Image Denoising Using New Adaptive Based Median Filter

Shrestha, Suman

doi:10.5121/sipij.2014.5401

Cited by 67 publications

(29 citation statements)

References 5 publications

(8 reference statements)

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“…The image is made locally smooth and the edges are removed. The location of the noise is determined and then SMF is applied locally to the image [28]. PSMF produces better results than SMF.…”

Section: Introductionmentioning

confidence: 99%

A new method based on pixel density in salt and pepper noise removal

Erkan¹,

Gökrem²

2018

Turk J Elec Eng & Comp Sci

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Abstract:In this paper, we deliver a new method to remove salt and pepper noise, which we refer to as based on pixel density filter (BPDF). The first step of the method is to determine whether or not a pixel is noisy, and then we decide on an adaptive window size that accepts the noisy pixel as the center. The most repetitive noiseless pixel value within the window is set as the new pixel value. By using 18 test images, we give the results of peak signal-to-noise ratio (PSNR), structural similarity (SSIM), image enhancement factor (IEF), standard median filter (SMF), adaptive median filter (AMF), adaptive fuzzy filter (AFM), progressive switching median filer (PSMF), decision-based algorithm (DBA), modified decision-based unsymmetrical trimmed median filter (MDBUTMF), noise adaptive fuzzy switching median filter (NAFSM), and BPDF. The results show that BPDF produces better results than the above-mentioned methods at low and medium noise density.

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Section: Introductionmentioning

confidence: 99%

A new method based on pixel density in salt and pepper noise removal

Erkan¹,

Gökrem²

2018

Turk J Elec Eng & Comp Sci

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“…GHFCM is employed for performing coarse level segmentation, as it is efficient and robust. In addition to this, the capability to withstand the noise makes it more suitable for this work [22].…”

Section: ) Coarse Segmentation By Ghfcmmentioning

confidence: 99%

“…As on 2012, World Health Organization (WHO) has assessed that nearly 5,30,000 new cases have been affected by cervical cancer globally. The report added that about 1, 22,844 women are diagnosed with cervical cancer in a year. However, the public shows least awareness with respect to this dreadful disease.…”

Section: Introductionmentioning

confidence: 99%

A Double Layered Segmentation Algorithm for Cervical Cell Images based on GHFCM and ABC

Lakshmi¹,

Ravi²

2017

IJIGSP

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Abstract-Cancer is a life threatening disease and it engulfs the lives of many women. Due to the technology advancement, the medical science is drastically improved. A statistical report claims that the diagnostic decisions of radiologists show more false positive rates, which is very dangerous. However, when the radiologists are supported by computer aided applications, the false positive results are considerably reduced. Understanding the potentiality of computer aided applications, this paper presents a double layered segmentation algorithm for cervical cell images. The entire work is subdivided into three important phases, which are cervical image preprocessing, coarse and fine level segmentation. The preprocessing phase attempts to remove the noise and enhance the image quality by means of adaptive mean filter and Contrast Limited Adaptive Histogram Equalization (CLAHE) technique respectively. The coarse level segmentation process is achieved by Generalized Hierarchical Fuzzy C Means (GHFCM) and the fine level segmentation process is carried out by Artificial Bee Colony (ABC) algorithm. The performance of the proposed segmentation algorithm is analysed in terms of accuracy, sensitivity and specificity. The experimental results show the efficacy of the proposed segmentation algorithm.

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“…In Decision Based Median Filtering [7] method, the first step is to detect the status of pixel whether it is a corrupted one or not. If pixel value is between minimum and maximum value of window to be processed, it is left unchanged, else median value of window is calculated and replaced for the corrupted pixel.…”

Section: Related Workmentioning

confidence: 99%

An Enhanced Approach for Preprocessing of Mammogram Images using Inverse Daubechies Wavelet Transform and Non-Linear Diffusion

Kowsalya¹,

Priyaa²

2015

IJCA

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Breast cancer is the most leading cause of death in women nowadays. Screening mammography is currently the best available radiological technique for early detection of breast cancer. The detection of breast cancer is disturbed due to the existence of artifacts which reduce the rate of accuracy. For this reason, the pre-processing of mammogram images is very important in the process of breast cancer analysis because it reduces the number of false positives. This paper discusses about two existing filtering techniques and compares it with the results of a proposed filtering method. It is used to solve the noise removal problems and separate the background region from the breast profile region using an automatic thresholding technique. The results are evaluated on the pre-processing method on a set of images obtained from MIAS database. Thus this preparation phase improves the image quality and accentuates the CAD results more accurately.

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Image Denoising Using New Adaptive Based Median Filter

Abstract: Noise is a major issue while transferring images through all kinds of electronic communication. One of the most common noise in electronic communication

Cited by 67 publications

References 5 publications

A new method based on pixel density in salt and pepper noise removal

A new method based on pixel density in salt and pepper noise removal

A Double Layered Segmentation Algorithm for Cervical Cell Images based on GHFCM and ABC

An Enhanced Approach for Preprocessing of Mammogram Images using Inverse Daubechies Wavelet Transform and Non-Linear Diffusion

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