Comparative Analysis of Median and Average Filters in Impulse Noise Suppression

Shi, Luyao; Chen, Yang; Wen-long, Yuan; Zhang, Libo; Yang, Benqiang; Hu, Shu; Luo, Limin; Coatrieux, Jean-Louis

doi:10.1142/s0219477515500029

Cited by 4 publications

(2 citation statements)

References 14 publications

(5 reference statements)

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“…Image Denoising: Some advanced image denoising methods have been proposed and applied to remove Rician noises and increase signal-to-noise ratio (SNR) in brain MR images [85]. The denoising algorithms should be edge preserving [86] and cannot blur important lesion information.…”

Section: Image Preprocessingmentioning

confidence: 99%

Pathological Brain Detection by Artificial Intelligence in Magnetic Resonance Imaging Scanning (Invited Review)

Wang¹,

Zhang²,

Zhan³

et al. 2016

PIER

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Abstract-(Aim) Pathological brain detection (PBD) systems aim to assist and even replace neuroradiologists to make decisions for patients. This review offers a comprehensive and quantitative comparison for PBD systems by artificial intelligence in magnetic resonance imaging (MRI) scanning. (Method) We first investigated four categories of brain diseases, including neoplastic disease, neurodegenerative disease, cerebrovascular disease, and inflammation. Next, we introduced important MRI techniques, such as the shimming, water and fat suppression, and three advanced imaging modalities (functional MRI, diffusion tensor imaging, and magnetic resonance spectroscopic imaging). Then, we discussed four image preprocessing techniques (image denoising, slice selection, brain extraction, spatial normalization, and intensity normalization), seven feature representation techniques (shape, moment, wavelet, statistics, entropy, gray level co-occurrence matrix, and Fourier transform), and two dimension reduction techniques (feature selection and feature extraction). Afterwards, we studied classification related methods: six learning models (decision tree, extreme learning machine, k-nearest neighbors, naive Bayes classifier, support vector machine, feed-forward neural network), five kernel functions (linear, homogeneous and inhomogeneous polynomial, radial basis function, and sigmoid), and three types of optimization methods (evolutionary algorithm, stochastic optimization, and swarm intelligence). (Results) We introduced three benchmark datasets and used K-fold stratified cross validation to avoid overfitting. We presented a detailed quantitative comparison among 44 state-of-theart PBD algorithms and discussed their advantages and limitations. (Discussions) Artificial intelligence is now making stride in the PBD field and enjoys a fair amount of success. In the future, semi-supervised learning and transfer learning techniques may be potential breakthroughs to develop PBD systems.

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Section: Image Preprocessingmentioning

confidence: 99%

Pathological Brain Detection by Artificial Intelligence in Magnetic Resonance Imaging Scanning (Invited Review)

Wang¹,

Zhang²,

Zhan³

et al. 2016

PIER

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“…Removal of such kind of noise is much necessary to carry on computation and processing on the obtained image. Various algorithms have been published which are dedicated to removing impulse noise from the input image [1][2][3][4][5][6][7][8]. In the paper, a simple and elegant approach is presented to enhance salt and pepper noise corrupted images, which obtains superior results than different algorithms.…”

Section: Introductionmentioning

confidence: 99%

Impulse Noise Removal Algorithm Using Modified Adaptive Distance-Related Weighted Mean Filter

Halder¹,

Choudhuri²

2021

Computational Intelligence in Pattern Recognition

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Reduction of impulse noise is a common preprocessing step for many image processing and computer vision applications. Salt-and-pepper noise is a common type of impulse noise which is obtained during image acquisition, storage, and transmission. In such a scenario, the image histogram has a high peak at both ends, leading to a corrupt image caused by the noisy pixels having maximum or minimum intensity values. In the proposed method, an adaptive distance-related weighted mean filter has been implemented to remove impulse noise from a corrupt image. In this technique, each noisy pixel is restored by the distance-related weighted mean of the first occurring non-noisy pixel in every immediate neighboring direction and thus it improves the quality of the image. The proposed technique is tested on standard sample images and it produces better quantitative results than the other existing state-of-the-art algorithms.

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A 3-D Reconstruction Method of Dense Bubbly Plume Based on Laser Scanning

Xue

Wang

et al. 2020

IEEE Trans. Instrum. Meas.

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Comparative Analysis of Median and Average Filters in Impulse Noise Suppression

Cited by 4 publications

References 14 publications

Pathological Brain Detection by Artificial Intelligence in Magnetic Resonance Imaging Scanning (Invited Review)

Pathological Brain Detection by Artificial Intelligence in Magnetic Resonance Imaging Scanning (Invited Review)

Impulse Noise Removal Algorithm Using Modified Adaptive Distance-Related Weighted Mean Filter

A 3-D Reconstruction Method of Dense Bubbly Plume Based on Laser Scanning

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