Noise Removal Method Research Articles

Mixed Noise Removal via Laplacian Scale Mixture Modeling and Nonlocal Low-Rank Approximation.

Recovering the image corrupted by additive white Gaussian noise (AWGN) and impulse noise is a challenging problem due to its difficulties in an accurate modeling of the distributions of the mixture noise. Many efforts have been made to first detect the locations of the impulse noise and then recover the clean image with image in painting techniques from an incomplete image corrupted by AWGN. However, it is quite challenging to accurately detect the locations of the impulse noise when the mixture noise is strong. In this paper, we propose an effective mixture noise removal method based on Laplacian scale mixture (LSM) modeling and nonlocal low-rank regularization. The impulse noise is modeled with LSM distributions, and both the hidden scale parameters and the impulse noise are jointly estimated to adaptively characterize the real noise. To exploit the nonlocal self-similarity and low-rank nature of natural image, a nonlocal low-rank regularization is adopted to regularize the denoising process. Experimental results on synthetic noisy images show that the proposed method outperforms existing mixture noise removal methods.

ECG signal performance de-noising assessment based on threshold tuning of dual-tree wavelet transform

BackgroundSince the electrocardiogram (ECG) signal has a low frequency and a weak amplitude, it is sensitive to miscellaneous mixed noises, which may reduce the diagnostic accuracy and hinder the physician’s correct decision on patients.MethodsThe dual tree wavelet transform (DT-WT) is one of the most recent enhanced versions of discrete wavelet transform. However, threshold tuning on this method for noise removal from ECG signal has not been investigated yet. In this work, we shall provide a comprehensive study on the impact of the choice of threshold algorithm, threshold value, and the appropriate wavelet decomposition level to evaluate the ECG signal de-noising performance.ResultsA set of simulations is performed on both synthetic and real ECG signals to achieve the promised results. First, the synthetic ECG signal is used to observe the algorithm response. The evaluation results of synthetic ECG signal corrupted by various types of noise has showed that the modified unified threshold and wavelet hyperbolic threshold de-noising method is better in realistic and colored noises. The tuned threshold is then used on real ECG signals from the MIT-BIH database. The results has shown that the proposed method achieves higher performance than the ordinary dual tree wavelet transform into all kinds of noise removal from ECG signal.ConclusionThe simulation results indicate that the algorithm is robust for all kinds of noises with varying degrees of input noise, providing a high quality clean signal. Moreover, the algorithm is quite simple and can be used in real time ECG monitoring.

A New Image Denoising Method Based on Adaptive Multiscale Morphological Edge Detection

Wavelet transform is an effective method for removal of noise from image. But traditional wavelet transform cannot improve the smooth effect and reserve image’s precise details simultaneously; even false Gibbs phenomenon can be produced. This paper proposes a new image denoising method based on adaptive multiscale morphological edge detection beyond the above limitation. Firstly, the noisy image is decomposed by using one wavelet base. Then, the image edge is detected by using the adaptive multiscale morphological edge detection based on the wavelet decomposition. On this basis, wavelet coefficients belonging to the edge position are dealt with with the improved wavelet domain wiener filtering, and the others are dealt with with the improved Bayesian threshold and the improved threshold function. Finally, wavelet coefficients are inversely processed to obtain the denoised image. Experimental results show that this method can effectively remove the image noise without blurring edges and highlight the characteristics of image edge at the same time. The validation results of the denoised images with higher peak signal to noise ratio (PSNR) and structural similarity (SSIM) demonstrate their robust capability for real applications in the future.

Multiplicative Noise Removal Based on the Linear Alternating Direction Method for a Hybrid Variational Model

To preserve the edge, multiplicative noise removal models based on the total variation regularization have been widely studied, but they suffer from the staircase effect. In this paper, to preserve the edge and reduce the staircase effect, we develop a hybrid variational model based on the variable splitting method for multiplicative noise removal; the new model is a strictly convex objective function which contains the total variation regularization and a modified regularization term. We use the linear alternative direction method to find the minimal solution and also give the convergence proof of the proposed algorithm. Experimental results verify that the proposed model can obtain the better results for removing the multiplicative noise compared with the recent method.

Method of Improved Fuzzy Contrast Combined Adaptive Threshold in NSCT for Medical Image Enhancement.

Noises and artifacts are introduced to medical images due to acquisition techniques and systems. This interference leads to low contrast and distortion in images, which not only impacts the effectiveness of the medical image but also seriously affects the clinical diagnoses. This paper proposes an algorithm for medical image enhancement based on the nonsubsampled contourlet transform (NSCT), which combines adaptive threshold and an improved fuzzy set. First, the original image is decomposed into the NSCT domain with a low-frequency subband and several high-frequency subbands. Then, a linear transformation is adopted for the coefficients of the low-frequency component. An adaptive threshold method is used for the removal of high-frequency image noise. Finally, the improved fuzzy set is used to enhance the global contrast and the Laplace operator is used to enhance the details of the medical images. Experiments and simulation results show that the proposed method is superior to existing methods of image noise removal, improves the contrast of the image significantly, and obtains a better visual effect.

Combination of noise removal and contrast enhancement methods for the preprocessing of mammogram images - towards the detection of breast cancer

Combination of noise removal and contrast enhancement methods for the preprocessing of mammogram images - towards the detection of breast cancer

視覚情報と意味情報のハイブリッドノイズ除去法に基づくWeb画像データベースの自動構築

視覚情報と意味情報のハイブリッドノイズ除去法に基づくWeb画像データベースの自動構築

Research on Hand Recognition Method Based on Markov Random Field

Now, there is a problem to satisfy the demand of flexible manufacturing systems. Human-computer interaction intelligent assembly technology is more and more attention. Gesture recognition and action understanding is the basis of human-computer interaction assembly. For this reason, the research on hand gesture recognition is carried out. Firstly, the basic according to the features of the hand skin color separation method is described in this paper. The principle of YCrCb color space and the conversion between RGB color space is illustrated. It is described how YCrCb three-channel method eliminates the influence of brightness on color, and how to identify skin color and other colors. Secondly, it is explained that the principle of Markov random field. Markov random field's application in image segmentation problem is shown. Iterative algorithm process based on Markov random field is introduced. Finally, it is described how to combine the two methods, the skin color recognition and iterative algorithm. They are combined with a small area noise removal method to achieve a better image of the hand segmentation. And segmentation results and conclusions are shown.

Speckle noise removal applied to ultrasound image of carotid artery based on total least squares model.

An ultrasonic image speckle noise removal method by using total least squares model is proposed and applied onto images of cardiovascular structures such as the carotid artery. On the basis of the least squares principle, the related principle of minimum square method is applied to cardiac ultrasound image speckle noise removal process to establish the model of total least squares, orthogonal projection transformation processing is utilized for the output of the model, and the denoising processing for the cardiac ultrasound image speckle noise is realized. Experimental results show that the improved algorithm can greatly improve the resolution of the image, and meet the needs of clinical medical diagnosis and treatment of the cardiovascular system for the head and neck. Furthermore, the success in imaging of carotid arteries has strong implications in neurological complications such as stroke.

Accuracy Improvement for Predicting Parkinson's Disease Progression.

Parkinson’s disease (PD) is a member of a larger group of neuromotor diseases marked by the progressive death of dopamineproducing cells in the brain. Providing computational tools for Parkinson disease using a set of data that contains medical information is very desirable for alleviating the symptoms that can help the amount of people who want to discover the risk of disease at an early stage. This paper proposes a new hybrid intelligent system for the prediction of PD progression using noise removal, clustering and prediction methods. Principal Component Analysis (PCA) and Expectation Maximization (EM) are respectively employed to address the multi-collinearity problems in the experimental datasets and clustering the data. We then apply Adaptive Neuro-Fuzzy Inference System (ANFIS) and Support Vector Regression (SVR) for prediction of PD progression. Experimental results on public Parkinson’s datasets show that the proposed method remarkably improves the accuracy of prediction of PD progression. The hybrid intelligent system can assist medical practitioners in the healthcare practice for early detection of Parkinson disease.

Extraction of Cancer Affected Regions in Mammogram Images by Clustering and Classification Algorithms

Objectives/Backgrounds: The breast cancer has increased significantly in the last few years. It is one type of cancer and is the second deadliest disease in the world wide. Recently, cancer is diagnosed by various test such as mammography, ultrasound, etc. Mammography is used to breast imaging to help in detecting breast cancer. Methods/Statistical Analysis: The Mammogram images are taken for the analysis to find the tumor affected regions by data mining techniques in this research work. This work uses the Median filter method for noise removal and Gaussian filter for image enhancement of preprocessing the images. The k-Means algorithm, which is easily detected and extracts tumor area by means of intensity values by segmenting the mammography images. Two types of mammography images; normal and abnormal are given as input to the algorithms. After clustering the images by k-Means algorithm, the results found are classified by J48, JRIP, Support Vector Machines (SVM), Naive Bayes and CART algorithms to verify the accuracy of the results based on its pixel values. Findings: The performance of taken classification algorithms is compared and find out the best classifier in terms of its accuracy, sensitivity and specificity. Improvements: In the future, the other classifiers and feature selection algorithms are applied to extract the mammography images. Also, it gives more than fifty images for analysis.

Asynchronous noise removal for earbud-based PPG sensors.

This paper addresses motion noise removal for an earbud-based reflective photoplethysmographic (PPG) sensor. This system can be worn comfortably on the ear for a long time. However, body movements such as walking and running cause signal degradation. To improve signal quality, I propose a novel asynchronous noise removal method using only the right and left PPG signals. It cancels not only uncorrelated noise components but also asynchronous noise components by simply introducing a weight function in the calculation process of a Wiener filter. Experimental results demonstrate that heart rate was successfully measured by this system. The simplicity of the procedure and effectiveness of the proposed method may make current systems more reliable.

Discontinuity preserving method for noise removal of multi-carrier signals

A nonlinear method based on anisotropic diffusion notion is proposed in this paper to remove noise from noisy signals modulated with multiple carrier signals by preserving carrier signals as well as discontinuities present in the original noiseless signals. Gaussian and correlated noise contaminating signals with up to four carriers are considered here. Our algorithm proposed here is implemented with both explicit and semi-implicit discretization schemes. Experiments presented here demonstrate promising results indicating a better performance for our nonlinear noise removal method in comparison with the state-of- art in the literature.

A fast 3D adaptive bilateral filter for ultrasound volume visualization

Background and objectiveThis paper introduces an effective noise removal method for medical ultrasound volume data. Ultrasound data usually need to be filtered because they contain significant noise. Conventional two-dimensional (2D) filtering methods cannot use the implicit information between adjacent layers, and existing 3D filtering methods are slow because of complicated filter kernels. Even though one filter method utilizes simple filters for speed, it is inefficient at removing noise and does not take into account the characteristics of ultrasound sampling. To solve this problem, we introduce a fast filtering method using parallel bilateral filtering and adjust the filter window size proportionally according to its position. MethodsWe devised a parallel bilateral filtering by obtaining a 3D summed area table of a quantized spatial filter. The filtering method is made adaptive by changing the kernel window size according to the distance from the ultrasound signal transmission point. ResultsExperiments were performed to compare the noise removal and loss of original data of the anisotropic diffusion filtering, bilateral filtering, and adaptive bilateral filtering of ultrasound volume-rendered images. The results show that the adaptive filter correctly takes into account the sampling characteristics of the ultrasound volumes. ConclusionsThe proposed method can more efficiently remove noise and minimize distortion from ultrasound data than existing simple or non-adaptive filtering methods.

Stripe noise removal for infrared image by minimizing difference between columns

This paper introduces a novel approach to eliminate stripe noise in infrared images. The differences between bias voltages in column readout circuit of uncooled infrared sensors result in strong stripe noise which changes slowly in time. The problem can be solved by estimating the bias of each column of infrared images and correcting infrared images with the estimated biases. The bias estimation is translated into an energy optimization problem in the paper. The optimization aims to minimize difference between neighboring columns of images. Our approach can be processed on a single image, or in a recursive way in order to significantly reduce the computation in one frame time. Our approach is compared to the state-of-the-art the stripe noise removal method using realistic infrared images, and the experimental results show the effectiveness and efficiencies of our proposed approach.

Speckle noise removal via nonlocal low-rank regularization

This paper presents a novel method for speckle noise removal. We propose a nonlocal low-rank regularization (NLR) approach toward exploiting structured sparsity and explore its application into speckle noise removal. A nonconvex surrogate functions for the rank instead of the convex nuclear norm is proposed. To further improve the computational efficiency of the proposed algorithm, we have developed a fast implementation using augmented Lagrange multiplier (ALM) method. We experimentally demonstrate the excellent performance of the technique, in terms of both Peak Signal to Noise Ratio (PSNR) and Structural Similarity (SSIM).

A robust bi-sparsity model with non-local regularization for mixed noise reduction

Sparse representation model (SRM) has been widely used in many image processing and computer vision tasks. However, the conventional SRM usually neglects the prior knowledge about similar signals. Considering the fact that similar signals also have subtle differences, in this paper we propose a robust bi-sparsity model (RBSM) to effectively exploit the prior knowledge about the similarities and the distinctions of signals. In RBSM, similar signals are encouraged to be coded on the same sub-dictionary. But the distinctiveness of similar signals is also addressed by imposing the l0-norm regularization on the difference between each coefficient and its non-local means. In addition, a weight vector is incorporated into the loss function to make the proposed model robust to outliers. We apply RBSM for mixed noise reduction and experimental results show that our proposed model is superior to several state-of-the-art mixed noise removal methods.

Weighted Joint Sparse Representation for Removing Mixed Noise in Image.

Joint sparse representation (JSR) has shown great potential in various image processing and computer vision tasks. Nevertheless, the conventional JSR is fragile to outliers. In this paper, we propose a weighted JSR (WJSR) model to simultaneously encode a set of data samples that are drawn from the same subspace but corrupted with noise and outliers. Our model is desirable to exploit the common information shared by these data samples while reducing the influence of outliers. To solve the WJSR model, we further introduce a greedy algorithm called weighted simultaneous orthogonal matching pursuit to efficiently approximate the global optimal solution. Then, we apply the WJSR for mixed noise removal by jointly coding the grouped nonlocal similar image patches. The denoising performance is further improved by incorporating it with the global prior and the sparse errors into a unified framework. Experimental results show that our denoising method is superior to several state-of-the-art mixed noise removal methods.

A hybrid optimization method for multiplicative noise and blur removal

The main contribution of this paper is to propose a new hybrid optimization method for the multiplicative noise and blur removal problem. A degraded image can often be recovered efficiently by minimizing an objective function which consists of a data-fidelity term and a regularization term. In the paper, we apply the quadratic penalty function method combined with the alternating direction method to minimize the corresponding objective function. Numerical experiments are presented to demonstrate the effectiveness of the proposed method. Experimental results illustrate the state-of-the-art performance of the proposed method to handle multiplicative noise and blur removal problem.

An enhancement method for X-ray image via fuzzy noise removal and homomorphic filtering

Many practical images captured from X-ray systems not only are corrupted by noise, but also are of non-uniformity and low-luminance. Enhancing these images by noise removal, gray-level transformation and/or contrast enhancement, is thus necessary and crucial before further processing.In this paper, a new scheme of enhancement method for X-ray image is presented, which consists of a fuzzy noise removal method and a homomorphic filtering method. The fuzzy noise removal method is composed of fuzzy noise detection and noise filtering. Region detection and degree detection are presented for fuzzy noise detection, and a modified weight median filter is proposed for filtering noise. Then the high-pass enhancement filter is introduced for image sharpening in the proposed homomorphic filtering method. A variety of degraded images and a real X-ray images have been handled by the proposed method, as well as the several existing methods. The comparisons of those experimental results show that the performance of our method overwhelms those of the existing ones.