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.

scholarly journals On the properties of some adaptive morphological filters for salt and pepper noise removal

2021 ◽  
Author(s):  
Marisol Mares-Javier ◽  
Carlos Guillén-Galván ◽  
Rafael Lemuz-López ◽  
Johan Debayle
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Noise Level ◽  
State Of The Art ◽  
Impulsive Noise ◽  
Noise Removal ◽  
Salt And Pepper Noise ◽  
Morphological Filters ◽  
Salt And Pepper

Mathematical Morphology (MM) is a tool that can be applied to many digital image processing tasks that include the reduction of impulsive or salt and pepper noise in grayscale images. The morphological filters used for this task are filters resulting from two basic operators: erosion and dilation. However, when the level of contamination of the image is higher, these filters tend to distort the image. In this work we propose a pair of operators with properties, that better adapt to impulsive noise than other classical morphological filters, it is demonstrated to be increasing idempotent morphological filters. Furthermore, the proposed pair turns out to be a Ʌ-filter and a V-filter which allow to build morphological openings and closings. Finally, they are compared with other filters of the state-of-the-art such as: SMF, PMSF, DBAIN, AMF and NAFSM, and have shown a better performance when the noise level is above 50%.

Decision-Based Marginal Diffusion for Salt-and-Pepper Noise Removal in Color Images

2020 ◽  
pp. 2150153
Author(s):  
Hongyao Deng ◽  
Xiuli Song ◽  
Huilian Fan
Keyword(s):  
Anisotropic Diffusion ◽  
Noise Suppression ◽  
Impulsive Noise ◽  
Geometric Analysis ◽  
Noise Removal ◽  
Diffusion Method ◽  
Salt And Pepper Noise ◽  
The Arts ◽  
Independent Distribution ◽  
Salt And Pepper

Salt-and-pepper noise suppression for vector-valued images usually employs vector median filtering, total variation L1 model, diffusion methods and variants. These approaches, however, often introduce excessive smoothing and can result in extensive visual feature blurring and are suitable only for images with low intensity noise. In this paper, a new method, as an important preprocessing step in cyber-physical systems, is presented to suppress salt-and-pepper noise that can overcomes this limitation. This method first detects the corrupted pixels and then restores them using channel-wise anisotropic diffusion. The means is twofold. On the one hand, the marginal approach is used to perform noise suppression separately in each channel because the contaminative pixel components are of independent distribution. On the other hand, a decision-based anisotropic diffusion method is applied to each channel to restores them. The anisotropic diffusion is an energy-dissipating process with time, and dependent on geometric analysis of shape of the energy surface. Simulation results indicate that the proposed method for impulsive noise removal achieves the state-of-the-arts results.

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 A “Salt and Pepper” Noise Reduction Scheme for Digital Images Based on Support Vector Machines Classification and Regression

2014 ◽  
Vol 2014 ◽  
pp. 1-15
Author(s):  
Hilario Gómez-Moreno ◽  
Pedro Gil-Jiménez ◽  
Sergio Lafuente-Arroyo ◽  
Roberto López-Sastre ◽  
Saturnino Maldonado-Bascón
Keyword(s):  
Support Vector Machines ◽  
Noise Reduction ◽  
Digital Images ◽  
State Of The Art ◽  
Noise Removal ◽  
Support Vector ◽  
Salt And Pepper Noise ◽  
Vector Machines ◽  
Classification And Regression ◽  
Salt And Pepper

We present a new impulse noise removal technique based on Support Vector Machines (SVM). Both classification and regression were used to reduce the “salt and pepper” noise found in digital images. Classification enables identification of noisy pixels, while regression provides a means to determine reconstruction values. The training vectors necessary for the SVM were generated synthetically in order to maintain control over quality and complexity. A modified median filter based on a previous noise detection stage and a regression-based filter are presented and compared to other well-known state-of-the-art noise reduction algorithms. The results show that the filters proposed achieved good results, outperforming other state-of-the-art algorithms for low and medium noise ratios, and were comparable for very highly corrupted images.

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.

Salt and Pepper Noise Removal Based on GA-BP Algorithm

2011 ◽  
Vol 301-303 ◽  
pp. 1243-1248
Author(s):  
Yin Mao Song ◽  
Xiao Juan Li
Keyword(s):  
Noise Removal ◽  
Bp Algorithm ◽  
Salt And Pepper Noise ◽  
Noise Detection ◽  
Edge Preserving ◽  
Bp Network ◽  
Median Filters ◽  
Noise Filter ◽  
Noise Density ◽  
Salt And Pepper

Noise detection-based median filters have been widely adopted to reduce salt and pepper noise in images. However, since noise pixel is not detected accurately, it is likely to blur the fringe of image under the high noise density. In this paper, we propose an algorithm of salt and pepper noise filter which is based on GA-BP algorithm noise detector to remove the salt and pepper noise in images. The algorithm firstly detect the location of noise pixels by using optimized GA-BP network,then,it introduce edge-preserving function and PRP algorithm to solve the objective function of extreme value further to realize the image denoising. Compared with the traditional algorithms, experimental results show that the proposed algorithm has an evident improvement, and have good characters of generalization, robust and self-adaptive.

scholarly journals An Iterative Weighted-Mean Filter for Removal of High-Density Salt-and-Pepper Noise

Symmetry ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 1990
Author(s):  
Fengyu Chen ◽  
Minghui Huang ◽  
Zhuxi Ma ◽  
Yibo Li ◽  
Qianbin Huang
Keyword(s):  
Noise Removal ◽  
High Density ◽  
Salt And Pepper Noise ◽  
Weighted Mean ◽  
Real Time Processing ◽  
Noise Density ◽  
Mean Filter ◽  
Execution Speed ◽  
Salt And Pepper

Salt-and-pepper noise, which is often introduced by sharp and sudden disturbances in the image signal, greatly reduces the quality of images. Great progress has been made for the salt-and-pepper noise removal; however, the problem of image blur and distortion still exists, and the efficiency of denoising requires improvement. This paper proposes an iterative weighted-mean filter (IWMF) algorithm in detecting and removing high-density salt-and-pepper noise. Three steps are required to implement this algorithm: First, the noise value and distribution characteristics were used to identify the noise pixels, effectively improving the accuracy of noise detection. Second, a weighted-mean filter was applied to the noisy pixels. We adopted an un-fixed shape symmetrical window with better detail preservation ability. Third, this method was performed iteratively, avoiding the streak effect and artifacts in high noise density. The experimental results showed that IWMF outperformed other state-of-the-art filters at various noise densities, both in subjective visualization and objective digital measures. The extremely fast execution speed of this method is quite suitable for real-time processing.

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