Noise Reduction Filter Research Articles

Comparison of FIR and IIR Filters for Audio Signal Noise Reduction

An audio signal is often used as a medium for spreading information. However, this signal is easily disrupted by noise from the environment around which the signal is taken, so a filter is needed to eliminate it. In this article, sound signals will be filtered using two types of filters, namely FIR with Hamming window method and IIR with the Butterworth method. Both filters are applied as low pass types with a cut-off frequency of 4000 Hz and an order of 100. Both filters will be employed to filter a 5000 Hz generated audio noise. The SNR and execution time of the filtered signal will then be compared to determine which filter is more effective. The result shows that FIR filters perform better than IIR filters for filtering audio noise.

EVALUATION OF A SLAM-BASED POINT CLOUD FOR DEFLECTION ANALYSIS IN HISTORIC TIMBER FLOORS

Abstract. This paper aims at evaluating the possibility of using wearable mobile mapping solutions as a tool for detecting deflections in timber floors. These construction systems are prone to present this type of damage due to the mechanical properties of the wood (relatively low flexural stiffness and creep behaviour). During this study we have evaluated the chance of introducing an additional stage to the general workflow. This stage is devoted to reduce the noise of the 3D point cloud by using the Statistical Outlier Removal filter in combination with a noise-reduction filter such as the Anisotropic filter, the PointCleannet or the Scored-based denoised networks (Deep Learning methods). According with our results, the use of this strategies improves the quality of the 3D point cloud form a qualitative and quantitative point of view. However, these improvements seem to be not sufficient for using this product as a universal source of information for deflection analysis. In this sense, and according with the sensor and study case exploited, this type of point clouds could be used in floors with 5-8-meter length and a relative deflection of about L/200 or higher.

Fast Point Cloud Registration Method with Incorporation of RGB Image Information

Point cloud registration has a wide range of applications in 3D reconstruction, pose estimation, intelligent driving, heritage conservation, and digital cities. The traditional iterative closest point (ICP) algorithm has strong dependence on the initial position, poor robustness, and low timeliness. To address the above issues, a fast point cloud registration method that incorporates RGB image information is proposed. The SIFT algorithm is used to detect feature points of point clouds corresponding to the RGB image, followed by feature point matching. The RANSAC algorithm is applied to remove erroneous point pairs in order to calculate the initial transformation matrix. After applying a pass-through filter for noise reduction and transiting down with a voxel grid, the point cloud is subjected to rotation and translation transformation for initial registration. On the basis of initial alignment, the FR-ICP algorithm is utilized for achieving precise registration. This method not only avoids the problem of ICP easily getting stuck in local optima, but also has higher registration accuracy and efficiency. Experimental studies were conducted based on point clouds of automotive parts collected in real scenes, and the results showed that the proposed method has a registration error of only 0.487 mm. Among the same group of experimental point clouds with comparable registration error, the proposed method showed a speed improvement of 69%/48% compared to ICP/FR-ICP with regard to registration speed.

A Multi-Feature Fusion Framework for Automatic Skin Cancer Diagnostics

Malignant melanoma is the most invasive skin cancer and is currently regarded as one of the deadliest disorders; however, it can be cured more successfully if detected and treated early. Recently, CAD (computer-aided diagnosis) systems have emerged as a powerful alternative tool for the automatic detection and categorization of skin lesions, such as malignant melanoma or benign nevus, in given dermoscopy images. In this paper, we propose an integrated CAD framework for rapid and accurate melanoma detection in dermoscopy images. Initially, an input dermoscopy image is pre-processed by using a median filter and bottom-hat filtering for noise reduction, artifact removal, and, thus, enhancing the image quality. After this, each skin lesion is described by an effective skin lesion descriptor with high discrimination and descriptiveness capabilities, which is constructed by calculating the HOG (Histogram of Oriented Gradient) and LBP (Local Binary Patterns) and their extensions. After feature selection, the lesion descriptors are fed into three supervised machine learning classification models, namely SVM (Support Vector Machine), kNN (k-Nearest Neighbors), and GAB (Gentle AdaBoost), to diagnostically classify melanocytic skin lesions into one of two diagnostic categories, melanoma or nevus. Experimental results achieved using 10-fold cross-validation on the publicly available MED-NODEE dermoscopy image dataset demonstrate that the proposed CAD framework performs either competitively or superiorly to several state-of-the-art methods with stronger training settings in relation to various diagnostic metrics, such as accuracy (94%), specificity (92%), and sensitivity (100%).

Autoencoder-based dense denoiser and block-based wiener filter for noise reduction of optical coherence tomography

Optical Coherence Tomography (OCT) is an advanced imaging modality used for diagnosis of retinal abnormalities. OCT is acquired using low coherence light waves, typically infra-red waves having resolution in micrometres so as to capture the retinal layers present in the eye. Analysing variation in thickness of different retinal layers using OCT can be used for diagnosis. However, these layers are not clearly visible due to the presence of varying amounts of speckle noise, due to which the efficacy of further diagnosis gets compromised. Despite multiple approaches being available for denoising of OCT images, an undesirable over smoothening of images, leads to loss of structural edge details, thereby leading to inaccurate diagnosis. Thus, an efficient approach that removes speckle noise, without compromising on the significant image details, is preferred. This paper presents an approach to eliminate the speckle noise from OCT images using an Autoencoder-based Dense Denoiser (ADD) neural network and Block-based Wiener Filter (BBWF).

Emerging technology for intraoperative margin assessment and post-operative tissue diagnosis for breast-conserving surgery

IntroductionTissue-preserving surgery is utilized progressively in cancer therapy, where a clear surgical margin is critical to avoid cancer recurrence, specifically in breast cancer (BC) surgery. The Intraoperative pathologic approaches that rely on tissue segmenting and staining have been recognized as the ground truth for BC diagnosis. Nevertheless, these methods are constrained by its complication and timewasting for tissue preparation. ObjectiveWe present a non-invasive optical imaging system incorporating a hyperspectral (HS) camera to discriminate between cancerous and non-cancerous tissues in ex-vivo breast specimens, which could be an intraoperative diagnostic technique to aid surgeons during surgery and later a valuable tool to assist pathologists. MethodsWe have established a hyperspectral Imaging (HSI) system comprising a push-broom HS camera at wavelength 380∼1050 nm with source light 390∼980 nm. We have measured the investigated samples' diffuse reflectance (Rd), fixed on slides from 30 distinct patients incorporating mutually normal and ductal carcinoma tissue. The samples were divided into two groups, stained tissues during the surgery (control group) and unstained samples (test group), both captured with the HSI system in the visible and near-infrared (VIS-NIR) range. Then, to address the problem of the spectral nonuniformity of the illumination device and the influence of the dark current, the radiance data were normalized to yield the radiance of the specimen and neutralize the intensity effect to focus on the spectral reflectance shift for each tissue. The selection of the threshold window from the measured Rd is carried out by exploiting the statistical analysis by calculating each region's mean and standard deviation. Afterward, we selected the optimum spectral images from the HS data cube to apply a custom K-means algorithm and contour delineation to identify the regular districts from the BC regions. ResultsWe noticed that the measured spectral Rd for the malignant tissues of the investigated case studies versus the reference source light varies regarding the cancer stage, as sometimes the Rd is higher for the tumor or vice versa for the normal tissue. Later, from the analysis of the whole samples, we found that the most appropriate wavelength for the BC tissues was 447 nm, which was highly reflected versus the normal tissue. However, the most convenient one for the normal tissue was at 545 nm with high reflection versus the BC tissue. Finally, we implement a moving average filter for noise reduction and a custom K-means clustering algorithm on the selected two spectral images (447, 551 nm) to identify the various regions and effectively-identified spectral tissue variations with a sensitivity of 98.95%, and specificity of 98.44%. A pathologist later confirmed these outcomes as the ground truth for the tissue sample investigations. ConclusionsThe proposed system could help the surgeon and the pathologist identify the cancerous tissue margins from the non-cancerous tissue with a non-invasive, rapid, and minimum time method achieving high sensitivity up to 98.95%.

Deconstruction of Related Technologies of Ground Image Processing Based on High-Resolution Satellite Remote Sensing Images

The Earth observation system heavily relies on sophisticated remotely sensed satellites, an important means to obtain global high-precision geospatial products and an important strategic area for the world’s major scientific and technological powers to develop. Although China’s satellites currently have real-time or quasi-real-time observations with a high temporal resolution, there are still a lot of gaps between their positioning accuracy and the world’s advanced level. This essay aims to study an efficient ground image processing technology and apply it to high-resolution satellite remote sensing images. The convolutional neural network is an efficient deep learning method for image recognition and feature extraction. In this essay, people use a convolutional neural network (CNN) to identify ground images, use a support vector machine (SVM) to classify and summarize images, and then use a Kalman filter for noise reduction, so as to obtain sophisticated remotely sensed images. In the experiment, 100 satellite remote sensing images in the GeoImageDB database were selected for the simulation test, the images were divided into 5 types, and their recognition accuracy, classification accuracy, image signal-to-noise ratio, and resolution were analyzed. The results show that the accuracy of CNN’s recognition of different types of images is up to about 94%, and the lowest is about 85%. The accuracy of the SVM for image classification is above 80%, and the highest is about 95%. The SNR of the image after noise reduction is basically above 6.5, and some even reach above 8.0. The resolution of the image is basically above 800ppi, and the highest even reaches an ultra-high resolution of 1400ppi. Overall, the processed images are of high quality. This shows that this essay uses CNN for image recognition and then uses an SVM for classification, and finally, the method of denoising the image has certain feasibility and has achieved good results through experiments.

Kalman filter for noise reduction of Li-Ion cell discharge current*

Knowledge of the Kalman filter is very important in machine learning since is the basis for understanding more advanced concepts. Towards this end, control and estimation courses should assure the understanding of the concept and its correct application. A tutorial on the design, implementation and test of the KF to denoise the discharge current of a Li-ion cell is presented in this article. The students are also meant to acquire the discharge data used in the case study — Discharge of a Li-ion cell. The Battery Discharger Board is a low cost device to discharge Li-ion cells with a user programmable current discharge profile. The discharge is controlled and monitored by an external microcontroller connected to a host computer that stores and processes the discharge data. This board has been constructed to help students to gain insight into batteries. The current is measured by ACS712 Hall sensors, which are low cost but also very noisy. To de-noise the current measurements two different KF are used with the current being modelled as the state of a first order integrator. In the first approach, the KF assumes that the system is disturbed by process and measurement noises while in the second it only assumes measurement noise, The operation of the discharge board is illustrated in two experiments: (i) one with a constant discharge current and (ii) the other with a pulsed current. In both experiments, the filters performance was very good.

Energy-Efficient Sparsity-Driven Speech Enhancement in Wireless Acoustic Sensor Networks

Wireless acoustic sensor network (WASN) has shown a superiority over conventional microphone arrays in many aspects. There exists an important tradeoff between the performance and power consumption, as usually the sensors are power driven with a limited amount of battery resource. Given a prescribed performance bound, in literature sensor selection (SS) and rate allocation (RA) methods can be leveraged to optimize the energy efficiency. In this work, we propose a joint rate allocation and sensor selection (RASS) approach to simultaneously optimize the sensor subset and rate distribution, which is formulated by minimizing the total transmission power in terms of selection and bit-rate variables and constraining the residual noise power. It can be shown that under a set of linear constraints on beamforming, the linearly-constrained minimum variance (LCMV) beamformer is the optimal noise reduction filter. Based on this, the RASS reduces to a mixed semi-definite and bilinear programming problem, which is then solved using a two-step algorithm. As the selection and bit-rate unknowns are bilinear, we first consider to optimize their product, resulting in an upper bound of RASS. Then, we use McCormick envelopes to relax the bilinear constraint, resulting in a linear program. The final selection and bit-rate solutions are obtained by posterior randomized rounding. It can be shown that SS and RA are special cases of the proposed RASS. Numerical results using simulated WASNs validate the power efficiency of the proposed method as well as the robustness against dynamic factors.

EoG based Biopotential Instrumentation Amplifier

To examine the biomedical signals from the human body is of a challenging task. Signal acquisition of the biosignal needs proper design methodology with the estimation of consequences further. In this paper we discuss about the signal acquisition of EoG using the surface electrodes and to amplify the weak biopotential signal an amplifier is proposed with the additive noise reduction filter. The biopotential instrumentation amplifier is designed using standard operational amplifier that posses the high voltage swing. However the major artifacts present in the EoG signal acquisition is because of power line interference can be eliminated by notch filter.

Hybrid Features of Mask Generated with Gabor Filter for Texture Analysis and Sobel Operator for Image Regions Segmentation Using K-Means Technique

To make the image easily represented for more analysis and processing the segmentation procedure is required, where the image is portioned into its formed regions using some segmentation techniques based on features extraction. In this paper, a proposed procedure for finding the regions that formed the image is achieved based on hybrid features in two different components of different two colors spaces L*a*b* and RGB segmented by the k-means method. The hybrid features which comprise the mask segmentation are a combination of texture image characterization extracted by the Gabor filter and gradient image intensity by the Sobel operator after image quality enhancement by applying wiener filter noise reduction and contrast enhancement using Contrast limited adaptive equalization (CLAHE). Some statistical metrics are used for evaluating the performance of the proposed work stages.

Iterative Reconstruction: State-of-the-Art and Future Perspectives.

Image reconstruction processing in computed tomography (CT) has evolved tremendously since its creation, succeeding at optimizing radiation dose while maintaining adequate image quality. Computed tomography vendors have developed and implemented various technical advances, such as automatic noise reduction filters, automatic exposure control, and refined imaging reconstruction algorithms.Focusing on imaging reconstruction, filtered back-projection has represented the standard reconstruction algorithm for over 3 decades, obtaining adequate image quality at standard radiation dose exposures. To overcome filtered back-projection reconstruction flaws in low-dose CT data sets, advanced iterative reconstruction algorithms consisting of either backward projection or both backward and forward projections have been developed, with the goal to enable low-dose CT acquisitions with high image quality. Iterative reconstruction techniques play a key role in routine workflow implementation (eg, screening protocols, vascular and pediatric applications), in quantitative CT imaging applications, and in dose exposure limitation in oncologic patients.Therefore, this review aims to provide an overview of the technical principles and the main clinical application of iterative reconstruction algorithms, focusing on the strengths and weaknesses, in addition to integrating future perspectives in the new era of artificial intelligence.

Mid-Infrared Frequency Modulation Spectroscopy of NO Detection in a Hollow-Core Antiresonant Fiber

Mid-infrared frequency modulation spectroscopy (FMS) in a tellurite hollow-core antiresonant fiber (HC-ARF) is investigated for gas detection. The spectroscopic system is demonstrated for nitric oxide (NO) detection by exploiting its strong absorption line at 1900.08 cm−1 with a quantum cascade laser (QCL). By modulating the injection current of the QCL at 250 MHz and measuring NO in a 35 cm long HC-ARF, we achieve a noise equivalent concentration of 67 ppb at an averaging time of 0.1 s. Compared to direct absorption spectroscopy with a low-pass filter for etalon noise reduction, the FMS technique shows an improvement factor of 22. The detection limit of FMS can be further improved to 6 ppb at a longer averaging time of 100 s, corresponding to a noise equivalent absorption coefficient of 1.0 × 10−7 cm−1.

Cancer metastasis is related to normal tissue stemness.

The occurrence of cancer metastasis may be related to stem cells in normal tissues. We searched for patient IDs with both normal tissue stem cell values and TCGA (The Cancer Genome Atlas) clinical data for pairing and obtained 639 sets of data (stemness index of normal tissue, stemness index of tumor tissue, cancer stage, distant metastasis, tumor size) and invasion, and lymph node involvement). However, clinical data on cancer metastasis are of only four stages (e.g., Stage I, II, III, and IV), which cannot show subtle changes continuously. We need to find an effective data mining method to transform this four-valued clinical description into a numerical curve. We data-mine this data through numericalization, sorting, and noise reduction filtering. The results showed that: as the normal tissue stemness value (NS) increased, the tumor tissue stemness value (TS) increased proportionally (1.26 times NS). When NS >0.5, the rate of change in TS decelerated (0.43 times NS), and tumor metastasis began to occur. Clinical indicators, such as cancer stage, distant metastasis, tumor size and invasion, and lymph node involvement, showed that tumor metastasis became more and more severe with the increase of NS. This study suggests that tumor metastasis is triggered when the NS in the patient's body is more significant than 0.5.

Design and Optimization of Fuzzy-Based FIR Filters for Noise Reduction in ECG Signals Using Neural Network

Cardiovascular disease (CVD) has been identified as a threat to human life for decades, with the majority of individuals dying as a result of delayed diagnosis and treatment. An electrocardiogram (ECG) plays a vital role in the prognosis of such an ailment. The presence of noise and artifacts complicates the accurate detection and identification of CVD. As a result, reliable signal recovery tasks necessitate noise removal, which is an inverse problem. The main noises present in electrocardiogram (ECG) signals are EMG noise, electrode motion artifact noise. In this paper, radial basis function (RBF) and multi swarm optimization neural network (MSONN) are used to denoise the ECG signal. The cut-off frequency is calculated using a low-pass filter. By using, fuzzy FIR filtering technique baseline wander noises can be removed. Results show that MOS based approach outperforms existing approaches in terms of accuracy and is observed to be 87% even when the dataset size is small. Further, noises if any exists are also removed by the use of cascaded multiplier less Fuzzy FIR filters

Robust mean shift filter for mixed Gaussian and impulsive noise reduction in color digital images

Noise reduction is one of the most important topics of digital image processing and despite the fact that it has been studied for a long time it remains the subject of active research. In the following work, we present an extension of the Mean Shift technique, which is efficiently reducing the Gaussian noise, so that it is able to cope with the impulsive disturbances. Furthermore, the elaborated technique can be applied to enhance the images corrupted by a mixture of strong Gaussian and impulsive noise, severely decreasing the quality of color digital images. By means of our approach, which is based on a novel similarity measure between a pixel and a patch located in the center of the processing block, even heavily disturbed images can be effectively restored, which enables the success of further stages of the image processing pipeline. We evaluate the efficiency of the proposed method using a publicly available database of test color images and compare the restored images applying a set of standard quality metrics with the results delivered by state-of-the-art denoising methods. Additionally, we compare our method with the Medoid and Quick Shift techniques, accelerating the original Mean Shift algorithm, in terms of objective quality criteria and computational complexity. The results of the performed experiments indicate that the proposed technique is superior to the widely used denoising techniques and can be used as a robust extension of the Mean Shift procedure. In the paper, a particular emphasis is placed on the ability of the presented algorithm to preserve and enhance image edges. The performed experiments evaluated with the use of the Pratt’s index, quantitatively confirm the superiority of the proposed design over the Mean Shift and standard denoising methods. The preservation of edges and even their sharpening is a very important feature of our algorithm whereas the final goal is segmentation, playing a crucial role in various computer vision tasks. The proposed algorithm is intended for the mixed noise reduction in color images, but it can be also applied in multispectral imaging and clustering of multidimensional data. To enable the comparison of our method with the standard denoising techniques and to help applying it in other image processing fields, we made its code freely available.

The unsupervised pattern recognition for the ECG signal features detection

Smart devices and cloud-based technologies allow continuous non-invasive bio-signal recordings in daily life activities. There are many programs and applications that allow monitoring health condition and sharing that information with other people. However, bio-signals that are recorded in real nonstationary conditions are highly contaminated with noise that depends on various activities. Improper signal processing algorithms may lead to a faulty diagnosis or inaccurate decision-making results. Movement contaminated bio-signals require adaptive filtering and feature extraction algorithms because low frequency trends are mostly unstable, and the noise may cause higher impulses than the signal itself. That is why ordinary ECG signal parameters extraction algorithms fail in real time signal processing. In this research signals were filtered using two different methods: Butterworth filter for the high frequency noise reduction and BEADS algorithm for the low frequency noise removal. A new ECG feature extraction algorithm was proposed that is based on the unsupervised MTEO algorithm together with additional local extremum search. The proposed algorithm was compared with other methods on the MIT-BIH database. The additional comparison was made with Pan-Tompkins and k-TEO algorithms on ECG signals that were recorded in movement. Several examples are presented that show how each algorithm performs during training sessions with various intensity levels. The suggested method performed in linear time complexity that made it sufficient for the real time data processing. The obtained ECG parameter values could be used for diagnostics and fatigue recognition in health monitoring processes.

Multireader image quality evaluation of dynamic myocardial computed tomography perfusion imaging with a novel four-dimensional noise reduction filter.

Dynamic myocardial computed tomography perfusion (CTP) is a novel technique able to depict cardiac ischemia. To evaluate the impact of a four-dimensional noise reduction filter (similarity filter [4D-SF]) on image quality in dynamic CTP imaging, allowing for substantial radiation dose reduction. Dynamic CTP datasets of 30 patients (16 women) with suspected coronary artery disease, acquired with a 320-slice CT system, were retrieved, reconstructed with the deep learning-based algorithm of the system (DLR), and filtered with the 4D-SF. For each case, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) in six regions of interest (33-38mm2) were calculated before and after filtering, in four-chamber and short-axis views, and t-tested. Furthermore, six radiologists of different expertise evaluated subjective image preference by answering five visual grading analysis-type questions (regarding acceptable level of noise, absence of artifacts, natural appearance, cardiac contour sharpness, diagnostic acceptability) using a 5-point scale. The results were analyzed using visual grade characteristics (VGC) and intraclass correlation coefficient (ICC). Mean SNR in four-chamber view (unfiltered vs. filtered) were: septum=4.1 ± 2.1 versus 7.6 ± 5.6; lateral wall=4.5 ± 2.0 versus 8.0 ± 4.9; CNRseptum=16.6 ± 8.9 versus 31.7 ± 28; lateral wall=16.2 ± 8.9 versus 31.3 ± 28.9. Similar results were obtained in short-axis view. The perceived filtered image quality indicated decreased noise (VGCAUC=0.96) and artifacts (0.65), improved natural appearance (0.59), cardiac contour sharpness (0.74), and diagnostic acceptability (0.78). The inter-observer variability was excellent (ICC=0.79). All results were statistically significant (P < 0.05). Similarity filtering after DLR improves image quality, possibly enabling dose reduction in dynamic CTP imaging in patient with suspected chronic coronary syndrome.

Design hybrid filter technique for mixed noise reduction from synthetic aperture radar imagery

For military and civilian applications, synthetic aperture radar (SAR) imaging is an essential instrument for obtaining images of the Earth's surface. Speckle noise, a form of noise that is multiplicative, generated by conflicting echoes returned from each pixel, has a significant impact on the SAR picture. On SAR pictures, a hybrid filter for mixed noise reduction is used to remove the mixed noises that are present in the data during capture and transmission. Specifically, speckle noise and salt and pepper noises from SAR images. Both are being worked on at the same time to minimize mixed noise in SAR pictures without revealing edges or other features. This study proposes a technique that combines a hybrid filter derived from a statistics filters with nonlinear functions (SFNF). When comparing to mean, median as no adaptive filters, and frost filter, a lee filter, and fuzzy filters as adaptive filters, this hybrid filter produces good results. MATLAB was used to carry out the simulation. To illustrate the filtering technique's performance, quantitative measurements like signal to noise ratio (SNR) procedure, the mean square error (MSE) method, and edge measurement (β) mechanization are used.

Compressive-sensing swept-source optical coherence tomography angiography with reduced noise.

Optical coherence tomography angiography (OCTA), as a functional extension of optical coherence tomography (OCT), has exhibited a great potential to aid in clinical diagnostics. Currently, OCTA still suffers from motion artifact and noise. Therefore, in this article, we propose to implement compressive sensing (CS) on B-scans to reduce motion artifact by increasing B-scan rate. Meanwhile, a noise reduction filter is specially designed by combining CS, Gaussian filter and median filter. Specially, CS filtering is realized by averaging multiple CS repetitions on en-face OCTA images with varied sampling functions. The method is evaluated on in vivo OCTA images of human skin. The results show that vasculature structures can be reconstructed well through CS on B-scans with a sampling rate of 70%. Moreover, the noise can be significantly eliminated by the developed filter. This implies that our method has a good potential to expedite OCTA imaging and improve the image quality.