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The identification of intercepted radio fuze modulation types is a prerequisite for decision-making in interference systems. However, the electromagnetic environment of modern battlefields is complex, and the signal-to-noise ratio (SNR) of such environments is usually low, which makes it difficult to implement accurate recognition of radio fuzes. To solve the above problem, a radio fuze automatic modulation recognition (AMR) method for low-SNR environments is proposed. First, an adaptive denoising algorithm based on data rearrangement and the two-dimensional (2D) fast Fourier transform (FFT) (DR2D) is used to reduce the noise of the intercepted radio fuze intermediate frequency (IF) signal. Then, the textural features of the denoised IF signal rearranged data matrix are extracted from the statistical indicator vectors of gray-level cooccurrence matrices (GLCMs), and support vector machines (SVMs) are used for classification. The DR2D-based adaptive denoising algorithm achieves an average correlation coefficient of more than 0.76 for ten fuze types under SNRs of -10 dB and above, which is higher than that of other typical algorithms. The trained SVM classification model achieves an average recognition accuracy of more than 96% on seven modulation types and recognition accuracies of more than 94% on each modulation type under SNRs of -12 dB and above, which represents a good AMR performance of radio fuzes under low SNRs. [ABSTRACT FROM AUTHOR]

Spaceborne photon-counting LiDAR is significantly affected by noise, and existing denoising algorithms cannot be universally adapted to different surface types and topographies under all observation conditions. Accordingly, a new denoising method is presented to extract signal photons adaptively. The method includes two steps. First, the local neighborhood radius is calculated according to photons' density, then the first-step denoising process is completed via photons’ curvature feature based on KNN search and covariance matrix. Second, the local photon filtering direction and threshold are obtained based on the first-step denoising results by RANSAC and elevation frequency histogram, and the local dense noise photons that the first-step cannot be identified are further eliminated. The following results are drawn: (1) experimental results on MATLAS with different topographies indicate that the average accuracy of second-step denoising exceeds 0.94, and the accuracy is effectively improves with the number of denoising times; (2) experiments on ICESat-2 under different observation conditions demonstrate that the algorithm can accurately identify signal photons in different surface types and topographies. Overall, the proposed algorithm has good adaptability and robustness for adaptive denoising of large-scale photons, and the denoising results can provide more reasonable and reliable data for sustainable urban development.

As a burgeoning medical imaging method based on hybrid fusion of light and ultrasound, photoacoustic imaging (PAI) has demonstrated high potential in various biomedical applications, especially in revealing the functional and molecular information to improve diagnostic accuracy. However, stemming from weak amplitude and unavoidable random noise, caused by limited laser power and severe attenuation in deep tissue imaging, PA signals are usually of low signal‐to‐noise ratio, and reconstructed PA images are of low quality. Despite that conventional Kalman filter (KF) can remove Gaussian noise in time domain, it lacks adaptability in real‐time estimation due to its fixed model. Moreover, KF‐based denoising algorithm has not been applied in PAI before. In this paper, we propose an adaptive modified KF (MKF) targeted at PAI denoising by tuning system noise matrix Q and measurement noise matrix R in the conventional KF model. Additionally, in order to compensate the signal skewing caused by MKF, we cascade the backward part of Rauch–Tung–Striebel smoother, which utilizes the newly determined Q. Finally, as a supplement, we add a commonly used differential filter to remove in‐band reflection artifacts. Experimental results using phantom and ex vivo colorectal tissue are provided to prove validity of the algorithm. [ABSTRACT FROM AUTHOR]

An adaptive denoising method based on 2D empirical mode decomposition (EMD) is proposed to improve the infrared image quality of inspection UNMANNED aerial vehicles (UAVs) and provide guarantee for improving the inspection level of distribution network. Through rapid adaptive two-dimensional empirical mode decomposition algorithm decomposition of a UAV collected for distribution network inspection original noise of infrared image, get more than the IMF component and the residual amount, a forecast noise dominated the IMF component parameters such as threshold value and the variance of noise, using the estimated parameters in combination with the optimal linear interpolation algorithm of noise threshold function of leading the IMF component implementation of threshold denoising. After the denoised IMF component is obtained, the denoised infrared image is obtained after reconstruction with the signal-dominated IMF component, and the adaptive denoising of the infrared image of the distribution network inspection UAV is realized. The experimental results show that the method in this paper can maintain the details of the image, improve the definition, significantly improve the visual effect, the overall denoising performance is stable and feasible, and ensure the quality inspection UAV to collect infrared images. [ABSTRACT FROM AUTHOR]

Spaceborne photon-counting LiDAR is significantly affected by noise, and existing denoising algorithms cannot be universally adapted to different surface types and topographies under all observation conditions. Accordingly, a new denoising method is presented to extract signal photons adaptively. The method includes two steps. First, the local neighborhood radius is calculated according to photons' density, then the first-step denoising process is completed via photons' curvature feature based on KNN search and covariance matrix. Second, the local photon filtering direction and threshold are obtained based on the first-step denoising results by RANSAC and elevation frequency histogram, and the local dense noise photons that the first-step cannot be identified are further eliminated. The following results are drawn: (1) experimental results on MATLAS with different topographies indicate that the average accuracy of second-step denoising exceeds 0.94, and the accuracy is effectively improves with the number of denoising times; (2) experiments on ICESat-2 under different observation conditions demonstrate that the algorithm can accurately identify signal photons in different surface types and topographies. Overall, the proposed algorithm has good adaptability and robustness for adaptive denoising of large-scale photons, and the denoising results can provide more reasonable and reliable data for sustainable urban development. [ABSTRACT FROM AUTHOR]

Noise is a definite degeneration of medical images that interferes with the diagnostic process in clinical medicine. Although many denoising algorithms have been developed to improve the visual quality of medical images, failure to noise adaptation has been identified as a critical limitation of many existing denoising algorithms. Therefore, the objective of this study is to conduct an in-depth review to investigate and classify the various self-adaptive approaches and techniques implemented in recent medical image denoising applications. The articles published from the year 2015 have been retrieved from the web of science core collection database focusing on four medical imaging modalities, such as radiography, magnetic resonance imaging, computed tomography, and ultrasound. The analysis of the applications has emphasized the unique algorithmic components used to achieve the self-adaptability in detailed. Moreover, the strengths and weaknesses of those applications have been reviewed according to the various adaptive denoising approaches. Finally, this review highlights the limitations of existing adaptive denoising algorithms and open research directions for further studies of the domain. [ABSTRACT FROM AUTHOR]

Underwater acoustic (UWA) communications systems suffer from a low signal-to-noise ratio (SNR) and a doubly selective channel, which are caused by many limitations, such as high propagation loss, slow propagation speed, and time-varying environmental factors. To overcome a low SNR, various diversity techniques are often adopted in UWA communications. However, such diversity can only be exploited with knowledge of the channel. Accordingly, channel estimation needs to be performed without obtaining the benefit of diversity. Moreover, accurate side information that can support a channel estimator (CE) is difficult to acquire at a low SNR under doubly selective channel. In this article, a novel CE based on an adaptive denoising is proposed for UWA orthogonal frequency-division multiplexing (OFDM) systems. The proposed method exploits two different types of pilot symbols. Channel impulse response (CIR) is estimated based on primary pilot symbols. By minimizing the squared error of the received secondary pilot symbols, a near-optimal denoising window is adaptively determined based on the channel length for the given CIR estimate. The proposed method does not require a priori information about channel statistics and SNR values. Analysis on the effect of denoising and the performance of the proposed denoising window estimator are also presented. Simulation and at-sea experiments verify that the proposed method has superior performance, compared with conventional CEs over diverse channel conditions. Complexity analysis shows that the proposed method is computationally efficient. Therefore, the proposed method is effective for real-time UWA OFDM systems under a harsh UWA channel with strong noise.

Featured Application: Smart Grid; Power Quality Monitoring Systems; Supervisory Control and Data Acquisition; Advanced Metering Infrastructure. Wavelet transform modulus (WTM) has been used to detect or localize transient signal discontinuities. A numerical analysis indicated that these power quality disturbance (PQD) events are extremely sensitive to the random phase offset due to shift-variant wavelet or wavelet packet characteristics, which have not been comprehensively discussed yet. In this paper, we define wavelet packet transform modulus (WPTM) and present a WPTM-based PQD feature detection that is robust to severe power signal channels including random phase offset and low signal-to-noise ratio (≤25 dB). The presented WPTM-based detection that exhibits an exponentially increased degrees of freedom (DoF) and has better correlation properties than existing WTM-based detection of a limited DoF (two or three). We then use a standard median filter to efficiently remove impulsive noise and add a threshold modification step to reduce the false edge detection rate under random phase offset conditions while maintaining a reasonable detection rate. The proposed scheme uses the majority voting-based indirect correlation or root-mean-square metric between wavelet packet coefficients, rather than the conventional wavelet denoising or correlation metric. For a reliable numerical analysis, the proposed scheme uses both double- and single-edge-based detection measures, and the results verify its superiority over the conventional wavelet-based, wavelet-correlation-based, or non-wavelet-based schemes. [ABSTRACT FROM AUTHOR]

ABSTRACT—Partial discharge (PD) diagnostic is an effective tool for condition monitoring of the high-voltage equipment that provides an updated status of the dielectric insulation of the components. Reliability of the diagnostics depends on the quality of the PD measurement techniques and the processing of the measured PD data. The online measured data suffer from various inaccuracies caused by external noise from various sources such as power electronic equipment, radio broadband signals and wireless communication, etc. Therefore, extraction of useful data from the on-site measurements is still a challenge. This article presents a discrete wavelet transform (DWT)-based adaptive denoising algorithm and evaluates its performance. Various decisive steps in applying DWT-based denoising on any signal, including selection of mother wavelet, number of levels in multiresolution decomposition and criteria for reconstruction of the denoised signals are taken by the proposed algorithm and vary from one signal to another without a human intervention. Hence, the proposed technique is adaptive. The proposed solution can enhance the accuracy of the PD diagnostic for HV power components. [ABSTRACT FROM AUTHOR]

Wavelet transform modulus (WTM) has been used to detect or localize transient signal discontinuities. A numerical analysis indicated that these power quality disturbance (PQD) events are extremely sensitive to the random phase offset due to shift-variant wavelet or wavelet packet characteristics, which have not been comprehensively discussed yet. In this paper, we define wavelet packet transform modulus (WPTM) and present a WPTM-based PQD feature detection that is robust to severe power signal channels including random phase offset and low signal-to-noise ratio (≤25 dB). The presented WPTM-based detection that exhibits an exponentially increased degrees of freedom (DoF) and has better correlation properties than existing WTM-based detection of a limited DoF (two or three). We then use a standard median filter to efficiently remove impulsive noise and add a threshold modification step to reduce the false edge detection rate under random phase offset conditions while maintaining a reasonable detection rate. The proposed scheme uses the majority voting-based indirect correlation or root-mean-square metric between wavelet packet coefficients, rather than the conventional wavelet denoising or correlation metric. For a reliable numerical analysis, the proposed scheme uses both double- and single-edge-based detection measures, and the results verify its superiority over the conventional wavelet-based, wavelet-correlation-based, or non-wavelet-based schemes.

In recent years, significant progress has been made in intelligent fault diagnosis algorithms for rolling bearings. However, their real industrial application performance is hindered by challenges related to noise and variable load conditions. To solve this problem, we proposed an adaptive denoising convolutional neural network (ADCNN) which integrates adaptive denoising units to remove noise while preserving sensitive fault features, eliminating the need for manual denoising function settings. In addition, we use Maximum Overlap Discrete Wavelet Packet Transform to separate out the interfering components of noisy signal. To further improve ADCNN's noise immunity, we adopt a strategy of gradually decreasing the number of channels and using large convolutional kernels. ADCNN was evaluated alongside the latest methods on two different datasets, and the results demonstrate that ADCNN outperforms other methods both accuracy and robustness. Therefore, our approach presents a promising solution for diagnosing mechanical systems in noisy environments. [ABSTRACT FROM AUTHOR]

Abstract: Noise, especially high‐level noise, is a severe problem in Raman spectral analysis. It smears informative Raman peaks, distorts spectral features, and therefore affects final analytical results, particularly in multivariate analysis, which is frequently used in Raman spectroscopy. This becomes even worse when it comes to optical Raman probe‐based biological applications due to limited acquisition time, laser power, and collection efficiency. Noise suppression is usually the first step in the preprocessing procedure of Raman spectral analysis. It is crucial to reduce noise effectively before performing further analysis. Discrete wavelet transform is a useful tool for noise reduction. However, it only provides limited and fixed filter banks, which may not be optimal for the data under investigation. In this paper, a novel adaptive denoising method based on lifting wavelet transform is presented for improving the signal‐to‐noise ratio for a Raman probe‐based system. It enables users to develop an infinite number of lifting schemes from a base wavelet, and with the help of genetic algorithm, the optimal one can be easily found. This method is examined by a set of simulated Raman spectra with various noise level and a set of experimental Raman spectra. Performance comparison with other commonly used denoising methods is made. The results indicate that the proposed method is able to remove noise effectively while retaining informative Raman peaks satisfactorily. [ABSTRACT FROM AUTHOR]

We study an adaptive anisotropic Huber functional-based image restoration scheme. Using a combination of L2-L1 regularization functions, an adaptive Huber functional-based energy minimization model provides denoising with edge preservation in noisy digital images. We study a convergent finite difference scheme based on continuous piecewise linear functions and use a variable splitting scheme, namely the Split Bregman (In: Goldstein and Osher, SIAM J Imaging Sci 2(2):323-343, 2009) algorithm, to obtain the discrete minimizer. Experimental results are given in image denoising and comparison with additive operator splitting, dual fixed point, and projected gradient schemes illustrates that the best convergence rates are obtained for our algorithm. [ABSTRACT FROM AUTHOR]

The heart sound (HS) is an important physiological signal of the human body and can provide valuable diagnostic information in the clinical auscultation. The HS signal, however, is often contaminated by noise and the noisy HS signal will cause adverse influence of making the diagnosis. In this paper, we proposed an adaptive denoising algorithm, named adaOGS denoising, based on the overlapping-group sparsity (OGS) of the first-order difference of the HS signal. Under the Bayesian framework, the adaOGS algorithm is derived and solved as an optimization problem with OGS regularization based on the majorization–minimization (MM) algorithm. Compared with the conventional wavelet method, the proposed algorithm has the advantage that it does not need the predefined base functions and can also be performed in an adaptive way according to the noise level. Moreover, the experimental results show that the proposed algorithm outperforms the conventional wavelet methods such as ‘db10’, ‘db5’, and ‘bior5.5’, for denoising the noisy HS signals in lower noise level. [ABSTRACT FROM AUTHOR]

Fourier ptychographic microscopy (FPM) is a recently developed wide-field and high-resolution (HR) imaging technique, reconstructing HR spectrum from a series of low-resolution (LR) images at different illumination angles. Although many significant progresses have been made in FPM in the past few years, imaging noise is still an inevitable problem, which could seriously distort the results recovered using the conventional Fourier ptychography approach without image preprocessing. Generally, before FPM reconstruction, a thresholding denoising method is usually employed to eliminate the noise. However, conventional thresholding denoising algorithms cannot differentiate useful signals from imaging noise effectively, thus these algorithms usually eliminate signals and noise simultaneously. Here we propose an adaptive denoising method for FPM, which takes advantage of the information redundancy in FPM to separate signal from noise during the recovery process without any pre-knowledge about the noise statistics. Simulation and experimental results are presented to evaluate the performance of the proposed method. It is demonstrated that this method can both improve the accuracy and robustness of FPM and relax the imaging performance requirement for implementing high-quality FPM reconstruction. [ABSTRACT FROM AUTHOR]

• Adaptive denoising is crucial for obtaining higher spatial resolution dMRI also within clinical scenarios. • Comparison of two innovative adaptive techniques, patch2self and Nlsam, on reference adult data at 3T and 7T. • Patch2Self denoising turns out to be particularly suitable for diffusion kurtosis imaging model especially at ultra-high field. • Accurate denoising is key in making variations of DKI measures with magnetic field more coherent. Choosing the most appropriate denoising method to improve the quality of diagnostic images maximally is key in pre-processing of diffusion MRI images. Recent advancements in acquisition and reconstruction techniques have questioned traditional noise estimation methods favoring adaptive denoising frameworks, circumventing the need to know a priori information that is hardly available in a clinical setting. In this observational study, we compared two innovative adaptive techniques sharing some features, Patch2Self and Nlsam, through application on reference adult data at 3T and 7T. The primary aim was identifying the most effective method in case of Diffusion Kurtosis Imaging (DKI) data - particularly susceptible to noise and signal fluctuations - at 3T and 7T fields. A side goal consisted of investigating the dependence of kurtosis metrics' variability with respect to the magnetic field on the adopted denoising methodology. For comparison purposes, we focused on qualitative and quantitative analysis of DKI data and related microstructural maps before and after applying the two denoising approaches. Specifically, we assessed computational efficiency, preservation of anatomical details via perceptual metrics, consistency of microstructure model fitting, alleviation of degeneracies in model estimation, and joint variability with varying field strength and denoising method. Accounting for all these factors, Patch2Self framework has turned out to be specifically suitable for DKI data, with improving performance at 7T. Nlsam method is more robust in alleviating degenerate black voxels while introducing some blurring, which in turn is reflected in an overall loss of image sharpness. Regarding the impact of denoising on field-dependent variability, both methods have been shown to make variations from standard to Ultra-High Field more concordant with theoretical evidence, claiming that kurtosis metrics are sensitive to susceptibility-induced background gradients, directly proportional to the magnetic field strength and sensitive to the microscopic distribution of iron and myelin. This study serves as a proof-of-concept stressing the need for an accurate choice of a denoising methodology, specifically tailored for the data under analysis and allowing higher spatial resolution acquisition within clinically compatible timings, with all the potential benefits that improving suboptimal quality of diagnostic images entails. [ABSTRACT FROM AUTHOR]

An adaptive approach is proposed to denoise and detect the underwater sonar image in this paper. Firstly, to improve the denoising performance of non-local spatial information in the underwater sonar image, an adaptive non-local spatial information denoising method based on the golden ratio is proposed. Then, a new adaptive cultural algorithm (NACA) is proposed to accurately and quickly complete the underwater sonar image detection in this paper. Concretely, NACA has two improvements. In the first place, to obtain better initial clustering centres, an adaptive initialization algorithm based on data field (AIA-DF) is proposed in this paper. Secondly, in the belief space of NACA, a new update strategy is adopted to update cultural individuals in terms of the quantum-inspired shuffled frog leaping algorithm (QSFLA). The experimental results show that the proposed denoising method in this paper can effectively remove relatively large and small filtering degree parameters and improve the denoising performance to some extent. Compared with other comparison algorithms, the proposed NACA can converge to the global optimal solution within small epochs and accurately complete the object detection, having better effectiveness and adaptability.

Image processing as an aid for underwater vision has been subject to intensified interest in the recent years. The major problem is that they are inherently affected by poor contrast and noise due to the attenuation of light, backscatter in the underwater environment and the quality of sensing elements in camera during acquisition. Image denoising as a preprocessing step is needed for extracting features and accurate object recognition. Adaptive filters are preferred because traditional techniques often result in excess smoothing and fail to preserve edges while removing noise. A fuzzy-based image denoising algorithm is proposed to retain edge information and as well remove noise for restoration of underwater images. The adaptive nature of the proposed algorithm was tested using varying degrees of Gaussian noise. Performance metrics like peak signal noise ratio, normalized mean square error and mean structural similarity index were used for evaluation. Experimental results show the proposed method can remove varying levels of Gaussian noise better than the traditional filters while still preserving 27% edges. [ABSTRACT FROM AUTHOR]

Existing denoising algorithms based on a simplified signal-dependent noise model are valid under the assumption of the predefined parameters. Consequently, these methods fail if the predefined conditions are not satisfied. An adaptive method for eliminating random noise from the simplified signal-dependent noise model is presented in this paper. A linear mapping function between multiplicative noise and noiseless image data is established using the Maclaurin formula. Through demonstrations of the cross-correlation between random variables and independent random variable functions, the mapping function between the variances of multiplicative noise and noiseless image data is acquired. Accordingly, the adaptive denoising model of simplified signal-dependent noise in the wavelet domain is built. The experimental results confirm that the proposed method outperforms conventional ones. [ABSTRACT FROM AUTHOR]

Raman data with low signal-to-noise ratio (SNR) are often obtained in the process of rapid or low-power acquisition for biological samples. The noise removal out of a biological Raman spectrum is laborious owing to numerous and superimposed Raman peaks, and it often takes a lot of time to find the optimal preproceed parameters. In this study, an adaptive denoising method for biological Raman data has been proposed, using three comparisons and a quantitative prediction model to show its simplicity and effectiveness. In this work, we decomposed Raman spectra of human skin and rice leaf on Hilbert vibration decomposition (HVD) method, to obtain a series of initial values, for example as peak position, intensity and full width at half maximum (FWHM), then fitted Raman spectrum according the Vogit function until the indicator function F reached the minimum value. The SNR and the root mean square error（ R M S E) of HVD denoised spectrum were 57.5864 dB and 0.0108, and were both better than those of preproceed data (35.1826 dB, 0.0337; 32.4120 dB, 0.0352; 38.0685 dB, 0.0226) by Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD), and Local Complementary Ensemble Empirical Mode Decomposition (LCEMD), compared with adaptive denoising methods, EMD, EEMD and LCEEMD, the improved algorithm had the advantages of no modal aliasing, no noise residue and independence of normalized permutation entropy. Then HVD denoised data of rice leaf samples were compressed by principal component analysis (PCA) and used for chlorophyll content prediction model. The correlation coefficient and standard error for prediction set of HVD +PCA model were 0.856 and 5.38, and were also better than those of non-adaptive preproceed data models (Mobing Average Smoothing, Gaussian Filter Smoothing, Median Filter Smoothing, Savitzky-Golay Smoothing and Wavelet Transform Denoising). The final experimental comparison showed that the HVD denoising method can also be applied to the Raman spectrum noise removal of inorganic samples, but better performances had been shown in the noise removal of complex biological Raman spectra. Accurate denoising of biological Raman spectrum is of great significance for accurate quantitative prediction and qualitative analysis, and no prior setting of the preproceed parameters makes the HVD denoising method easy to operate. [ABSTRACT FROM AUTHOR]

[Display omitted] • WPD-RLSVFF is an effective adaptive filtering and denoising method. • The parameters of WPD can be automatically selected by margin and power indicators. • Experimental method based on SLD helps to improve the efficiency of data collection. • Optimal WPN is not necessarily the maximum power entropy among all nodes. • Power entropy decreases with increasing chatter severity. Unstable cutting has a relatively large negative impact on the machining quality and efficiency of thin-walled parts. It can easily cause severe vibration of milling systems, resulting in poor surface roughness of workpieces. Although some promising signal processing methods, such as wavelet packet decomposition (WPD), have been applied to process nonlinear signals, few studies have paid attention to determining a wavelet basis and the number of decomposition layers in WPD. Parameters of WPD play a crucial role and they are often empirically determined. In this paper, an adaptive denoising model based on WPD and recursive least squares with a variable forgetting factor (RLSVFF) is firstly established, and the influence of the forgetting factor in the model on signal denoising are investigated. Then, a novel chatter detection method based on multi-source signals fusion using WPD and power entropy is presented. On this basis, an automated selection method based on a margin indicator and power is proposed for applications to WPD. And the influence of different parameters of WPD on a margin indicator and power are investigated. In order to improve the efficiency of different levels of signal acquisition, a method based on a stability lobe diagram (SLD) is used to design experimental parameters. Compared with traditional denoising models and chatter detection methods based on a single signal, simulation and experimental results show that the adaptive denoising model and chatter detection method based on multi-source signals fusion proposed in this paper can more reliably detect the occurrence of early chatter and different levels of chatter. [ABSTRACT FROM AUTHOR]

Radar backscattering from human objects is subject to micro-Doppler modulations because of their flexible body articulations and complicated movement patterns, which can help identify the interested targets and provide valuable information about their motion dynamics. In this paper, a novel theoretical method to extract target micro-Doppler trajectories from continuous-wave radar echo is proposed with a united application of a modified high-order ambiguity function and an adaptive denoising technology. Through this method, multiple components corresponding to different target scattering parts and their micro-Doppler trajectories can be accurately extracted and estimated even in a time-varying low signal-to-noise ratio environment. Finally, a series of simulations is conducted to illustrate the validity and performance of the proposed techniques. [ABSTRACT FROM PUBLISHER]

Purpose: To develop and evaluate an image-domain noise reduction method based on a modified nonlocal means (NLM) algorithm that is adaptive to local noise level of CT images and to implement this method in a time frame consistent with clinical workflow. Methods: A computationally efficient technique for local noise estimation directly from CT images was developed. A forward projection, based on a 2D fan-beam approximation, was used to generate the projection data, with a noise model incorporating the effects of the bowtie filter and automatic exposure control. The noise propagation from projection data to images was analytically derived. The analytical noise map was validated using repeated scans of a phantom. A 3D NLM denoising algorithm was modified to adapt its denoising strength locally based on this noise map. The performance of this adaptive NLM filter was evaluated in phantom studies in terms of in-plane and cross-plane high-contrast spatial resolution, noise power spectrum (NPS), subjective low-contrast spatial resolution using the American College of Radiology (ACR) accreditation phantom, and objective low-contrast spatial resolution using a channelized Hotelling model observer (CHO). Graphical processing units (GPU) implementation of this noise map calculation and the adaptive NLM filtering were developed to meet demands of clinical workflow. Adaptive NLM was piloted on lower dose scans in clinical practice. Results: The local noise level estimation matches the noise distribution determined from multiple repetitive scans of a phantom, demonstrated by small variations in the ratio map between the analytical noise map and the one calculated from repeated scans. The phantom studies demonstrated that the adaptive NLM filter can reduce noise substantially without degrading the high-contrast spatial resolution, as illustrated by modulation transfer function and slice sensitivity profile results. The NPS results show that adaptive NLM denoising preserves the shape and peak frequency of the noise power spectrum better than commercial smoothing kernels, and indicate that the spatial resolution at low contrast levels is not significantly degraded. Both the subjective evaluation using the ACR phantom and the objective evaluation on a low-contrast detection task using a CHO model observer demonstrate an improvement on low-contrast performance. The GPU implementation can process and transfer 300 slice images within 5 min. On patient data, the adaptive NLM algorithm provides more effective denoising of CT data throughout a volume than standard NLM, and may allow significant lowering of radiation dose. After a two week pilot study of lower dose CT urography and CT enterography exams, both GI and GU radiology groups elected to proceed with permanent implementation of adaptive NLM in their GI and GU CT practices. Conclusions: This work describes and validates a computationally efficient technique for noise map estimation directly from CT images, and an adaptive NLM filtering based on this noise map, on phantom and patient data. Both the noise map calculation and the adaptive NLM filtering can be performed in times that allow integration with clinical workflow. The adaptive NLM algorithm provides effective denoising of CT data throughout a volume, and may allow significant lowering of radiation dose. [ABSTRACT FROM AUTHOR]

Image denoising is a relevant issue found in diverse image processing and computer vision problems. It is a challenge to preserve important features, such as edges, corners and other sharp structures, during the denoising process. Wavelet transforms have been widely used for image denoising since they provide a suitable basis for separating noisy signal from the image signal. This paper describes a novel image denoising method based on wavelet transforms to preserve edges. The decomposition is performed by dividing the image into a set of blocks and transforming the data into the wavelet domain. An adaptive thresholding scheme based on edge strength is used to effectively reduce noise while preserving important features of the original image. Experimental results, compared to other approaches, demonstrate that the proposed method is suitable for different classes of images contaminated by Gaussian noise. [ABSTRACT FROM AUTHOR]

Abstract: Lowering the cumulative radiation dose to a patient undergoing fluoroscopic examination requires efficient denoising algorithms. We propose a method, which extensively utilizes temporal dimension in order to maximize denoising efficiency. A set of subsequent images is processed and two estimates of denoised images are calculated. One is based on a special implementation of an adaptive edge preserving wavelet transform, while the other is based on the statistical method intersection of confidence intervals (ICI) rule. Wavelet transform is thought to produce high quality denoised images and ICI estimate can be used to further improve denoising performance about object edges. The estimates are fused to produce the final denoised image. We show that the proposed method performs very well and do not suffer from blurring in clinically important parts of images. As a result, its application could allow for significant lowering of the fluoroscope single frame dose. [Copyright &y& Elsevier]

Single-sensor digital color cameras use a process called color demosaicking to produce full color images from the data captured by a color filter array (CFA). The quality of demosaicked images is degraded due to the sensor noise introduced during the image acquisition process. The conventional solution to combating CFA sensor noise is demosaicking first, followed by a separate denoising processing. This strategy will generate many noise-caused color artifacts in the demosaicking process, which are hard to remove in the denoising process. Few denoising schemes that work directly on the CFA images have been presented because of the difficulties arisen from the red, green and blue interlaced mosaic pattern, yet a well designed "denoising first and demosaicking later" scheme can have advantages such as less noise-caused color artifacts and cost-effective implementation. This paper presents a principle component analysis (PCA) based spatially-adaptive denoising algorithm, which works directly on the CFA data using a supporting window to analyze the local image statistics. By exploiting the spatial and spectral correlations existed in the CFA image, the proposed method can effectively suppress noise while preserving color edges and details. Experiments using both simulated and real CFA images indicate that the proposed scheme outperforms many existing approaches, including those sophisticated demosaicking and denoising schemes, in terms of both objective measurement and visual evaluation. [ABSTRACT FROM AUTHOR]

Wavelet based denoising is an effective way to improve the quality of images. Various methods have been proposed for denoising using real-valued wavelet transform. Complex valued wavelets exist but are rarely used. The complex wavelet transform provides phase information and it is shift invariant in nature. In medical image denoising, both removal of phase incoherency as well as maintaining the phase coherency are needed. This paper is an attempt to explore and apply the complex Daubechies wavelet transform for medical image denoising. We have proposed a method to compute a complex threshold, which does not depend on any assumed model of noise. In this sense this is a "universal" method. The proposed complex-domain shrinkage function depends on mean, variance and median of wavelet coefficients. To test the effectiveness of the proposed method, we have computed the input and output SNR and PSNR of various types of medical images. The method gives an improvement for Gaussian additive, Speckle and Salt-&-Pepper noise as well as for the mixture of these noise types for a range of noisy images with 15 db to 30 db noise levels and outperforms other real-valued wavelet transform based methods. The application of the proposed method to Ultrasound, X-ray and MRI images is demonstrated in the experiments. [ABSTRACT FROM AUTHOR]

• An evaluator of damage localization accuracy is proposed for adaptive denoising. • Robust principal component analysis is harnessed to extract local damage features. • Hierarchical clustering is used to evaluate the number and size of damage zones. • Damage features in different sources are integrated for robust damage localization. Structural damage localization by using full-field vibration measurements is inevitably contaminated by measurement noise and not robust for multi-damage cases. To overcome these problems, a novel robust multi-damage localization method is proposed based on adaptive denoising and data fusion. The major contributions are in three aspects. Firstly, an evaluator of multi-damage localization performance is proposed, which converts the damage localization into an optimization problem. Secondly, a hierarchical clustering is adopted to evaluate the damage zones by examining spatial characteristics of the damage. Thirdly, a data fusion strategy is developed based on the assessment of damage localization performance, which guarantees providing robust multi-damage localization results. In addition, numerical and experimental studies of multi-damaged plates are conducted to validate the feasibility and effectiveness of the proposed method. It is found that the accuracy of the multi-damage localization is significantly enhanced by optimizing the process of damage feature extraction and data fusion. [ABSTRACT FROM AUTHOR]

Bai, Y.-X. and Yu, Y., 2020. Local adaptive denoising of phytoplankton image based on minimum bayes risk. In: Qiu, Y.; Zhu, H., and Fang, X. (eds.), Current Advancements in Marine and Coastal Research for Technological and Sociological Applications. Journal of Coastal Research, Special Issue No. 107, pp. 29-32. Coconut Creek (Florida), ISSN 0749-0208. Marine phytoplankton is an important marine biological resource. We can classify and identify marine phytoplankton, which is classified and quantitatively analyzed under the microscope. However, denoising is an important part of image processing. By analyzing the histogram of noisy image, we can make adaptive denoising according to Bayes decision of minimum error rate. Through the scanning system of microscope and computer, we can form a set of adaptive desiccation of phytoplankton, which is of great significance for the identification of phytoplankton species. Through the improvement of image acquisition, image processing and feature selection, we can automatically identify marine phytoplankton, which will provide a new real-time, fast and efficient detection platform for marine ecological environment monitoring. Firstly, this paper analyzes the minimum Bayesian risk. Then, this paper demonstrated the adaptive desiccation of phytoplankton. Finally, some suggestions are put forward. [ABSTRACT FROM AUTHOR]