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.
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 HYBRID FILTERING TECHNIQUE FOR ELIMINATING UNIFORM NOISE AND IMPULSE NOIS...sipij
A new hybrid filtering technique is proposed to improving denoising process on digital images.
This technique is performed in two steps. In the first step, uniform noise and impulse noise is
eliminated using decision based algorithm (DBA). Image denoising process is further improved
by an appropriately combining DBA with Adaptive Neuro Fuzzy Inference System (ANFIS) at
the removal of uniform noise and impulse noise on the digital images. Three well known images
are selected for training and the internal parameters of the neuro-fuzzy network are adaptively
optimized by training. This technique offers excellent line, edge, and fine detail preservation
performance while, at the same time, effectively denoising digital images. Extensive simulation
results were realized for ANFIS network and different filters are compared. Results show that
the proposed filter is superior performance in terms of image denoising and edges and fine
details preservation properties.
AN EMERGING TREND OF FEATURE EXTRACTION METHOD IN VIDEO PROCESSINGcscpconf
Recently the progress in technology and flourishing applications open up new forecast and defy
for the image and video processing community. Compared to still images, video sequences
afford more information about how objects and scenarios change over time. Quality of video is
very significant before applying it to any kind of processing techniques. This paper deals with
two major problems in video processing they are noise reduction and object segmentation on
video frames. The segmentation of objects is performed using foreground segmentation based
and fuzzy c-means clustering segmentation is compared with the proposed method Improvised
fuzzy c – means segmentation based on color. This was applied in the video frame to segment
various objects in the current frame. The proposed technique is a powerful method for image
segmentation and it works for both single and multiple feature data with spatial information.
The experimental result was conducted using various noises and filtering methods to show which is best suited among others and the proposed segmentation approach generates good quality segmented frames.
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 HYBRID FILTERING TECHNIQUE FOR ELIMINATING UNIFORM NOISE AND IMPULSE NOIS...sipij
A new hybrid filtering technique is proposed to improving denoising process on digital images.
This technique is performed in two steps. In the first step, uniform noise and impulse noise is
eliminated using decision based algorithm (DBA). Image denoising process is further improved
by an appropriately combining DBA with Adaptive Neuro Fuzzy Inference System (ANFIS) at
the removal of uniform noise and impulse noise on the digital images. Three well known images
are selected for training and the internal parameters of the neuro-fuzzy network are adaptively
optimized by training. This technique offers excellent line, edge, and fine detail preservation
performance while, at the same time, effectively denoising digital images. Extensive simulation
results were realized for ANFIS network and different filters are compared. Results show that
the proposed filter is superior performance in terms of image denoising and edges and fine
details preservation properties.
AN EMERGING TREND OF FEATURE EXTRACTION METHOD IN VIDEO PROCESSINGcscpconf
Recently the progress in technology and flourishing applications open up new forecast and defy
for the image and video processing community. Compared to still images, video sequences
afford more information about how objects and scenarios change over time. Quality of video is
very significant before applying it to any kind of processing techniques. This paper deals with
two major problems in video processing they are noise reduction and object segmentation on
video frames. The segmentation of objects is performed using foreground segmentation based
and fuzzy c-means clustering segmentation is compared with the proposed method Improvised
fuzzy c – means segmentation based on color. This was applied in the video frame to segment
various objects in the current frame. The proposed technique is a powerful method for image
segmentation and it works for both single and multiple feature data with spatial information.
The experimental result was conducted using various noises and filtering methods to show which is best suited among others and the proposed segmentation approach generates good quality segmented frames.
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.
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.
Adaptive Noise Reduction Scheme for Salt and Peppersipij
In this paper, a new adaptive noise reduction scheme for images corrupted by impulse noise is presented. The proposed scheme efficiently identifies and reduces salt and pepper noise. MAG (Mean Absolute Gradient) is used to identify pixels which are most likely corrupted by salt and pepper noise that are candidates for further median based noise reduction processing. Directional filtering is then applied after noise reduction to achieve a good tradeoff between detail preservation and noise removal. The proposed scheme can remove salt and pepper noise with noise density as high as 90% and produce better result in terms of qualitative and quantitative measures of images.
Novel adaptive filter (naf) for impulse noise suppression from digital imagesijbbjournal
In general, it is known that an adaptive filter adjusts its parameters iteratively such as size of the working
window, decision threshold values used in two stage detection-estimation based switching filters, number of
iterations etc. It is also known that nonlinear filters such as median filters and its several variants are
popularly known for their ability in dealing with the unknown circumstances. In this paper an efficient and
simple adaptive nonlinear filtering scheme is presented to eliminate the impulse noise from the digital images with an impulsive noise detection and reduction scheme based on adaptive nonlinear filter techniques. The proposed scheme employs image statistics based dynamically varying working window and an adaptive threshold for noise detection with a Noise Exclusive Median (NEM) based restoration. The intensity value of the Noise Exclusive Median (NEM) is derived from the processed pixels in local
neighborhood of a dynamically adaptive window. In the proposed scheme use of an adaptive threshold value derived from the noisy image statistics returns more precise results for the noisy pixel detection. The
proposed scheme is simple and can be implemented as either a single pass or a multi-pass with a maximum
of three iterations with a simple stopping criterion. The goodness of the proposed scheme is evaluated with respect to the qualitative and quantitative measures obtained by MATLAB simulations with standard images added with impulsive noise of varying densities. From the comparative analysis it is evident that the proposed scheme out performs the state-of-art schemes, preferably in cases of high-density impulse noise
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Filter for Removal of Impulse Noise By Using Fuzzy LogicCSCJournals
Digital image processing is a subset of the electronic domain wherein the image is converted to an array of small integers, called pixels, representing a physical quantity such as scene radiance, stored in a digital memory, and processed by computer or other digital hardware. Fuzzy logic represents a good mathematical framework to deal with uncertainty of information. Fuzzy image processing [4] is the collection of all approaches that understand, represent and process the images, their segments and features as fuzzy sets. The representation and processing depend on the selected fuzzy technique and on the problem to be solved. This paper combines the features of Image Enhancement and fuzzy logic. This research problem deals with Fuzzy inference system (FIS) which help to take the decision about the pixels of the image under consideration. This paper focuses on the removal of the impulse noise with the preservation of edge sharpness and image details along with improving the contrast of the images which is considered as the one of the most difficult tasks in image processing.
Adapter Wavelet Thresholding for Image Denoising Using Various Shrinkage Unde...muhammed jassim k
At Softroniics we provide job oriented training for freshers in IT sector. We are Pioneers in all leading technologies like Android, Java, .NET, PHP, Python, Embedded Systems, Matlab, NS2, VLSI etc. We are specializiling in technologies like Big Data, Cloud Computing, Internet Of Things (iOT), Data Mining, Networking, Information Security, Image Processing, Mechanical, Automobile automation and many other. We are providing long term and short term internship also.
We are providing short term in industrial training, internship and inplant training for Btech/Bsc/MCA/MTech students. Attached is the list of Topics for Mechanical, Automobile and Mechatronics areas.
MD MANIKANDAN-9037291113,04954021113
softroniics@gmail.com
www.softroniics.com
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.
Performance of Various Order Statistics Filters in Impulse and Mixed Noise Re...sipij
Remote sensing images (ranges from satellite to seismic) are affected by number of noises like interference, impulse and speckle noises. Image denoising is one of the traditional problems in digital image processing, which plays vital role as a pre-processing step in number of image and video applications. Image denoising still remains a challenging research area for researchers because noise
removal introduces artifacts and causes blurring of the images. This study is done with the intension of designing a best algorithm for impulsive noise reduction in an industrial environment. A review of the typical impulsive noise reduction systems which are based on order statistics are done and particularized for the described situation. Finally, computational aspects are analyzed in terms of PSNR values and some solutions are proposed.
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.
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.
Adaptive Noise Reduction Scheme for Salt and Peppersipij
In this paper, a new adaptive noise reduction scheme for images corrupted by impulse noise is presented. The proposed scheme efficiently identifies and reduces salt and pepper noise. MAG (Mean Absolute Gradient) is used to identify pixels which are most likely corrupted by salt and pepper noise that are candidates for further median based noise reduction processing. Directional filtering is then applied after noise reduction to achieve a good tradeoff between detail preservation and noise removal. The proposed scheme can remove salt and pepper noise with noise density as high as 90% and produce better result in terms of qualitative and quantitative measures of images.
Novel adaptive filter (naf) for impulse noise suppression from digital imagesijbbjournal
In general, it is known that an adaptive filter adjusts its parameters iteratively such as size of the working
window, decision threshold values used in two stage detection-estimation based switching filters, number of
iterations etc. It is also known that nonlinear filters such as median filters and its several variants are
popularly known for their ability in dealing with the unknown circumstances. In this paper an efficient and
simple adaptive nonlinear filtering scheme is presented to eliminate the impulse noise from the digital images with an impulsive noise detection and reduction scheme based on adaptive nonlinear filter techniques. The proposed scheme employs image statistics based dynamically varying working window and an adaptive threshold for noise detection with a Noise Exclusive Median (NEM) based restoration. The intensity value of the Noise Exclusive Median (NEM) is derived from the processed pixels in local
neighborhood of a dynamically adaptive window. In the proposed scheme use of an adaptive threshold value derived from the noisy image statistics returns more precise results for the noisy pixel detection. The
proposed scheme is simple and can be implemented as either a single pass or a multi-pass with a maximum
of three iterations with a simple stopping criterion. The goodness of the proposed scheme is evaluated with respect to the qualitative and quantitative measures obtained by MATLAB simulations with standard images added with impulsive noise of varying densities. From the comparative analysis it is evident that the proposed scheme out performs the state-of-art schemes, preferably in cases of high-density impulse noise
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Filter for Removal of Impulse Noise By Using Fuzzy LogicCSCJournals
Digital image processing is a subset of the electronic domain wherein the image is converted to an array of small integers, called pixels, representing a physical quantity such as scene radiance, stored in a digital memory, and processed by computer or other digital hardware. Fuzzy logic represents a good mathematical framework to deal with uncertainty of information. Fuzzy image processing [4] is the collection of all approaches that understand, represent and process the images, their segments and features as fuzzy sets. The representation and processing depend on the selected fuzzy technique and on the problem to be solved. This paper combines the features of Image Enhancement and fuzzy logic. This research problem deals with Fuzzy inference system (FIS) which help to take the decision about the pixels of the image under consideration. This paper focuses on the removal of the impulse noise with the preservation of edge sharpness and image details along with improving the contrast of the images which is considered as the one of the most difficult tasks in image processing.
Adapter Wavelet Thresholding for Image Denoising Using Various Shrinkage Unde...muhammed jassim k
At Softroniics we provide job oriented training for freshers in IT sector. We are Pioneers in all leading technologies like Android, Java, .NET, PHP, Python, Embedded Systems, Matlab, NS2, VLSI etc. We are specializiling in technologies like Big Data, Cloud Computing, Internet Of Things (iOT), Data Mining, Networking, Information Security, Image Processing, Mechanical, Automobile automation and many other. We are providing long term and short term internship also.
We are providing short term in industrial training, internship and inplant training for Btech/Bsc/MCA/MTech students. Attached is the list of Topics for Mechanical, Automobile and Mechatronics areas.
MD MANIKANDAN-9037291113,04954021113
softroniics@gmail.com
www.softroniics.com
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.
Performance of Various Order Statistics Filters in Impulse and Mixed Noise Re...sipij
Remote sensing images (ranges from satellite to seismic) are affected by number of noises like interference, impulse and speckle noises. Image denoising is one of the traditional problems in digital image processing, which plays vital role as a pre-processing step in number of image and video applications. Image denoising still remains a challenging research area for researchers because noise
removal introduces artifacts and causes blurring of the images. This study is done with the intension of designing a best algorithm for impulsive noise reduction in an industrial environment. A review of the typical impulsive noise reduction systems which are based on order statistics are done and particularized for the described situation. Finally, computational aspects are analyzed in terms of PSNR values and some solutions are proposed.
How to Spot and Cope with Emerging Transitions in Complex Systems for Organiz...Eric Garland
A presentation on how our current situation of global economic transition requires a new approach to organizational learning and decision making. We are ending the era of authoritarian intelligence and moving toward more of a peer-to-peer approach toward decoding changes in the world.
For more check out https://www.competitivefutures.com/ and http://www.ericgarland.co/keynote-speaker-executive-educator/
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.
Published Date:
Saturday, January 1, 1994
Author:
Justice S.M. Daud & Justice H. Suresh
An enquiring into the Dec. '92 & Jan. '93 riots in Bombay by the Indian People's Human Rights Tribunal, conducted by Justice S.M. Daud & Justice H. Suresh
Main Author: Daud, S. M.
Other Authors: Suresh, H.
Language(s): English
Published: Bombay : Indian People's Human Rights Commission, 1994.
Applied Enterprise Semantic Mining -- Charlotte 201410Mark Tabladillo
Text mining is projected to dominate data mining, and the reasons are evident: we have more text available than numeric data. Microsoft introduced a new technology to SQL Server 2014 called Semantic Search. This session's detailed description and demos give you important information for the enterprise implementation of Tag Index and Document Similarity Index, and will also provide a comparison between what semantic search is and what Delve does. The demos include a web-based Silverlight application, and content documents from Wikipedia. We'll also look at strategy tips for how to best leverage the new semantic technology with existing Microsoft data mining.
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
Edge Detection with Detail Preservation for RVIN Using Adaptive Threshold Fil...iosrjce
Images are often corrupted by impulse noise in the procedures of image acquisition and
transmission. In this paper we proposes a method for effective detection of noisy pixel based on median value
and an efficient algorithm for the estimation and replacement of noisy pixel, the replacement of noisy pixel is
carried out twicewhich provides better preservation of image details. The presence of high performing detection
stage for the detection noisy pixel makes the proposed method suitable in the case of noiselevels as high as 60%
to 90% random valued impulse noise; the proposed method yields better image quality.
An Efficient Image Denoising Approach for the Recovery of Impulse NoisejournalBEEI
Image noise is one of the key issues in image processing applications today. The noise will affect the quality of the image and thus degrades the actual information of the image. Visual quality is the prerequisite for many imagery applications such as remote sensing. In recent years, the significance of noise assessment and the recovery of noisy images are increasing. The impulse noise is characterized by replacing a portion of an image’s pixel values with random values Such noise can be introduced due to transmission errors. Accordingly, this paper focuses on the effect of visual quality of the image due to impulse noise during the transmission of images. In this paper, a hybrid statistical noise suppression technique has been developed for improving the quality of the impulse noisy color images. We further proved the performance of the proposed image enhancement scheme using the advanced performance metrics.
Nonlinear Transformation Based Detection And Directional Mean Filter to Remo...IJMER
In this paper, a novel two stage algorithm for the removal of random valued impulse noise
from the images is presented. In the first stage the noise pixels are detected by using an exponential
nonlinear function. The transformation of the pixels increases the gap between noisy and noise free
candidates which leads to an efficient detection. In the second stage, the directional differences between
the pixels in the four main directions are calculated. The mean values of the pixels which lie in the
direction of minimum difference are calculated and the noisy pixel values are replaced with the mean
value of the pixels lying in the direction of minimum difference. Experimental results show that proposed
method is superior to the conventional methods in peak signal to noise ratio.
Ultrasound images and SAR i.e. synthetic aperture radar images are usually corrupted because of speckle
noise also called as granular noise. It is quite a tedious task to remove such noise and analyze those
corrupted images. Till now many researchers worked to remove speckle noise using frequency domain
methods, temporal methods, and adaptive methods. Different filters have been developed as Mean and
Median filters, Statistic Lee filter, Statistic Kuan filter, Frost filter, Srad filter. This paper reviews filters
used to remove speckle noise.
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.
Image Filtering Using all Neighbor Directional Weighted Pixels: Optimization ...sipij
In this paper a novel approach for de noising images corrupted by random valued impulses has been proposed. Noise suppression is done in two steps. The detection of noisy pixels is done using all neighbor directional weighted pixels (ANDWP) in the 5 x 5 window. The filtering scheme is based on minimum variance of the four directional pixels. In this approach, relatively recent category of stochastic global optimization technique i.e., particle swarm optimization (PSO) has also been used for searching the parameters of detection and filtering operators required for optimal performance. Results obtained shows better de noising and preservation of fine details for highly corrupted images.
A Hybrid Filtering Technique for Random Valued Impulse Noise Elimination on D...IDES Editor
A novel adaptive network fuzzy inference system
(ANFIS) based filter is presented for the enhancement of
images corrupted by random valued impulse noise (RVIN).
This technique is performed in two steps. In the first step,
impulse noise using an Asymmetric Trimmed Median Filter
(ATMF). In the second step, image restoration is obtained by
an appropriately combining ATMF with ANFIS at the removal
of higher level of RVIN on the digital images. Three well
known images are selected for training and the internal
parameters of the neuro-fuzzy network are adaptively
optimized by training. This technique offers excellent line,
edge, and fine detail preservation performance while, at the
same time, effectively enhancing digital images. Extensive
simulation results were realized for ANFIS network and
different filters are compared. Results show that the proposed
filter is superior performance in terms of image denoising
and edges and fine details preservation properties.
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.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
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
Random Valued Impulse Noise Elimination using Neural FilterEditor IJCATR
A neural filtering technique is proposed in this paper for restoring the images extremely corrupted with random valued impulse noise. The proposed intelligent filter is carried out in two stages. In first stage the corrupted image is filtered by applying an asymmetric trimmed median filter. An asymmetric trimmed median filtered output image is suitably combined with a feed forward neural network in the second stage. The internal parameters of the feed forward neural network are adaptively optimized by training of three well known images. This is quite effective in eliminating random valued impulse noise. Simulation results show that the proposed filter is superior in terms of eliminating impulse noise as well as preserving edges and fine details of digital images and results are compared with other existing nonlinear filters.
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.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
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A Decision tree and Conditional Median Filter Based Denoising for impulse noise in images
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A Decision tree and Conditional Median Filter Based Denoising for impulse noise in images Shiby. S, PG Scholar1, Asha Sunil, Asst.Professor2 *(Department of electronics and communication, Younus College of Engineering and Technology, kollam dist., India) ** (Department of electronics and communication, Younus College of Engineering and Technology, kollam dist., India) ABSTRACT
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.
I. INTRODUCTION
Now a day’s visual information transmitted in the form of digital images is becoming a major method of communication, but the image obtained after transmission is often corrupted with noise. Noise hides the important details of images. To enhance the image qualities, we have to remove noises from the images without any loss of information. Image denoising is one such powerful methodology which is deployed to remove the noise through the manipulation of the image data to produce very high quality images. These noises are appeared on the images in different ways :at the time of acquisition due to noisy sensors, due to faulty scanner or due to faulty digital camera, due to transmission channel errors, due to corrupted storage media. Impulse noise in image is present due to bit errors in transmission or induced during the signal acquisition stage. There are two types of impulse noise, like salt and pepper noise [9] and random valued noise. Salt and pepper noise can corrupt the images where the corrupted pixel takes either maximum or minimum gray level. The removal of noise from image is known as denoising. The important property of a good image denoising model is that, it should completely remove the noise as far as possible as well as preserve edge, i.e. linear filtering and non linear filtering. In linear filtering denoising techniques [5],[6],[7],[10] is directly applied to the image pixel without checking the availability of noisy and non noisy pixels. The example of linear filtering is mean filter. The disadvantage of this filter is it will affect the quality of non noisy pixels. In the case of non linear filter,
this is done by two steps. First step detection then filtering. Non linear filtering techniques are implemented widely because of their superior performance in removing salt and pepper noise and also preserving fine details of image. There are many works on the restoration of images corrupted by salt and pepper noise. The median filter was once the most popular non linear filter for removing impulse noise, because of its good denoising power and computational efficiency. Median filters are known for their capability to remove impulse noise as well as preserve the edges. In image processing [2],[3] many methods have been developed for the removal of impulse noise in images. The standard median filter [10] is such technique for the removal of image impulse noise. This technique has the disadvantage of poor image quality obtained after the de-noising. This might blur the image because it modifies both noisy and noisy free pixels. In order to overcome this disadvantage of standard median filter new technique switching median filter have been introduced. The switching median filter consists of two main steps an impulse detector to detect the noisy pixels and an impulse noise filter filters the noisy pixels. The advantage of this technique is that it effectively removes the noisy pixels only rather than the whole pixels of the image to avoid causing damage on noisy-free pixels. Luo proposed another technique An Alpha Trimmed Mean Based Method (ATMBM) [11]. It uses alpha trimmed mean for impulse noise detection and the detected noisy pixel values is replaced by the original detected value and the median value of its local window. A Differential Rank Impulse Detector (DRID) was presented in [4]. In DRID impulse detector works on the comparison
RESEARCH ARTICLE OPEN ACCESS
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of signal samples within a narrow rank window by considering both rank and absolute values. Based on the complexity the de-noising techniques have been classified into lower complexity [5],[6] and higher complexity technique [4],[9] .The lower complexity technique provides a good quality for the reconstructed image. In the field VLSI reduction of chip area is found to be one important criteria and the new denoising technique decision tree based de- noising method (DTBDM) using a conditional median filter is introduced for the removal of salt and pepper noise in images. The decision tree is a simple but powerful tool for the complex multivariable analysis. It can breakdown a complex decision making process into simpler one and finds better solution for the problem. To enhance the effects of removal of impulse noise the reconstructed pixels have been written back as a part of input data. Especially, it can remove the noise from corrupted images efficiently and requires no previous training.
II. PROPOSED DECISION TREE AND CONDITIONAL MEDIAN FILTER BASED DENOISING METHOD
In this method a 3×3 mask is used for the denoising of the image. Let us consider the image pixel to be deniosed is located at the coordinate (i,j) and it is denoted as pi,j and its luminance value is named as fi,j as shown in Fig. 1. We divide eight pixel values except the central pixel within the mask into two sets: WTopHalf and WBottomHalf. They are given as WTopHalf = {a,b,c,d}. (1) WBottomHalf = {e,f,g,h}. (2) The main components of decision tree and conditional median filter based denoising method (DTCMBDM) Decision tree based impulse detector and conditional median filter. The detector determines whether pi,j is a noisy pixel by using the decision tree and the correlation between pixel pi,j and its neighboring pixels. If the result is positive, a modified conditional median filter generates the reconstructed value. Otherwise the value will be kept unchanged. The design concept of DTCMBDM is shown in fig. 2. Fig 2.1 A 3×3 mask centered on Pi,j
are decision-tree-based impulse detector and conditional median filter. The detector determines whether pi,j is a noisy pixel by using the decision tree and the correlation between pixel pi,j and its neighboring pixels. If the result is positive, a modified conditional median filter generates the reconstructed value. Otherwise the value will be kept unchanged. The design concept of DTCMBDM is shown in fig. 2.
2.1 Decision- Tree Based impulse detector
We can determine whether the Pi,j is a noisy pixel using the correlation between Pi,j and neighboring pixels [10]. In the decision tree based impulse noise detector we have three modules- isolation module (IM), fringe module (FM), Similarity module (SM). Three concatenating decisions of these modules build a decision tree. The decision tree is a binary tree and can determine the status of Pi,j by using different equations in three different modules. If the result of the isolation module is negative we can say that the current pixel belongs to noisy free. If the result is positive it means that the current might be a noisy pixel or just situated on an edge. The fringe module is used to confirm the result. If the current pixel is situated on an edge, the result of fringe module will be negative (noisy free), otherwise the result will be positive. If the isolation module and fringe module cannot determine whether the current pixel belongs to noisy free, similarity module is used to confirm the result. It compares the similarity between current pixel and its neighboring pixels. If the result is positive, Pi,j is noisy pixel; otherwise it is noise free. The following section will explain the three modules in detail.
2.1.1 Isolation module
Isolation module is the first module. In this module we check whether the current pixel is an isolation point by observing the smoothness of the surrounding pixels. The pixel with shadow suffering from noise have low similarity with the neighboring pixels is called isolation point. The difference between it and its neighboring pixel value is large. Using this concept, we determine the maximum and minimum luminance values in WTopHalf, named as TopHalf_max, TopHalf_min, and calculate the difference between them, named as Tophalf_diff. For WBottomHalf the same concept is used to obtain the BottomHalf_diff. The difference values are compared with a threshold Th_IMa to decide whether the surrounding pixel belong to smooth area. The equations are as follows. Tophalf_diff = TopHalf_max -TopHalf_min. (3) BottomHalf_diff= BottomHalf_max-BottomHalf_min (4)
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true, if(Tophalf_diff≥Th_IMa) or(BottomHalf_diff≥Th_IMa) Decision I= false, otherwise. (5) Fig 2.2 Dataflow of DTCMBDM IM_TopHalf
true, if(|fi,j-TopHalf_max|≥Th_IMb) Or(|fi,j-TopHalf_min|≥Th_IMb) = false, otherwise (6) IM_BottomHalf
true, if(|fi,j-BottomHalf_max|≥Th_IMb) Or(|fi,j-BottomHalf_min|≥Th_IMb) = false, otherwise. (7)
true, if ( Tophalf_diff≥Th_IMa) or(BottomHalf_diff≥Th_IMa) Decision II = false, otherwise. (8) Finally, we make a temporary decision whether Pi,j belongs to a suspected noisy pixel or is noisy free
2.1.2 Fringe Module
In this module we determine whether the current pixel is a noisy pixel or just situated on an edge. Inorder to deal with this case, we define four directions, from E1 to E4, as shown in Fig. 4. By calculating the absolute difference between fi,j and values other pixel values along the same direction, we can determine whether there is an edge or not. The detailed equations are as
false, if (|a-fi,j|≥Th_FMa) or (|h-fi,j|≥ Th_FMa) FM_E1 = or (|a-h|≥Th_FMb) true, otherwise. (9)
false, if (|c-fi,j|≥ Th_FMa) or(|f-fi,j|≥ Th_FMa) or (|c-f|≥Th_FMb) FM_E2 = true, otherwise. (10)
false, if (|b-fi,j|≥ Th_FMa) or(|g-fi,j|≥ Th_FMa) FM_E3 = or (|b-g|≥Th_FMb) true, otherwise. (11)
false, if (|b-fi,j|≥ Th_FMa) or (|g-fi,j|≥ Th_FMa) FM_E4 = or (|b-g|≥Th_FMb) True, otherwise. (12)
false, if ( FM_E1) or (FM_E2) or(FM_E3) or (FM_E4) Decision III = true, otherwise. (13) Fig 2.3 Four directions in DTCMBDM
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2.1.3 Similarity Module
Similarity module is the last module. The median is always located in the center of the variational series, while the impulse is usually located near one of its ends. Hence if there are extreme big or small values, that implies the possibility of noisy. According to this concept, we sort nine signals values within the mask in ascending order in which the fourth, fifth, and sixth values are represented as 4thinWi,j, MedianInWi,j, and 6thinWi,j. We can define Maxi,j and Minxi,j as Maxi,j = 6thinWi,j +Th_SMa, Mini,j = 4thinWi,j -Th_SMa, Maxi,j and Mini,j are used to determine the status of pixel pi,j .Inorder to make the decision more precisely, we perform some modifications as
Maxi,j, if(Maxi,j<=MedianInWi;j +Th_SMb) Nmax= MedianInWi,j , otherwise (14) +Th_SMb
Mini,j, if(Mini,j<=MedianInWi,j -Th_SMb) Nmin= MedianInWi,j , otherwise (15) -Th_SMb So we can say that if fi,j is not between Nmax and Nmin , then pi,j is a noise pixel. Then a conditional median filter will be used to obtain the reconstructed value. Otherwise the original value fi,j will be the output. The equation is as
true, 1if (fi,j>=Nmax)or (fi,j<=Nmin) Decision IV = false, otherwise. (16)
The fixed values of threshold make our algorithm simple and suitable for hardware implementation. According to our experimental results, the thresholds Th IMa, TH IMb, Th FMa, Th FMb, Th SMa, and Th SMb are all predefined values and set as 20, 25, 40, 80, 15, 60, respectively.
2.2 Modified Conditional Median Filter
At the end of three decision modules, the decision tree based noise detector detects the noisy pixel values within the image and then reconstructs these noisy pixel values with an efficient conditional median filter. The conditional median filter sorts every 9 pixel values in each 3×3 windows which contain the noisy pixel values. Then it verifies whether the median satisfies the desired condition (should between Nmax and Nmin). If the median satisfies the desired condition then the noisy pixel will be replaced by the median value. Otherwise it verifies the same condition for the next neighborhood of the median. If the condition is satisfied then noisy pixel value will be replaced by the neighborhood pixel value. Otherwise go for next neighborhood and the process is repeated until all the noisy pixel values are reconstructed.
Algorithm 1 . Reconstruction of noisy pixel value by conditional median filter
1. If dec1 = dec2 =dec3 = dec4 = true then
2. If sort(4) <= Nmax and sort(4) >= Nmin then
3. mat2(i,j) := Sort(4)
4. Elseif sort(5) <= Nmax and sort(5) >= Nmin then
5. mat2(i,j) := Sort(5)
6. Elseif sort(3) <= Nmax and sort(3) >= Nmin then
7. mat2(i,j) := Sort(3)
8. Elseif sort(2) <= Nmax and sort(2) >= Nmin then
9. mat2(i,j) := Sort(2)
10. Elseif sort(1) <= Nmax and sort(1) >= Nmin then
11. mat2(i,j) := Sort(1)
12. Elseif sort(0) <= Nmax and sort(0) >= Nmin then
13. mat2(i,j) := Sort(0)
14. Else
15. mat2(i,j) := Sort(6)
16. end if
17. end if
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Fig 3.1 VLSI Architecture of DTCMBDM
III. PROPOSED VLSI ARCHITECTURE OF DTCMBDM
A pipelined architecture is used to obtain a better timing performance. Also the proposed architecture has low implementation cost since it uses only two line. The pixel values of the image are stored using SRAM. Fig.5 shows block diagram for DTCMBDM. The architecture adopts an adaptive technology and consists of five main blocks: line buffer, register bank (RB), decision-tree-based impulse detector, Conditional filter, and controller. Each of them is described briefly in the following sections. 3.1 Line buffers In the DTCMBDM three scanning lines are required since it uses a 3×3 mask. Four crossover multipliers are used to realize three scanning lines with two line buffers. Odd-Line Buffer Even-line Buffer are used to store the pixels at odd and even rows, respectively. The line buffer is implemented with a dual-port SRAM (one port for reading out data and other for writing back data concurrently) instead of a series of shift registers to reduce cost and power consumption. If the size of an image is Iw × Ih, the size required for one line buffer is Iw -3 bytes in which 3 represents the number of pixels stored in the register bank. 3.2 Register bank The register bank consists of nine registers. It is used to store the 3 ×3 pixel values of the current mask W. The nine values stored in RB are then used simultaneously by data detector and noise filter for denoising. Once the denoising process for pi,j is completed, the reconstructed pixel value generated by the conditio1nal median filter is outputted and written into the line buffer. The selection signals of the four multiplexers are all set to 1 or 0 for denoising the odd or the even rows, respectively.
3.3 Decision tree based impulse detector The decision tree based impulse detector is used to detect the noisy pixels in an image. The impulse detector checks each pixel in rows and columns of the image and their relation with the neighboring pixels. It is a complex decision making process. The impulse detector finds solution for the multivariable problem by dividing the complex tasks into simpler problems and finds a unique solution for the problem. For that purpose impulse detector having three modules, Isolation Module, Module, Similarity Module. 3.4 Conditional median filter Median filter is one of the most suitable filter for the removal of impulse noise in images. It is possible to improve the efficiency of this filter by adding certain conditions. Such a type of filter is called conditional median filter. This can not only reduce the computational complexity but also improve image quality. 3.5 Controller Controller sends signals to control pipelining and timing statuses of the proposed circuits. It also sends control signals to schedule reading and writing statuses of the data that are stored in register bank or in line buffers. The realization of the controller is based on the concept of finite state machine (FSM). By the controller design, the proposed circuit can automatically receive stream-in data of original images and produce stream-out results of reconstructed images.
IV. SIMULATION RESULTS
The characteristics and performance of the denoising Algorithms can be test verified by taking Coin as the test the image. Consider the test image coin and by applying impulse noises of varying intensities in MATLAB Environment. The digital grey scale image taken here cannot process in VLSI
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directly. The image is converted to its corresponding pixel values and is fed to the denoising process. The proposed decision tree and conditional median filter based de-noising Method in VLSI is designed using VHDL. MODELSIM is used for the simulation. The simulation results are as follows Fig 4.1 Original image Fig 4.2 Noisy image Fig 4.3 Reconstructed image
V. CONCLUSION
An efficient non-linear algorithm to remove high density salt and pepper noise using VLSI is proposed.. The conditional median filter not only reduce computation time but also improve the signal to noise ratio. The algorithm removes noise even at higher noise densities and preserves the edge and fine details. The performance of the algorithm is better when compared to the architecture of this type. So this technique can be used directly for medical imaging, scanning techniques, face recognition, license plate recognition etc.
VI. ACKNOWLEDGEMENT
The author would like to thank Younus college of Engineering, the faculty members of the Department of Electronics and Communication Engineering for the valuable suggestions and facilities provided for completing the task successfully
REFERENCES
[1] P.-Y. Chen, C.-Y. Lien, and H.-M. Chuang, “A Low-Cost VLSI Implementation for Efficient Removal of Impulse Noise,” IEEE Trans. Very Large Scale Integration Systems, vol. 18, no. 3, 2010 [2] R.C. Gonzalez and R.E. Woods, Digital Image Processing. Pearson Education, 2007. [3] W.K. Pratt, Digital Image Processing. Wiley-Inter science, 1991. [4] I. Aizenberg and C. Butakoff, “Effective Impulse Detector Based on Rank-Order Criteria,” IEEE Signal Processing Letters, vol. 11,no. 3, pp. 363-366, Mar. 2004. [5] S.-J. Ko and Y.-H. Lee, “Center Weighted Median Filters and Their Applications to Image Enhancement,” IEEE Trans. Circuits Systems, vol. 38, no. 9, pp. 984-993, Sept. 1991. [6] Y. Dong and S. Xu, “A New Directional Weighted Median Filter for Removal of Random-Valued Impulse Noise,” IEEE Signal Processing Letters, vol. 14, no. 3, pp. 193-196, Mar. 2007. [7] H. Hwang and R.A. Haddad, “Adaptive Median Filters: New Algorithms and Results,” IEEE Trans. Image Processing, vol. 4, no. 4, pp. 499-502,Apr. 1995. [8] P.E. Ng and K.K. Ma, “A Switching Median Filter with Boundary Discriminative Noise Detection for Extremely Corrupted Images,”IEEE Trans. Image Processing, vol. 15, no. 6, pp. 1506-1516, June 2006. [9] P.-Y. Chen and C.-Y. Lien, “An Efficient Edge-Preserving Algorithm for Removal of Salt-and- Pepper Noise,” IEEE Signal Processing Letters, vol. 15, pp. 833-836, Dec. 2008. [10] T. Nodes and N. Gallagher, “Median Filters: Some Modifications and Their Properties,” IEEE Trans. Acoustics, Speech, Signal Processing, vol. ASSP-30, no. 5, pp. 739-746, Oct. 1982. [11] W. Luo, “An Efficient Detail-Preserving Approach for Removing Impulse Noise in Images,” IEEE Signal Processing Letters, vol. 13, no. 7, pp. 413-416, July 2006.