Impulse Noise Detection Research Articles

Impulse Noise Image Restoration Using Nonconvex Variational Model and Difference of Convex Functions Algorithm.

In this article, the problem of impulse noise image restoration is investigated. A typical way to eliminate impulse noise is to use an L1 norm data fitting term and a total variation (TV) regularization. However, a convex optimization method designed in this way always yields staircase artifacts. In addition, the L1 norm fitting term tends to penalize corrupted and noise-free data equally, and is not robust to impulse noise. In order to seek a solution of high recovery quality, we propose a new variational model that integrates the nonconvex data fitting term and the nonconvex TV regularization. The usage of the nonconvex TV regularizer helps to eliminate the staircase artifacts. Moreover, the nonconvex fidelity term can detect impulse noise effectively in the way that it is enforced when the observed data is slightly corrupted, while is less enforced for the severely corrupted pixels. A novel difference of convex functions algorithm is also developed to solve the variational model. Using the variational method, we prove that the sequence generated by the proposed algorithm converges to a stationary point of the nonconvex objective function. Experimental results show that our proposed algorithm is efficient and compares favorably with state-of-the-art methods.

Detection and cancellation method of impulse noise in gearbox non-stationary vibration signals—a case study of port ship unloader

This work proposes a signal preprocessing framework that cancels out impulse noise in the non-stationary gearbox vibration signals of a ship unloader, which reduce the interference of non-cyclic impulses on the damage detection. The algorithm involves three main steps: (i) Preliminary localization of impulse noise, including suppressing components related to normal machine operation, segmenting the signal, and localizing abnormal sub-segment signal based on higher-order statistical criteria. (ii) Detection of impulse boundaries. The influence of random impulse noise on the signal envelope spectrum is studied, and an impulse indicator is constructed to accurately detect the boundaries of impulse noise in abnormal sub-segment signals. (iii) Cancellation of impulse noise. Replace the identified random impulse with Gaussian white noise. Laboratory and industrial data confirm the excellent performance of the proposed algorithm. It can accurately identify and cancels out the non-cyclic impulse noise in the non-stationary gearbox vibration signal, which facilitates the subsequent damage detection.

Impulse noise removal by using a nonconvex TGV regularizer and nonconvex fidelity

With the aim of acquiring high-quality restored images, this paper derives a novel nonconvex variational model for the removal of impulse noise. The investigated solver integrates the superiorities of nonconvex second-order total generalized variation (TGV) regularization and nonconvex data fidelity. More precisely, the usage of a nonconvex TGV regularizer helps to eliminate the staircase artifacts and simultaneously preserve edge details. Nonconvex fidelity, which enhances sparsity, is adopted to effectively detect impulse noise. Computationally, incorporating the popular iteratively reweighted ℓ1 algorithm and variable splitting method, we propose to adopt an efficient alternating direction method of multipliers for the purpose of rapidly resolving the designed optimization problem. Additionally, simulation examples have been executed to compare our method with several state-of-the-art techniques. Experimental results and quantitative comparisons confirm that the developed strategy outperforms other competitors for suppressing impulse noise (even high density) in both visual outcomes and recovery accuracy.

A robust iterative receiver for single carrier underwater acoustic communications under impulsive noise

Achieving reliable underwater acoustic (UWA) communications is challenging due to the rapid changes of UWA channels and presence of impulsive noise. The soft decision feedback equalization (SDFE) has attracted much attention due to its capability of tackling the rapid changes of UWA channels. However, the existing SDFE exhibits error floor and even divergence due to the presence of severe impulsive noise. To overcome this problem, an improved SDFE (ISDFE) is proposed, which incorporates impulsive noise detection, clipping level estimation and damping to handle the impulsive noise. As the performance of the ISDFE also depends on the accuracy of channel estimation (CE), we propose a variable kernel width maximum complex correntropy criterion Kalman filter (VKW-MCCC-KF) algorithm to achieve robust and accurate CE under impulsive noise. With the proposed ISDFE and VKW-MCCC-KF, an iterative receiver for single carrier (SC) UWA mobile communication under impulsive noise is proposed, which incorporates the adaptive broadband Doppler compensation, VKW-MCCC-KF based CE and ISDFE, working in an iterative manner. Experimental results show that, compared with existing receivers, the proposed receiver achieves better performance and robustness to impulsive noise under fast changing UWA channels.

Extended neighborhood-based road and median filter for impulse noise removal from depth map

In recent years there has been growing interest in the study of depth map's impulse noise detection and removal. The Rank-ordered Absolute Differences (ROAD) based on neighborhood statistics can effectively detect impulse noise for RGB images. However, since the statistical object of the ROAD is each pixel value in the neighborhood, and the neighborhood may contain other impulse noise, invalid depth zero-pixel points and other singular points, especially for large areas with depth missing, it is difficult for the algorithm to ensure the correctness and accuracy of the detection results. Therefore, the ROAD algorithm is not fully applicable to depth maps. Similarly, the median filter, as an effective impulse noise filtering algorithm, also has the same deficiency, so it is not suitable for areas in depth maps with no depth value. To address these issues, we present here an Extended Neighborhood-based ROAD and Median Filter algorithm to remove impulse noise from depth map. This approach improves the functionality of the ROAD algorithm and the Median Filter by extending the neighborhood of the current pixel adaptively, shielding zero-valued pixels and filtering out detected impulse noises in real time during detection, therefore effectively improving the accuracy of impulse noise filtering. It consequently reduces the miss rate and the error detection rate, and ultimately achieves a better edge-preserving and denoising effect. Our empirical evidence shows that the proposed algorithm outperforms existing denoising algorithms on both quantitative measures and visual perception qualities.

Adaptive Noise Detector and Partition Filter for Image Restoration

The random-value impulse noise (RVIN) detection approach in image denoising, which is dependent on manually defined detection thresholds or local window information, does not have strong generalization performance and cannot successfully cope with damaged pictures with high noise levels. The fusion of the K-means clustering approach in the noise detection stage is reviewed in this research, and the internal relationship between the flat region and the detail area of the damaged picture is thoroughly explored to suggest an unique two-stage method for gray image denoising. Based on the concept of pixel clustering and grouping, all pixels in the damaged picture are separated into various groups based on gray distance similarity features, and the best detection threshold of each group is solved to identify the noise. In the noise reduction step, a partition decision filter based on the gray value characteristics of pixels in the flat and detail areas is given. For the noise pixels in flat and detail areas, local consensus index (LCI) weighted filter and edge direction filter are designed respectively to recover the pixels damaged by the RVIN. The experimental results show that the accuracy of the proposed noise detection method is more than 90%, and is superior to most mainstream methods. At the same time, the proposed filtering method not only has good noise reduction and generalization performance for natural images and medical images with medium and high noise but also is superior to other advanced filtering technologies in visual effect and objective quality evaluation.

A Neighborhood Impact Driven K-Medoid Clustering and Fuzzy Logic Blended Approach for High Density Impulse Noise Detection and Removal

In the field of image processing, removing impulse noise has been regarded as one of the most important tasks, primarily because of the noise pattern it presents. Existing filters used the effect of only those non-noisy pixels which were present inside the specified windows ignoring the effect of the non-noisy pixels present in the surrounding windows. So, the least distant non-noisy pixels in the present window as well as in the surrounding windows may have an influence on the present window's noisy pixels. Hence, considering the above factors, in this paper, a two-step technique named KMDCIFF (K-medoid clustering identified fuzzy filter) is proposed for removing impulse noise from digital images. In the proposed KMDCIFF algorithm, the first step is noise detection using K-medoid clustering, followed by a fuzzy logic-acquainted noise reduction strategy that utilizes the least distant local and non-local non-noisy pixels for removal operation. The detection process involves the application of K-medoid clustering on all 5×5 windows produced by centering each pixel of the considered image. In order to remove noise, a 7×7 window is constructed with each detected noisy pixel in the center. Analyzing the impact of the least distant local and non-local pixel on each noisy pixel, the same is replaced by an estimated pixel’s intensity value obtained from the most influential non-noisy pixels. KMDCIFF is evaluated using well-known metrics for diverse types of images. At a high noise density of 80%, KMDCIFF exhibited significant peak-signal-to-noise-ratios (PSNRs) of 26.97 dB and 29.67 dB and structural similarity indexes (SSIMs) of 0.8045 and 0.9288 on random and fixed valued impulse noise impacted Lena image, respectively. Comparing the results of the contemporary study to those of previous studies of a similar kind in this sector, the results are unswervingly astounding.

Automatic Adaptive Filtering Technique for Removal of Impulse Noise Using Gabor Filter

Tremendous development in Internet of Things (IoT) and mobile devices lead to several images pooled on social media websites and communicated through networking channels. These images are mostly corrupted with impulse noises due to hot pixels generated in the camera sensors and communication channels. Adaptive mean filter technique removes impulse noise at low density but is unsuccessful as noise density increases and computationally expensive. In this paper, automatic adaptive filtering technique for removal of impulse (salt and pepper) noise is demonstrated. The proposed algorithm consists of impulse noise detection and noise removal modules. An automatic impulse noise detection module is based on mean and variance technique that selects the noisy pixels among the entire image. The noise removal module is based on replacement of noisy pixel through mean and edge direction using Gabor filter. The proposed technique demonstrated better robustness compared with existing techniques.

A SURVEY OF IMAGE DENOISING FILTERS BASED ON BOUNDARY DISCRIMINATION NOISE DETECTIO

Image denoising is an essential and complex activity that should be carried out before any other image processing because it checks for errors within the image(s) and rectifies them. There are ways to remove noise, the switching scheme is an outstanding method when equated to others, it initially segregates the noisy pixels and then filters them. Boundary Discriminative Noise Detection (BDND) is a type of algorithm that uses the switching method and is good for impulse noise detection, many works have been presented using several enhancements to detect noise from images using BDND. In this paper, we present a detailed outline of impulse noise and noise removal techniques by looking at over a decade of research conducted to establish a fundamental understanding of the Boundary discriminative noise detector algorithm used in image denoising. We analyzed 19 relevant papers through Google Scholar, focusing on three aspects: the methods for detecting noisy pixels, the type(s) of noise, and the major challenges. We found that many of the image denoising methods still use BDND and at least one algorithm is developed yearly except for 2017 to 2021, indicating the algorithm is significant in the field of image denoising. Furthermore, we wrap up the survey by highlighting some research challenges and offering a list of key recommendations to spur further research in this area.

Impulsive Noise Excision Using Robust Smoothing

The impulsive noise originated from regional lightning discharges can dramatically degrade the performance of skywave over-the-horizon radar (OTHR) target detection with generally 10–20-dB increase of noise level in the Doppler domain. As a result, it is necessary to excise impulsive noise in corrupted data while preserving the useful echo signal. Traditional techniques first locate the impulsive noise in slow-time data with a high-pass finite-duration impulse response (FIR) notch filter and then restore the contaminated samples by neighboring good data. However, the effect of impulsive noise is propagated into the subsequent filtered residuals, which will mislead the impulsive noise detection. Instead of the conventional detection-interpolation methods, we propose a novel impulsive noise excision approach based on robust smoothing. The proposed method can robustly smooth the corrupted data and determine the position of impulsive noise with a preset threshold against the residuals of corrupted data subtracting the smoothed signal. The contaminated data at last are replaced by the smoothed signal. Experimental results have demonstrated the effectiveness of our proposed method.

Random-Valued Impulse Noise Detection and Removal based on Local Statistics of Images

Random-valued impulse noise removal from images is a challenging task in the field of image processing and computer vision. In this paper, an effective three-step noise removal method was proposed using local statistics of grayscale images. Unlike most existing denoising algorithms that assume the noise density is known, our method estimated the noise density in the first step. Based on the estimated noise density, a noise detector was implemented to detect corrupted pixels in the second step. Finally, a modified weighted mean filter was utilized to restore the detected noisy pixels while leaving the noise-free pixels unchanged. The noise removal performance of our method was compared with 10 well-known denoising algorithms. Experimental results demonstrated that our proposed method outperformed other denoising algorithms in terms of noise detection and image restoration in the vast majority of the cases.

Speckle Noise Detection and Removal for Laser Speech Measurement Systems

Laser speech measurement is a new sound capture technology based on Laser Doppler Vibrometry (LDV). It avoids the need for contact, is easily concealed and is ideal for remote speech acquisition, which has led to its wide-scale adoption for military and security applications. However, lasers are easily affected by complex detection environments. Thus, speckle noise often appears in the measured speech, seriously affecting its quality and intelligibility. This paper examines all of the characteristics of impulsive noise in laser measured speech and proposes a novel automatic impulsive noise detection and removal method. This method first foregrounds noise using decorrelation based on a linear prediction (LP) model that improves the noise-to-signal ratio (NSR) of the measured signal. This makes it possible to detect the position of noise through a combination of the average short-time energy and kurtosis. The method not only precisely locates small clicks (with a duration of just a few samples), but also finds the location of longer bursts and scratches (with a duration of up to a hundred samples). The located samples can then be replaced by more appropriate samples whose coding is based on the LP model. This strategy avoids unnecessary processing and obviates the need to compromise the quality of the relatively large fraction of samples that are unaffected by speckle noise. Experimental results show that the proposed automatic speckle noise detection and removal method outperforms other related methods across a wide range of degraded audio signals.

A Local Consensus Index Scheme for Random-Valued Impulse Noise Detection Systems

The issue of impulse noise detection and reduction is a critical problem for image processing application systems. In order to detect impulse noises in corrupted images, a statistic named local consensus index (LCI) is proposed for quantitatively evaluating how noise free a pixel is, and then an impulse noise detection scheme based on LCI is introduced. First, the similarity between arbitrary two pixels in an image is quantified based on both their geometric distance and intensity difference, and the LCI of arbitrary pixel is calculated by summing all the similarity values of pixels in its neighborhood. As a new statistic, the value of LCI indicates the local consensus of the concerned pixel regarding its neighbors and could also tell whether a pixel is noise free or impulsive. Therefore, LCI can be directly used as an efficient indicator of impulse noise. Furthermore, to improve the performance of impulse noise detection, different strategies are applied to the pixels at flat regions and the ones with complex textures, since distributions of LCI value within those regions are totally different. As for impulse noise filtering, a hybrid graph Laplacian regularization (HGLR) method is introduced to restore the intensities of those pixels degraded by impulse noise. We conduct extensive experiments to verify the effectiveness of our impulsive noise detection and reduction method, and the results show that the proposed method outperforms the state-of-the-art techniques in terms of impulse detection and noise removal.

Impulsive Noise Detection for Speech Enhancement in HHT Domain

This paper introduces a novel single channel speech enhancement method in the time domain to mitigate the effects of acoustic impulsive noises. The ensemble empirical mode decomposition is applied to analyze the noisy speech signal. The estimation and selection of noise components is based on the impulsiveness index of decomposition modes. An adaptive threshold is proposed to define the criterion to select the noise components. The proposed method is evaluated in speech enhancement experiments considering four acoustic noises with different impulsiveness indices and non-stationarity degrees under various signal-to-noise ratios. Four speech enhancement algorithms are adopted as baseline in the evaluation analysis considering spectral and time domains. Seven objective measures are adopted to compare the proposed and baseline approaches in terms of speech quality and intelligibility. Results show that the proposed solution outperforms the competing algorithms for most of the noisy scenarios. The novel method shows particularly interesting performance when speech signals are corrupted by highly impulsive acoustic noises.

Design of a Test for Detecting the Presence of Impulsive Noise.

This paper proposes a new test method of detecting the presence of impulsive noise based on a complementary cumulative density function (CCDF). Impulsive noise severely degrades performance of communication systems and the conventional Kolmogorov–Smirnov (K–S) test may not perform well, because the test does not consider the characteristics of impulsive noise. In order to detect the presence of impulsive noise reliably, the CCDF of measurement samples is analyzed and compared with the CCDF of additive white Gaussian noise to find the difference between those CCDFs. Due to the nature of heavy-tails in impulsive noise, only the maximum difference may not be sufficient for the accurate detection of impulsive noise. Therefore, the proposed method applies the test hypothesis using the weighted sum of all the differences between those CCDFs. Simulation results justify that the proposed test is more robust and provides lower miss detection probability than the K–S test in the presence of impulsive noise.

Adaptive Trimmed Median Filter for Impulse Noise Detection and Removal with an Application to Mammogram Images

Mammography imaging is one of the most widely used techniques for breast cancer screening and analysis of abnormalities. However, due to some technical difficulties during the time of acquisition and digital storage of mammogram images, impulse noise may be present. Therefore, detection and removals of impulse noise from the mammogram images are very essential for early detection and further diagnosis of breast cancer. In this paper, a novel adaptive trimmed median filter (ATMF) is proposed for impulse noise (salt & pepper (SNP)) detection and removal with an application to mammogram image denoising. Automatic switching mechanism for updating the Window of Interest (WoI) size from ([Formula: see text]) to ([Formula: see text]) or ([Formula: see text]) is performed. The proposed method is applied on publicly available mammogram images corrupted with varying SNP noise densities in the range 5%–90%. The performance of the proposed method is measured by various quantitative indices like peak signal to noise ratio (PSNR), mean square error (MSE), image enhancement factor (IEF) and structural similarity index measure (SSIM). The comparative analysis of the proposed method is done with respect to other state-of-the-art noise removal methods viz., standard median filter (SMF), decision based median filter (DMF), decision based unsymmetric trimmed median filter (DUTMF), modified decision based unsymmetric trimmed median filter (MDUTMF) and decision based unsymmetric trimmed winsorized mean filter (DUTWMF). The superiority of the proposed method over other compared methods is well evident from the experimental results in terms of the quantitative indices (viz., PSNR, IEF and SSIM) and also from the visual quality of the denoised images. Paired t-test confirms the statistical significance of the higher PSNR values achieved by the proposed method as compared to the other counterpart techniques. The proposed method turned out to be very effective in denoising both high and low density noises present in (mammogram) images.

Impulse Noise Detection and Removal Method Based on Modified Weighted Median

Impulse noise generally occurs because of bit errors in progression of image acquisition and transmission. It is well known that median filtering method is an impulse noise removal method. Lots of modified median filters have been proposed in the last decades to improve the methods for noise suppression and detail preservation, which have their own deficiencies while identifying and restoring noise pixels. In this article, after deeply analyzing the reasons, such as decreased noise detection and noise removal accuracy that forms the basis of the deficiencies, this article proposes a modified weighted median filter method for color images corrupted by salt-and-pepper noise. In this method, a pixel is classified into either “noise free pixel” or “noise pixel” by checking the center pixel in the current filtering window with the extreme values (0 or 255) for an 8-bit image using noise detection step. Directional differences and the number of “good” pixels in the current filtering window modify the detected noise pixels. Simulation effects on considered test images reveal the proposed method to be improved over state-of-the-art de-noising methods in terms of PSNR and SSIM with pictorial comparative analysis.

Detection and Removal of Impulse Noise from Colour Image using Lagrange Interpolation and Centre Weighted Vector Median Filter

Detection and Removal of Impulse Noise from Colour Image using Lagrange Interpolation and Centre Weighted Vector Median Filter

Median filters combined with denoising convolutional neural network for Gaussian and impulse noises

Elimination of combined Gaussian and impulse noises in digital image processing with preservation of image details and suppression of noise are challenging problem. For this purpose, a new filter which is median filters combined with convolutional neural network for Gaussian and salt & pepper noises. The previous methods are application dependents; some used for impulse noise and other employed only for Gaussian noise. The elimination of Gaussian and impulse noise completed into two steps. First the detection of impulse noise with the rejection of noise by employed of 3 × 3 and 5 × 5 window size median filters. In the second step removal of Gaussian noise performed by residual learning denoising convolutional neural network. It is very favorable and the ability of learning and denoising performance in the field of digital image processing. Denoising convolutional neural network also has active Gaussian noise with an unknown level of noise. Experimental work showed that the proposed method can achieve low loss and root mean square error during training, high peak signal to noise ratio, low mean square error, image quality assessment with good quality and mean absolute error for close prediction between denoised and original color images.

A random-valued impulse noise removal algorithm via just noticeable difference threshold detector and weighted variation method

The paper presents a random-valued impulse noise (RVIN) removal algorithm by using distinct impulse noise detection and correction stages. In the noise detection phase, the local luminance adaption of an image is used to detect the obvious noise pixels firstly, and the just noticeable difference (JND) is introduced to detect the noise pixels hiding in image details afterward. In the noise candidate correction phase, the obvious noise pixels, almost distributing in the smooth region, are restored by a weighted mean filter. For the noise candidates hiding in image details, we adopt a weighted detail-preserving variation method to restore them. Since human visual perception and detail-preserving are taken into account during the course of the noises detection and restoration, our method outperforms some existing methods, both in visual and quantitative measurements.