Median Filtering Using First-Order and Second-Order Neighborhood Pixels to Reduce Fixed Value Impulse Noise from Grayscale Digital Images

Mursal, Ali Salim Nasar; Ibrahim, Haidi

doi:10.3390/electronics9122034

Cited by 7 publications

(3 citation statements)

References 34 publications

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“…In the one-dimensional case, the median filter is a sliding window with an odd number of pixels. The value of the center pixel of the window is replaced by the median value of each pixel in the window [16][17] for filtering.…”

Section: A Image Denoising Based On Median Filtering Algorithmmentioning

confidence: 99%

Denoising Method of Interior Design Image based on Median Filtering Algorithm

Li¹

2022

IJACSA

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Interior design image generation process is prone to the interference of many factors, resulting in the interior design image denoising effect decreases, denoising time increases, so the interior design image denoising method based on median filtering algorithm is proposed. The architecture of interior design image collection is set up, including video signal conversion module, compression coding module, programmable logic chip module and power module. The interior design image collection is realized by using sensors to collect interior design related video information and converting video signals. Based on the results of image acquisition, the median filtering algorithm based on rough set theory is used to realize the denoising of interior design images. Experimental results show that the denoising effect of the proposed method is better, the average signal-to-noise ratio of interior design images is 54.6dB, and the denoising time is always lower than 0.3s, which can be widely used in practice.

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Section: A Image Denoising Based On Median Filtering Algorithmmentioning

confidence: 99%

Denoising Method of Interior Design Image based on Median Filtering Algorithm

Li¹

2022

IJACSA

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“…Image denoising is a fundamental and classic topic of image processing tasks. Due to the varying environment and sensor noise, the captured image usually contains noise and the transmission and storage process may also cause the image to be degraded by noise [1]. Therefore, image denoising is an important and indispensable part of many high-level vision tasks [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

A Multi-Scale Feature Extraction-Based Normalized Attention Neural Network for Image Denoising

et al. 2021

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Due to the rapid development of deep learning and artificial intelligence techniques, denoising via neural networks has drawn great attention due to their flexibility and excellent performances. However, for most convolutional network denoising methods, the convolution kernel is only one layer deep, and features of distinct scales are neglected. Moreover, in the convolution operation, all channels are treated equally; the relationships of channels are not considered. In this paper, we propose a multi-scale feature extraction-based normalized attention neural network (MFENANN) for image denoising. In MFENANN, we define a multi-scale feature extraction block to extract and combine features at distinct scales of the noisy image. In addition, we propose a normalized attention network (NAN) to learn the relationships between channels, which smooths the optimization landscape and speeds up the convergence process for training an attention model. Moreover, we introduce the NAN to convolutional network denoising, in which each channel gets gain; channels can play different roles in the subsequent convolution. To testify the effectiveness of the proposed MFENANN, we used both grayscale and color image sets whose noise levels ranged from 0 to 75 to do the experiments. The experimental results show that compared with some state-of-the-art denoising methods, the restored images of MFENANN have larger peak signal-to-noise ratios (PSNR) and structural similarity index measure (SSIM) values and get better overall appearance.

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“…In the field of GPR, image denoising methods can be divided into four groups: those based on spatial filtering, those based on transform domain, those based on subspace, and those based on deep learning (DL) [10][11][12]. Lee et al [13] proposed a denoising filter, Lee filter, which is based on a linear noise model and a minimum mean square achieved.…”

Section: Introductionmentioning

confidence: 99%

GPR B-Scan Image Denoising via Multi-Scale Convolutional Autoencoder with Data Augmentation

et al. 2021

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Ground-penetrating radar (GPR), as a non-invasive instrument, has been widely used in civil engineering. In GPR B-scan images, there may exist random noise due to the influence of the environment and equipment hardware, which complicates the interpretability of the useful information. Many methods have been proposed to eliminate or suppress the random noise. However, the existing methods have an unsatisfactory denoising effect when the image is severely contaminated by random noise. This paper proposes a multi-scale convolutional autoencoder (MCAE) to denoise GPR data. At the same time, to solve the problem of training dataset insufficiency, we designed the data augmentation strategy, Wasserstein generative adversarial network (WGAN), to increase the training dataset of MCAE. Experimental results conducted on both simulated, generated, and field datasets demonstrated that the proposed scheme has promising performance for image denoising. In terms of three indexes: the peak signal-to-noise ratio (PSNR), the time cost, and the structural similarity index (SSIM), the proposed scheme can achieve better performance of random noise suppression compared with the state-of-the-art competing methods (e.g., CAE, BM3D, WNNM).

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Median Filtering Using First-Order and Second-Order Neighborhood Pixels to Reduce Fixed Value Impulse Noise from Grayscale Digital Images

Cited by 7 publications

References 34 publications

Denoising Method of Interior Design Image based on Median Filtering Algorithm

Denoising Method of Interior Design Image based on Median Filtering Algorithm

A Multi-Scale Feature Extraction-Based Normalized Attention Neural Network for Image Denoising

GPR B-Scan Image Denoising via Multi-Scale Convolutional Autoencoder with Data Augmentation

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