Adaptive Right Median Filter for Salt-and-Pepper Noise Removal

Erkan, Uğur; Gökrem, Levent; Enginoğlu, Serdar

doi:10.29137/umagd.495904

Cited by 15 publications

(4 citation statements)

References 29 publications

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“…The first four of these images are among the most popular images. The second two of them are from TEST IMAGES [5,28], as Figure 1 and Figure 2 shown, respectively. Figure 3 and Figure 4 give the results of AMF, MDBUTMF, NAFSMF, and INRHGA for Girl face and Chair image with 80% and 90 % densities by model 1 and model 2 of impulse noise, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…Impulse noise removal is one of the essential issues in image processing, many approaches have been proposed to suppression of noise in digital images from the literature, and however, eliminate noise from digital images is still a difficult problem [1][2][3][4][5][6][7]. Different sensors, for example, laser scanners, medical scanners, cameras, and weather satellites, can obtain digital images, but these images might inherently be polluted by noise during acquisition compression processes, transmission [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

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Impulse Noise Removal Based on Hybrid Genetic Algorithm

Alaoui¹,

Mashat²,

Adamou‐Mitiche³

et al. 2021

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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.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Impulse Noise Removal Based on Hybrid Genetic Algorithm

Alaoui¹,

Mashat²,

Adamou‐Mitiche³

et al. 2021

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show abstract

“…On the other hand, median filters are nonlinear filters and do not cause blurring of edges. They are also widely used to remove the SPN ( Erkan et al, 2020a ; Duan & Zhang, 2010 ; Erkan, Gökrem & Enginoğlu, 2018 ; Erkan & Gökrem, 2017 ; Erkan, Gökrem & Enginoğlu, 2019 ; Sharadqh et al, 2019 ; Yildirim, 2021 ). However, even if the median filters take the median of only the noiseless pixels, they do not provide good results because they cannot express the neighbourhood relationship at a high-level noise ( Loya & Keskar, 2015 ).…”

Section: Introductionmentioning

confidence: 99%

A new approach for SPN removal: nearest value based mean filter

Turan¹

2022

PeerJ Computer Science

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In this study, a new adaptive filter is proposed to eliminate salt and pepper noise (SPN). The basis of the proposed method consists of two-stages. (1) Changing the noisy pixel value with the closest pixel value or assigning their average to the noisy pixel in case there is more than one pixel with the same distance; (2) the updating of the calculated noisy pixel values with the average filter by correlating them with the noise ratio. The method developed was named as Nearest Value Based Mean Filter (NVBMF), because of using the pixel value which the closest distance in the first stage. Results obtained with the proposed method: it has been compared with the results obtained with the Adaptive Frequency Median Filter, Adaptive Riesz Mean Filter, Improved Adaptive Weighted Mean Filter, Adaptive Switching Weight Mean Filter, Adaptive Weighted Mean Filter, Different Applied Median Filter, Iterative Mean Filter, Two-Stage Filter, Multistage Selective Convolution Filter, Different Adaptive Modified Riesz Mean Filter, Stationary Framelet Transform Based Filter and A New Type Adaptive Median Filter methods. In the comparison phase, nine different noise levels were applied to the original images. Denoised images were compared using Peak Signal-to-Noise Ratio, Image Enhancement Factor, and Structural Similarity Index Map image quality metrics. Comparisons were made using three separate image datasets and Cameraman, Airplane images. NVBMF achieved the best result in 52 out of 84 comparisons for PSNR, best in 47 out of 84 comparisons for SSIM, and best in 36 out of 84 comparisons for IEF. In addition, values nearly to the best result were obtained in comparisons where the best result could not be reached. The results obtained show that the NVBMF can be used as an effective method in denoising SPN.

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“…Noise removal is a pre-processing step in image processing. Therefore, it affects the success of other operations in image processing positively [1][2][3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%