Noise Detection Fuzzy (NDF) Filter for Removing Salt and Pepper Noise

Kam, H.S.; Tan, Wooi Haw

doi:10.1007/978-3-642-05036-7_45

Cited by 5 publications

(7 citation statements)

References 8 publications

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“…In [8], an algorithm for the detection of impulse noise based on the analysis of β increment intensity in each (i, j) pixel relative to immediate N-1 neighbors was proposed…”

Section: Related Workmentioning

confidence: 99%

“…If the value of parameter β ij is small, then the pixel at location (i, j) is considered as uncorrupted one. According to the results of experiments, for 8-bit monochrome images with intensity range for shades of gray from 0 to 255, threshold value of parameter β being determined in [8] is 90 at N = 9. A powerful impulse noise detection method called boundary discriminative noise detection (BDND) is proposed in [9].…”

Section: Related Workmentioning

confidence: 99%

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Switching median filter for suppressing multi-pixel impulse noise

Trubitsyn

Grachev

2021

Computer Optics

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This paper proposes a new switching median filter for suppressing multi-pixel impulse noise in X-ray images. A multi-pixel impulse is understood as a set of several neighboring pixels, the intensity of each significantly exceeds background intensity. Multi-pixel noise can occur, for example, due to the blooming effect, the reason being the limited value of pixel saturation capacity. This article defines the thresholds for the intensity increment relative to the eight immediate neighbors, above which the current pixel is processed by the median filter. The dependence of these thresholds on the number of pixels in an impulse is presented. The proposed algorithm is based on the median filtering process, which consists of several iterations. In this case, the filter has the smallest possible size, which minimizes image distortion during processing. In particular, to exclude a single-pixel impulse, pixel processing is turned on when intensity surge exceeds 3.5 with the grayscale value ranging from 0 to 1. At the same time, to exclude nine-pixel impulses, three iterations are required with the following thresholds: the first iteration with a threshold 2.0; the second iteration also with a threshold 2.0 and the third iteration with a threshold 3.5. The algorithm proposed was tested on real X-ray images corrupted by multi-pixel impulse noise. The algorithm is not only simple, but also reliable and suitable for real-time implementation and application. The efficiency of the technique is shown in comparison with other known filtering methods with respect to the degree of noise suppression. The main result of the testing is that only the proposed method allows excluding multi-pixel noise. Other advantage of the algorithm is its weak effect on the level of Gaussian noise leading to the absence of image blurring (or preserving image details) during processing.

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“…In [8], an algorithm for the detection of impulse noise based on the analysis of β increment intensity in each (i, j) pixel relative to immediate N-1 neighbors was proposed…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Switching median filter for suppressing multi-pixel impulse noise

Trubitsyn

Grachev

2021

Computer Optics

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“…Assumed that screed is a 3. If delta of modularity function is negative (∆Q i < 0, i = 1, ..., 8), assumed that corresponding pixel is damaged.…”

Section: Introductionmentioning

confidence: 99%

The algorithm of the impulse noise filtration in images based on an algorithm of community detection in graphs

Белим

Larionov

2017

2017 Dynamics of Systems, Mechanisms and Machines (Dynamics)

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This article suggests an algorithm of impulse noise filtration, based on the community detection in graphs. The image is representing as non-oriented weighted graph. Each pixel of an image is corresponding to a vertex of the graph. Community detection algorithm is running on the given graph. Assumed that communities that contain only one pixel are corresponding to noised pixels of an image. Suggested method was tested with help of computer experiment.This experiment was conducted on grayscale, and on colored images, on artificial images and on photos. It is shown that the suggested method is better than median filter by 20% regardless of noise percent. Higher efficiency is justified by the fact that most of filters are changing all of image pixels, but suggested method is finding and restoring only noised pixels. The dependence of the effectiveness of the proposed method on the percentage of noise in the image is shown.

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“…Вопросы восстановления поврежденных пикселей на изображениях широко представлены в литературе [1][2][3][4][5][6] и, как правило, решаются применением различных алгоритмов фильтрации для устранения шумов на изображениях. При этом используется два подхода: 1) расчет и применение фильтров подавления шумов; 2) локализация поврежденных пикселей и их восстановление.…”

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“…Существует достаточно много алгоритмов восстановления повреждённых пикселей с известным расположением, рассмотренных в литературе, в частности восстановление по ближайшим соседям (тот же принцип используется при интерполяции изображений), медианная фильтрация, усредняющие фильтры, нейросетевые алгоритмы [1][2][3][4][5][6]. В качестве недостатков использования фильтров дополнительно отмечают неравномерность заполнения областей, нарушение структуры границ, сглаживание границ.…”

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An Algorithm for the Restoration of Pixels Image Based on the Neural Network

Panishchev

2019

Proceedings of the SWSU

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In work the algorithm of restoration of the images damaged as a result of influence of noise of various nature is considered. The advantages and disadvantages of the existing approaches, as well as the prospects of using artificial neural networks, are noted. A double-layer neural network is used as an image restoration tool, and it is assumed that the location of the damaged pixels is known. A neuron is represented as a 3x3 array, where each element of the array has a pixel color value that corresponds to the value of that color in the palette. The neural network is trained on intact images, while the color difference of pixels acts as a learning criterion. For a more accurate restoration, it is ecommended at the training stage to select images similar in color to damaged ones. At the recovery stage, neurons (3x3) are formed around the damaged pixels, so that the damaged pixel is located in the middle of the neuron data array. The damaged pixel is assigned a neuron value depending on the average value of the weights matrix. An algorithm for the restoration of pixels, as well as its software implementation. The simulation was carried out in the RGB palette separately for each channel. To assess the quality of the recovery were selected groups of images with varying degrees of damage. Unlike existing solutions, the algorithm has the simplicity of implementation. The research results show that regardless of the degree of damage (within 50%), about 70% of damaged pixels are restored. Further studies suggest a modification of the algorithm to restore images with enlarged areas of damage, as well as adapting it to restore three-dimensional images.

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Noise Detection Fuzzy (NDF) Filter for Removing Salt and Pepper Noise

Cited by 5 publications

References 8 publications

Switching median filter for suppressing multi-pixel impulse noise

Switching median filter for suppressing multi-pixel impulse noise

The algorithm of the impulse noise filtration in images based on an algorithm of community detection in graphs

An Algorithm for the Restoration of Pixels Image Based on the Neural Network

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