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.

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.

Adaptive Four-dot Median Filter for Removing 1-99% Densities of Salt-and-Pepper Noise in Images

2018 ◽  
Vol 11 (3) ◽  
pp. 47-61 ◽  
Author(s):  
Xin-Ming Zhang ◽  
Qiang Kang ◽  
Jin-Feng Cheng ◽  
Xia Wang
Keyword(s):  
Image Restoration ◽  
Performance Improvement ◽  
Adaptive Filtering ◽  
Median Filter ◽  
Window Size ◽  
Salt And Pepper Noise ◽  
Noise Detection ◽  
Median Filters ◽  
Simple Logic ◽  
Salt And Pepper

In order to accelerate denoising and improve the denoising performance of the current median filters, an Adaptive Four-dot Median Filter (AFMF) for image restoration is proposed in this article. AFMF is not only very efficient and fast in logic execution, but also it can restore the corrupted images with 1–99% densities of salt-and-pepper noise to the satisfactory ones. Without any complicated operation for noise detection, it intuitively and simply distinguishes impulse noises, while keeping the noise-free pixels intact. Only the uncorrupted pixels of the four-dot mask in adaptive filtering windows are used for the adoption of candidates for median finding, whatever filtering window size is. Furthermore, the adoption of recursive median filters leads to denoising performance improvement and faster filtering. The simple logic of the proposed algorithm obtains significant milestones on the fidelity of a restored image. Relevant experimental results on subjective visualization and objective digital measure validate the robustness of the proposed filter.

An Algorithm for Salt and Pepper Noise Removal Based on Information Entropy

2012 ◽  
Vol 220-223 ◽  
pp. 2273-2279
Author(s):  
Feng Zhao ◽  
Rui Chuan Ma ◽  
Jia Qing Ma
Keyword(s):  
Information Entropy ◽  
Detection Method ◽  
Noise Removal ◽  
Salt And Pepper Noise ◽  
Noise Detection ◽  
Statistical Property ◽  
Weighted Means ◽  
Mean Filter ◽  
Distribution Uniformity ◽  
Salt And Pepper

By using information entropy to estimate the distribution uniformity of the pixels with a same gray level, an accurate salt and pepper noise detection method is presented based on the statistical property of salt and pepper noise. And then, a new modified mean filter is designed, which sets up noise-centre filtering windows, Moreover, the weighted means are calculated by merely using the non-noise points in each filtering window. The presented filter can efficiently preserve the details of images, avoid the affection of noise points on the restore points, and reduce the dimness of the noise points. Experimental results show that this algorithm has the better performance on noise detection, noise filtering, and the protection of detail.

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 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 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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