Pepper Noise Removal Research Articles

Study of Salt and Pepper Noise Removal by Different Filtering Techniques

Study of Salt and Pepper Noise Removal by Different Filtering Techniques

An effective method for salt and pepper noise removal based on algebra and fuzzy logic function

An effective method for salt and pepper noise removal based on algebra and fuzzy logic function

DTMF: Decision Based Trimmed Multimode Approach Filter for Denoising MRI Images

The brain MRI image denoising is a challenging and attracting field for young researchers because it enhances the quality of medical images. Salt and pepper noise is the most dangerous noise which reduces the accuracy of brain diagnosis, and it damages the brain medical images severely, that leads to neurologists to fix incorrect treatments or surgery. The pitfalls raised in the existing denoising methods are less Peak signal to noise ratio, high time consumption and incapable for enormous level of noise range. Hence, this research proposes a novel denoising filter which is entitled as ‘Decision based Trimmed Multimode approach oriented Filter (DTMF)’ for salt and pepper noise removal. Herein, the noise removal section is branched into six steps which efficiently reduce noises based on multimode of majority strength. The main concepts used in this research are viz. decision based approach, trimming process, majority of intensity, median, mean, dynamic windows and Square shaped Exemplar Modeled Patch Mechanism (SEMPM). The essential contributions of this approach are i) designing rule set for majority strength structured multimode denoising, ii) computation of majority property oriented parameters like majority-instance, majority strength and majority value, iii) novel SEMPM mechanism to predict noise-free data. The novel SEMPM mechanism grants a solution for the prediction of noise-free pixel in account of the noisy pixels whose surrounding window is completely packed by noisy data. The proposed decision-based approach removes the salt and pepper noise with high peak signal to noise ratio even for huge noise range, with reasonable time consumption.

Salt and pepper noise removal method based on graph signal reconstruction

Salt and Pepper (SAP) noise is a very common type of impulse noise which frequently arises in image acquisition and transmission. Removing SAP noise is an important task in image restoration. In this paper, we model the SAP removal problem as a graph signal reconstruction problem, so that the information of noisy pixels can also participate in the estimation of their gray values. In the proposed model, the regularization term depends on the Power Spectral Density (PSD) of original signals, which makes the reconstructed signal follows the frequency content of the potential original signal well. Further, an algorithm for estimating the local PSD of an image block is designed and corresponding theoretical analysis is given. Finally, a block SAP noise removal algorithm is proposed. The computational complexity is also discussed. Experimental results on ten commonly used test images demonstrate that the proposed algorithm outperforms the state-of-the-art methods.

A random exploration based fast adaptive and selective mean filter for salt and pepper noise removal in satellite digital images

The digital image is one of the discoveries that play an important role in various aspects of modern human life. These findings are useful in various fields, including defense (military and non-military), security, health, education, and others. In practice, the image acquisition process often suffers from problems, both in the process of capturing and transmitting images. Among the problems is the appearance of noise which results in the degradation of information in the image and thus disrupts further processes of image processing. One type of noise that damages digital images is salt and pepper noise which randomly changes the pixel values to 0 (black) or 255 (white). Researchers have proposed several methods to deal with this type of noise, including median filter, adaptive mean filter, switching median filter, modified decision based unsymmetric trimmed median filter, and different applied median filter. However, this method suffers from a decrease in performance when applied to images with high-intensity noise. Therefore, in this research, a new filtering method is proposed that can improve the image by randomly exploring pixels, then collecting the surrounding pixel data from the processed pixels (kernel). The kernel will be enlarged if there are no free-noise pixels in the kernel. Furthermore, the damaged pixels will be replaced using the mean data centering statistic. Images enhanced using the proposed method have better quality than the previous methods, both quantitatively (SSIM and PSNR) and qualitatively.

Salt and Pepper Noise Removal Method Based on the Edge-Adaptive Total Variation Model

The traditional median filter can handle the image salt and pepper noise better. However, when the noise intensity is large, it is often necessary to enlarge the filter window to ensure the denoising effect, but the enlarged window may also cause excessive smoothing of the image, loss of texture details, and blurred edges. In view of the strong edge preservation characteristics of variational model denoising, we propose a salt and pepper noise removal method based on the edge-adaptive total variational model. Firstly, the image is segmented into edge regions and non-edge regions by edge detection operators. Secondly, the salt and pepper noise of the image is processed using the median filter and adaptive total variation model, respectively. Lastly, the non-edge regions processed by the median filter and the edge regions processed by the adaptive total variation model are extracted for splicing. The experimental results show that the method cannot only effectively remove salt and pepper noise, but also effectively protect the main edge details of the image.

Detailed Study of Low Density Salt and Pepper Noise Removal from Digital Color Images

Color digital images are greatly affected by salt and pepper noise, which negatively affects the clarity of the image and its characteristics. In this paper research we will investigate various filter used to deal with salt and pepper noise, a comparison will be done between these filters according to the calculated values of MSE and PSNR. An improvement procedure will be added to each filter to show how this improvement will enhance the values of MSE and PSNR and thus enhance the quality of the denoised image. Various noise densities will be experimented and it will be shown how median filter can be highly recommended to reduce the negative effects of salt and pepper noise.

Salt and pepper noise removal method based on stationary Framelet transform with non‐convex sparsity regularization

Salt and pepper noise occurs randomly and causes image degradation. Numerous denoising methods have been proposed to suppress this noise. However, existing methods have two main limitations. First, noise characteristics, such as noise location information and sparsity, are often described inaccurately or even ignored. Second, many existing methods separate the contaminated image into a recovered image and a noise part, leading to the recovery of an image with unsatisfactory smooth and detailed parts. In this study, the authors introduce a noise detection strategy to determine the position of the noise and employ a non-convex sparsity regularization depicted by l p ${l_p}$ quasi-norm to describe the sparsity of the noise, thereby addressing the first limitation. We adopt the morphological component analysis framework with stationary Framelet transform to decompose the processed image into the cartoon, texture, and noise parts to resolve the second limitation. Then, the proposed model is applied by using the alternating direction method of multipliers (ADMM). Finally, experiments are conducted to verify the proposed method and compare it with some current state-of-the-art denoising methods. The experimental results show that the proposed method can remove salt and pepper noise while preserving the details of the processed image and outperforming some state-of-the-art methods.

Image Noise Removal Method Based on Thresholding and Regularization Techniques

In this article, a salt and pepper noise (SPN) removal method is proposed based on thresholding and regularization techniques. The proposed method utilizes the ability to remove noise from an image denoising model based on Total Variation (TV) regularization and characteristics of SPN. First, a technique based on the characteristic of SPN is proposed to detect noisy pixels. Second, a modified TV regularization-based method is applied to restore the above noisy pixels. In addition, numerical implementation of the model based on the Nesterov optimal method is also provided. Five test cases with various noise levels for a large natural image dataset were studied in the experiments. The peak signal-to-noise ratio and structural similarity metrics were employed to assess the image quality after denoising. The experimental results indicated that the proposed method removes SPN remarkably and outperforms state-of-the-art image denoising methods for SPN.

Salt and Pepper Noise Removal Method Based on Graph Signal Reconstruction

Salt and Pepper Noise Removal Method Based on Graph Signal Reconstruction

On the Properties of Some Adaptive Morphological Filters for Salt and Pepper Noise Removal

Mathematical Morphology (MM) is a tool that can be applied to many digital image processing tasks that include the reduction of impulsive or salt and pepper noise in grayscale images. The morphological filters used for this task are filters resulting from two basic operators: erosion and dilation. However, when the level of contamination of the image is higher, these filters tend to distort the image. In this work we propose a pair of operators with properties, that better adapt to impulsive noise than other classical morphological filters, it is demonstrated to be increasing idempotent morphological filters. Furthermore, the proposed pair turns out to be a ?-filter and a ?-filter which allow to build morphological openings and closings. Finally, they are compared with other filters of the state-of-the-art such as: SMF, DBAIN, AMF and NAFSM, and have shown a better performance in time-quality ratio when the noise level is above 50%.

Image Denoising Based on Adaptive Sector Rotation Median Filter

By analyzing the characteristics of different median filtering algorithms, an adaptive fan rotation median filtering method based on the standard median filtering method is proposed to restore the details of the image to the maximum extent for achieving better pepper and salt noise removal effect. The proposed method can change the window size adaptively according to the pollution degree in the window, and calculate the gray difference value for different areas, judge the correlation between the center point and different areas according to the gray difference value, and take the median value of the highest correlation area as output, which can restore the details of the image while removing the noise. The experimental results show that the new algorithm has a high similarity with the original image structure and a high peak signal-to-noise ratio after filtering, and can better recover the image details. Compared with other methods, the noise removal effect and detail recovery effect are better.

Troika Generative Adversarial Network (T-GAN): A Synthetic Image Generator That Improves Neural Network Training for Handwriting Classification

Training an artificial neural network for handwriting classification requires a sufficiently sized annotated dataset in order to avoid overfitting. In the absence of sufficient instances, data augmentation techniques are normally considered. In this paper, we propose the troika generative adversarial network (T-GAN) for data augmentation to address the scarcity of publicly labeled handwriting datasets. T-GAN has three generator subnetworks architectured to have some weight-sharing in order to learn the joint distribution from three specific domains. We used T-GAN to augment the data from a subset of the IAM Handwriting Database. We then compared this with other data augmentation techniques by measuring the improvements brought by each technique to the handwriting classification accuracies in three types of artificial neural networks (ANNs): deep ANN, convolutional neural network (CNN), and deep CNN. The data augmentation technique involving the T-GAN yielded the highest accuracy improvements in each of the three ANN classifier types – outperforming the standard techniques of image rotation, affine transformation, and combination of these two – as well as the technique that uses another GAN-based model, the coupled GAN (CoGAN). Furthermore, a paired t-test between the 10-fold cross-validation results of the T-GAN and CoGAN, the second-best augmentation technique in this study, on a deep CNN-made classifier confirmed the superiority of the data augmentation technique that uses the T-GAN. Finally, when the generated synthetic data instances from the T-GAN were further enhanced using the pepper noise removal and median filter, the classification accuracy of the trained CNN and deep CNN classifiers were further improved to 93.54% and 95.45%, respectively. Each of these is a big improvement from the original accuracies of 67.43% and 68.32%, respectively of the 2 classifiers trained on the original unaugmented dataset. Thus, data augmentation using T-GAN – coupled with the mentioned two image noise removal techniques – can be a preferred pre-training technique for augmenting handwriting datasets with insufficient data samples.

An adaptive content based closer proximity pixel replacement algorithm for high density salt and pepper noise removal in images

An Adaptive Content based Closer Proximity Pixel Replacement algorithm for the removal of high density salt and pepper noise in images is proposed. The algorithm uses decision tree to identify and correct the pixels of the image is noisy or not. The algorithm finds Euclidean distance between the processed pixel and the number of non-noisy pixels inside the current processing kernel. The algorithm requires only two non-noisy pixels to be present in kernel for the algorithm to operate. The faulty pixels are replaced only by the median of pixels that occurs more frequently in the current processing kernel based on the Euclidean distance. The algorithm increases the window size by two when there are no non-noisy pixels in the current processing kernel. The proposed algorithm was compared with 16 standard and existing algorithms derived from recent literatures. Exhaustive experiments on standard database images suggest that the algorithm exhibit excellent noise suppression and good information preservation characteristics even at very high noise densities.

Salt and pepper noise removal from images: An extensive analysis

In the process of image acquisition and transmission, the digital images are often corrupted with impulse noise. This is mainly because of the errors in the sensors or in the communication channel. It is highly imperative that this noise should be removed prior to its subjection for further processing, such as edge detection, image segmentation, object recognition, and pattern recognition. Various methods have been developed for salt and pepper noise removal from corrupted images. This paper analyses the various methods proposed for removal of salt and pepper noise from images.

A two-stage filter for high density salt and pepper denoising

Image restoration is an important and interesting problem in the field of image processing because it improves the quality of input images, which facilitates postprocessing tasks. The salt-and-pepper noise has a simpler structure than other noises, such as Gaussian and Poisson noises, but is a very common type of noise caused by many electronic devices. In this article, we propose a two-stage filter to remove high-density salt-and-pepper noise on images. The range of application of the proposed denoising method goes from low-density to high-density corrupted images. In the experiments, we assessed the image quality after denoising using the peak signal-to-noise ratio and structural similarity metric. We also compared our method against other similar state-of-the-art denoising methods to prove its effectiveness for salt and pepper noise removal. From the findings, one can conclude that the proposed method can successfully remove super-high-density noise with noise level above 90%.

Salt and Pepper Noise Removal using Pixon-based Segmentation and Adaptive Median Filter

Removing salt and pepper noise is an active research area in image processing. In this paper, a two-phase method is proposed for removing salt and pepper noise while preserving edges and fine details. In the first phase, noise candidate pixels are detected which are likely to be contaminated by noise. In the second phase, only noise candidate pixels are restored using adaptive median filter. In terms of noise detection, a two-stage method is utilized. At first, a thresholding is applied on the image to initial estimation of the noise candidate pixels. Since some pixels in the image may be similar to the salt and pepper noise, these pixels are mistakenly identified as noise. Hence, in the second step of the noise detection, the pixon-based segmentation is used to identify the salt and pepper noise pixels more accurately. Pixon is the neighboring pixels with similar gray levels. The proposed method was evaluated on several noisy images, and the results show the accuracy of the proposed method in salt and pepper noise removal and outperforms to several existing methods.

Development and Verification of Novel Black and White Area Preserving Salt and Pepper Noise Removal Image Processing Design

Development and Verification of Novel Black and White Area Preserving Salt and Pepper Noise Removal Image Processing Design

Adaptive Technique for Salt and Pepper Noise Removal through Functional Link Artificial Neural Network

In this paper, an adaptive method for removing salt and pepper noise from images is proposed. A second order difference operator is used to locate the corrupted pixels in images by comparing with a threshold, which is selected adaptively using the image properties. A functional link artificial neural network (FLANN) based method is proposed to set a threshold for each corrupted image for identification of noisy pixels using recursive zero attracting least mean square (RZALMS) as the updating algorithm. Median filter is used to eliminate noise from the detected pixel locations.

A nonlinear acceleration method for iterative algorithms

Iterative methods have led to better understanding and solving problems such as missing sampling, deconvolution, inverse systems, and impulsive and Salt and Pepper noise removal problems. However, the challenges regarding the speed of convergence and or the accuracy of the answer still remain. In order to improve the existing iterative algorithms, a non-linear method is discussed in this paper. The mentioned method is analyzed from different aspects, including its convergence and its ability to accelerate recursive algorithms. We show that this method is capable of improving Iterative Method (IM) as a non-uniform sampling reconstruction algorithm and some other iterative sparse recovery algorithms such as Iterative Reweighted Least Squares (IRLS), Iterative Method with Adaptive Thresholding (IMAT), Smoothed ℓ0 (SL0) and Alternating Direction Method of Multipliers (ADMM) for solving LASSO problem family (including LASSO itself, LASSO-LSQR and Group-LASSO). It is also capable of both accelerating and stabilizing the well-known Chebyshev Acceleration (CA) method. Furthermore, the proposed algorithm can extend the stability range by reducing the sensitivity of iterative algorithms to the changes of adaptation rate.