scholarly journals Probabilistic Decision Based Average Trimmed Filter for the Removal of High-Density Salt and Pepper Noise

2020 ◽  
Vol 8 (5) ◽  
pp. 4350-4357
Keyword(s):  
Signal To Noise Ratio ◽  
Median Filter ◽  
Absolute Error ◽  
Noise Removal ◽  
Low Noise ◽  
Salt And Pepper Noise ◽  
Ratio Image ◽  
Noise Density ◽  
Chest X Ray ◽  
Salt And Pepper

The paper focuses on the evacuation of salt and pepper noise from a contaminated image. A probabilistic decision based average trimmed filter (PDBATF) is proposed for both high and low noise density. The proposed algorithm addresses the issue related to even number of noise-free pixel in trimmed median filter for the calculation of processing pixel. The proposed average trimmed filter is incorporated for low noise density while the proposed patch else average trimmed filter is applied for high noise density. Finally, they are combined together to develop the proposed PDBATF. The proposed algorithm show an excellent noise removal capability compared to the recently developed algorithms in terms of peak signal to noise ratio, image enhancement factor, mean absolute error and execution time. It works very efficiently in de-noising contaminated medical images such as chest-x-ray and malaria-blood-smear.

scholarly journals AN FPGA IMPLEMENTATION OF MODIFIED DECISION BASED UNSYMMETRICAL TRIMMED MEDIAN FILTER FOR THE REMOVAL OF SALT AND PEPPER NOISE IN DIGITAL IMAGES

2014 ◽  
pp. 159-164
Author(s):  
CHAITANYA BETHINA ◽  
M. PREMKUMAR
Keyword(s):  
Noise Suppression ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Noise Removal ◽  
Mean Value ◽  
Low Noise ◽  
Color Images ◽  
Salt And Pepper Noise ◽  
Noisy Pixel ◽  
Salt And Pepper

A modified decision based unsymmetrical trimmed median filter algorithm for the restoration of gray scale, and color images that are highly corrupted by salt and pepper noise is proposed in this paper. Images are often corrupted by impulse noise during acquisition and transmission; thus, an efficient noise suppression technique is required before subsequent image processing operations. Median filter (MF) is widely used in noise removal methods due to its denoising capability and computational efficiency. However, it is effective only for low noise densities. Extensive experimental results demonstrate that our method can obtain better performances in terms of both subjective and objective evaluations than denoising techniques. Especially, the proposed method can preserve edges very well while removing salt and pepper noise. Modified Decision Based Algorithm (MDBA), and Progressive Switched Median Filter (PSMF) shows better results at low and medium noise densities. At high noise densities, their performance is poor. A new algorithm to remove high-density salt and pepper noise using modified Decision Based Unsymmetric Trimmed Median Filter (DBUTMF) is proposed. The proposed algorithm replaces the noisy pixel by trimmed median. Since our algorithm is algorithmically simple, it is very suitable to be applied to many real-time applications and higher noise densities. When all the pixel values are 0’s and 255’s then the noise pixel is replaced by mean value of all the elements present in the selected window. The proposed algorithm is tested against different grayscale and color images and it gives better Peak Signal-to-Noise Ratio (PSNR) and Image Enhancement Factor (IEF).

scholarly journals An Improved Method to Remove Salt and Pepper Noise in Noisy Images

2021 ◽  
Author(s):  
Jinder Kaur ◽  
Gurwinder Kaur ◽  
Ashwani Kumar
Keyword(s):  
Signal To Noise Ratio ◽  
Median Filter ◽  
Similarity Index ◽  
Noise Removal ◽  
Mean Value ◽  
Salt And Pepper Noise ◽  
Gray Scale ◽  
Noisy Pixel ◽  
Rgb Images ◽  
Salt And Pepper

In the field of image processing, removal of noise from Gray scale as well as RGB images is an ambitious task. The important function of noise removal algorithm is to eliminate noise from a noisy image. The salt and pepper noise (SPN) is frequently arising into Gray scale and RGB images while capturing, acquiring and transmitting over the insecure several communication mechanisms. In past, the numerous noise removal methods have been introduced to extract the noise from images adulterated with SPN. The proposed work introduces the SPN removal algorithm for Gray scale at low along with high density noise (10\% to 90\%). According to the different conditions of proposed algorithm, the noisy pixel is reconstructed by Winsorized mean or mean value of all pixels except the centre pixel which are present in the processing window. The noise from an image can be removed by using the proposed algorithm without degrading the quality of image. The performance evaluation of proposed and modified decision based unsymmetric median filter (MDBUTMF) is done on the basis of different performance parameters such as Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Image Enhancement Factor (IEF) and Structure Similarity Index Measurement (SSIM).

scholarly journals High Density Impulse Noise Removal using Advanced Median Filter for Preserving Image Quality Metrics

2019 ◽  
Vol 8 (4) ◽  
pp. 11909-11914
Keyword(s):  
Image Quality ◽  
Impulse Noise ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Noise Removal ◽  
Quality Metrics ◽  
High Density ◽  
Image Quality Metrics ◽  
Neighboring Pixel ◽  
Salt And Pepper

In this work, a procedure to remove the high density salt and pepper noise from a corrupted image is developed and to compare the output image with the original image through the image quality metrics. As a common practice the corrupted pixels are replaced by the median of neighboring pixel values by considering a constant number of neighboring pixels. But in this proposed method the corrupted pixels are identified and are replaced by the median of the neighboring pixel values which are adjustable, to preserve and improve the image quality metrics. This method makes a comparison between the corrupted and uncorrupted pixels and performs the median filtering process only on the corrupted ones. In this work a 3x3, 5x5 and 7x7 square neighborhood are used. The output images are observed with low neighborhood as well as high neighborhood pixel values. The calculation of PSNR (Peak Signal to Noise Ratio) and MSE (Mean square error) value for each dimension with different percentages are considered for the comparative analysis

scholarly journals A New Spatial Domain Filter for Impulse Noise Removal with Improved Accuracy Based on Multiple Conditions

2020 ◽  
Vol 9 (4) ◽  
pp. 1305-1309
Keyword(s):  
Impulse Noise ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Noise Removal ◽  
Spatial Domain ◽  
Median Filtering ◽  
Impulse Noise Removal ◽  
Noise Density ◽  
Improved Accuracy ◽  
Multiple Conditions

Numerous filtering methods are proposed for Impulse noise removal, it is an important task in the field of image restoration. The familiar spatial domain algorithm to remove impulse noise is Standard Median Filter (SMF). Most of the existing algorithms are based on median filtering and recent algorithms are Modified Hybrid Median Filter (MHMF) and New Modified Hybrid Median Filter (NMHMF). These two are worked up to 20% noise density. In this paper proposed a new` algorithm for impulse noise removal above 20% noise density conditions with different samples of images. The implementation of proposed method compares with known existing methods by comparing Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR).

A Statistical Salt-and-Pepper Noise Removal Algorithm

2019 ◽  
Vol 19 (01) ◽  
pp. 1950006 ◽  
Author(s):  
Amiya Halder ◽  
Sayan Halder ◽  
Samrat Chakraborty ◽  
Apurba Sarkar
Keyword(s):  
Standard Deviation ◽  
Noise Removal ◽  
Noisy Image ◽  
Salt And Pepper Noise ◽  
Novel Approach ◽  
Noise Density ◽  
Salt And Pepper

This paper proposes a novel approach to remove salt-and-pepper noise from a given noisy image. The proposed algorithm is based on statistical quantities such as mean and standard deviation. It determines the intensity to be placed on the impulse point by calculating the eligibility of the nearby points in a very simple way. This method works iteratively and removes all the impulse points restoring the edges and minute details. The proposed algorithm is very efficient and gives better results than various existing algorithms. The performance of the proposed method are compared with other existing methods with images of noise density as high as 99% and is found to perform better.

scholarly journals Impulsive Noise Removal with an Adaptive Weighted Arithmetic Mean Operator for Any Noise Density

2021 ◽  
Vol 11 (2) ◽  
pp. 560
Author(s):  
Manuel González-Hidalgo ◽  
Sebastia Massanet ◽  
Arnau Mir ◽  
Daniel Ruiz-Aguilera
Keyword(s):  
State Of The Art ◽  
Impulsive Noise ◽  
Noise Removal ◽  
Arithmetic Mean ◽  
Salt And Pepper Noise ◽  
Weighted Arithmetic ◽  
Noise Density ◽  
Salt And Pepper ◽  
Robust To Noise ◽  
Restoration Measures

Many computer vision algorithms which are not robust to noise incorporate a noise removal stage in their workflow to avoid distortions in the final result. In the last decade, many filters for salt-and-pepper noise removal have been proposed. In this paper, a novel filter based on the weighted arithmetic mean aggregation function and the fuzzy mathematical morphology is proposed. The performance of the proposed filter is highly competitive when compared with other state-of-the-art filters regardless of the amount of salt-and-pepper noise present in the image, achieving notable results for any noise density from 5% to 98%. A statistical analysis based on some objective restoration measures supports that this filter surpasses several state-of-the-art filters for most of the noise levels considered in the comparison experiments.

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