scholarly journals Impulse Noise Removal Based on Hybrid Genetic Algorithm

2021 ◽  
Vol 38 (4) ◽  
pp. 1245-1251
Author(s):  
Nail Alaoui ◽  
Arwa Mashat ◽  
Amel Baha Houda Adamou-Mitiche ◽  
Lahcène Mitiche ◽  
Aicha Djalab ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Impulse Noise ◽  
Signal To Noise Ratio ◽  
Hybrid Genetic Algorithm ◽  
Structural Similarity ◽  
Noise Removal ◽  
Impulse Noise Removal ◽  
Salt And Pepper ◽  
Crossover And Mutation ◽  
Different Levels

In this paper, we introduce a new method, impulse noise removal based on hybrid genetic algorithm (INRHGA) to remove impulse noise at different noise densities of noise while preserving the main features of the image. The proposed approach merges the genetic algorithm and methods for filtering images that are combined into the population as essential solutions to create a developed and improved population. A set of individuals is developed into a number of iterations using factors of crossover and mutation. Our method develops a group of images instead of a set of parameters from the filters. We then introduced some of the concepts and steps of it. The proposed algorithm is compared with some image denoising algorithm. By using Peak Signal to Noise Ratio (PSNR), structural similarity (SSIM). For example, for Lenna image with 60% salt and pepper noise density, PSNR, SSIM results of AMF, MDBUTMFG and NAFSM methods are 20,39/ 28.74/ 29.85 and 0.5679/ 0.8312/ 0.8818 respectively, while PSNR, SSIM results of the proposed algorithm are 29.92 and 0.8838, respectively. Experimental results indicate that INRHGA is very effective and visually comparable with the above-mentioned methods at different levels of noise.

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).

Adaptive Trimmed Median Filter for Impulse Noise Detection and Removal with an Application to Mammogram Images

2020 ◽  
Vol 20 (04) ◽  
pp. 2050032
Author(s):  
Rubul Kumar Bania ◽  
Anindya Halder
Keyword(s):  
Breast Cancer ◽  
Impulse Noise ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Statistical Significance ◽  
Similarity Index ◽  
Structural Similarity ◽  
Noise Removal ◽  
Quantitative Indices ◽  
Mammogram Images

Mammography imaging is one of the most widely used techniques for breast cancer screening and analysis of abnormalities. However, due to some technical difficulties during the time of acquisition and digital storage of mammogram images, impulse noise may be present. Therefore, detection and removals of impulse noise from the mammogram images are very essential for early detection and further diagnosis of breast cancer. In this paper, a novel adaptive trimmed median filter (ATMF) is proposed for impulse noise (salt & pepper (SNP)) detection and removal with an application to mammogram image denoising. Automatic switching mechanism for updating the Window of Interest (WoI) size from ([Formula: see text]) to ([Formula: see text]) or ([Formula: see text]) is performed. The proposed method is applied on publicly available mammogram images corrupted with varying SNP noise densities in the range 5%–90%. The performance of the proposed method is measured by various quantitative indices like peak signal to noise ratio (PSNR), mean square error (MSE), image enhancement factor (IEF) and structural similarity index measure (SSIM). The comparative analysis of the proposed method is done with respect to other state-of-the-art noise removal methods viz., standard median filter (SMF), decision based median filter (DMF), decision based unsymmetric trimmed median filter (DUTMF), modified decision based unsymmetric trimmed median filter (MDUTMF) and decision based unsymmetric trimmed winsorized mean filter (DUTWMF). The superiority of the proposed method over other compared methods is well evident from the experimental results in terms of the quantitative indices (viz., PSNR, IEF and SSIM) and also from the visual quality of the denoised images. Paired t-test confirms the statistical significance of the higher PSNR values achieved by the proposed method as compared to the other counterpart techniques. The proposed method turned out to be very effective in denoising both high and low density noises present in (mammogram) images.

scholarly journals Salt and Pepper Noise Removal with Noise Detection and a Patch-Based Sparse Representation

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Di Guo ◽  
Xiaobo Qu ◽  
Xiaofeng Du ◽  
Keshou Wu ◽  
Xuhui Chen
Keyword(s):  
Sparse Representation ◽  
Impulse Noise ◽  
Structural Similarity ◽  
Noise Removal ◽  
Noise Detection ◽  
Noise Levels ◽  
Denoising Method ◽  
Transmission Errors ◽  
Alternating Direction ◽  
Salt And Pepper

Images may be corrupted by salt and pepper impulse noise due to noisy sensors or channel transmission errors. A denoising method by detecting noise candidates and enforcing image sparsity with a patch-based sparse representation is proposed. First, noise candidates are detected and an initial guide image is obtained via an adaptive median filtering; second, a patch-based sparse representation is learnt from this guide image; third, a weightedl1-l1regularization method is proposed to penalize the noise candidates heavier than the rest of pixels. An alternating direction minimization algorithm is derived to solve the regularization model. Experiments are conducted for 30%∼90% impulse noise levels, and the simulation results demonstrate that the proposed method outperforms total variation and Wavelet in terms of preserving edges and structural similarity to the noise-free images.

Salt and Pepper Noise Removal Using Resizable Window and Gaussian Estimation Function

2016 ◽  
Vol 6 (5) ◽  
pp. 2219
Author(s):  
Suhad A. Ali ◽  
C. Elaf A. Abbood ◽  
Shaymaa Abdu LKadhm
Keyword(s):  
Signal To Noise Ratio ◽  
Structural Similarity ◽  
Noise Removal ◽  
Salt And Pepper Noise ◽  
Noisy Pixel ◽  
Different Types ◽  
Estimation Function ◽  
Salt And Pepper ◽  
Gaussian Estimation

<p class="Default">Most types of the images are corrupted in many ways that because exposed to different types of noises. The corruptions happen during transmission from space to another, during storing or capturing. Image processing has various techniques to process the image. Before process the image, there is need to remove noise that corrupt the image and enhance it to be as near as to the original image. This paper proposed a new method to process a particular common type of noise. This method removes salt and pepper noise by using many techniques. First, detect the noisy pixel, then increasing the size of the pixel window depending on some criteria to be enough to estimate the results. To estimates the pixels of image, the Gaussian estimation function is used. The resulted image quality is measured by the statistical quantity measures that's Peak Signal-to-Noise Ratio (PSNR) and The Structural Similarity (SSIM) metrics. The results illustrate the quality of the enhanced image compared with the other traditional techniques. The slight gradual of SSIM metric described the performance of the proposed method with high increasing of noise levels.</p>

scholarly journals Salt and Pepper Noise Removal Using Resizable Window and Gaussian Estimation Function

2016 ◽  
Vol 6 (5) ◽  
pp. 2219
Author(s):  
Suhad A. Ali ◽  
C. Elaf A. Abbood ◽  
Shaymaa Abdu LKadhm
Keyword(s):  
Signal To Noise Ratio ◽  
Structural Similarity ◽  
Noise Removal ◽  
Salt And Pepper Noise ◽  
Noisy Pixel ◽  
Different Types ◽  
Estimation Function ◽  
Salt And Pepper ◽  
Gaussian Estimation

<p class="Default">Most types of the images are corrupted in many ways that because exposed to different types of noises. The corruptions happen during transmission from space to another, during storing or capturing. Image processing has various techniques to process the image. Before process the image, there is need to remove noise that corrupt the image and enhance it to be as near as to the original image. This paper proposed a new method to process a particular common type of noise. This method removes salt and pepper noise by using many techniques. First, detect the noisy pixel, then increasing the size of the pixel window depending on some criteria to be enough to estimate the results. To estimates the pixels of image, the Gaussian estimation function is used. The resulted image quality is measured by the statistical quantity measures that's Peak Signal-to-Noise Ratio (PSNR) and The Structural Similarity (SSIM) metrics. The results illustrate the quality of the enhanced image compared with the other traditional techniques. The slight gradual of SSIM metric described the performance of the proposed method with high increasing of noise levels.</p>

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