When these
variables are less the filtered image is nearly equal to the original image.
The MSE is decreasing and PSNR is increasing when the noise
percentage is less.
13. REFERENCES
.1]Wavelet domain image de-noising by thresholding and Wiener
filtering by Kazubek M. Signal Processing Letters IEEE, Volume: 10,
Issue no. 11, Nov. 2003 265 Vol.3.
[2]A hybrid filter for image enhancement by Shaomin Peng and Lori
Lucke Department of Electrical Engineering University of Minnesota
Minneapolis, MN 55455
[3] Image Denoising using Wavelet Thresholding and Model Selection by
Shi Zhong. Image Processing, 2000, Proceedings, 2000 International
Conference held on, Volume: 3, 10-13 Sept. 2000 Pages: 262.
[4] Performance Comparison of Median and Wiener Filter in Image De-
noising by suresh kumar,. International Journal of Computer Applications
Page No.( 0975 –8887) Volume 12– No.4, November 2010
[5] Multi-level Adaptive Fuzzy Filter for Mixed Noise Removal by
Shaomin Peng and Lori Lucke. Department of Electrical Engineering
University of JIinnesota