1588 results about "De noise" patented technology

A method and apparatus for de-noising weak bio-signals having a relatively low signal to noise ratio utilizes an iterative process of de-noising a data set comprised of a new set of frames. The method separately performs a non-linear de-noising operation on each of the component frames and combines the resultant de-noised frames to form a combined resultant de-noised input signal. The method is preferably carried out in a digital processor.

A method and apparatus for de-noising weak bio-signals having a relatively low signal to noise ratio utilizes an iterative process of wavelet de-noising a data set comprised of a new set of frames of wavelet coefficients partially generated through a cyclic shift algorithm. The method preferably operates on a data set having 2^N frames, and the iteration is performed N−1 times. The resultant wavelet coefficients are then linearly averaged and an inverse discrete wavelet transform is performed to arrive at the de-noised original signal. The method is preferably carried out in a digital processor.

The invention provides a binocular camera-based panoramic image splicing method. According to the method, a binocular camera is arranged at a certain point of view in the space, the binocular camera completes photographing for once and obtains two fisheye images; a traditional algorithm is improved according to the defect of insufficient distortion correction capacity of a latitude-longitude correction method in a horizontal direction; corrected images are projected into the same coordinate system through using a spherical surface orthographic projection method, so that the fast correction of the fisheye images can be realized; feature points in an overlapping area of the two projected images are extracted based on an SIFT feature point detection method; the search strategy of a K-D tree is adopted to search Euclidean nearest neighbor distances of the feature points, so that feature point matching can be performed; an RANSAC (random sample consensus) algorithm is used to perform de-noising on the feature points and eliminate mismatching points, so that image splicing can be completed; and a linear fusion method is adopted to fuse spliced images, and therefore, color and scene change bluntness in an image transition area can be avoided.

A temperature compensation method for denoising a fiber-optic gyroscope on the basis of time series analysis comprises four steps of: step 1, designing an experimental scheme, performing fixed point low and high temperature testing experiment on the fiber-optic gyroscope, and utilizing acquisition software for data acquisition; step 2, performing time series analysis on the zero offset data of the gyroscope, and establishing the mathematical model of the random error of the fiber-optic gyroscope; step 3, adopting a kalman filtering algorithm to filter random noise in the zero offset data of the fiber-optic gyroscope; and step 4, utilizing the data which is de-noised by the kalman filtering to identify the model structure of the temperature shift error of the fiber-optic gyroscope, and calculating the parameters of the identified model. The method establishes the multinomial model of the static temperature shift error of the fiber-optic gyroscope through time series analysis, kalman filtering denoising treatment and identification of the temperature shift error model structure and parameters. The method completely meets the real-time compensation requirement on the project, and has a better practicable value and a wide application prospect in the technical field of aerospace navigation.

Apparatuses and methods for extracting, de-noising, and analyzing electrocardiogram signals. Any of the apparatuses described herein may be implemented as a (or as part of a) computerized system. For example, described herein are apparatuses and methods of using them or performing the methods, for extracting and/or de-noising ECG signals from a starting signal. Also described herein are apparatuses and methods for analyzing an ECG signal, for example, to generate one or more indicators or markers of cardiac fitness, including in particular indicators of atrial fibrillation. Described herein are apparatuses and method for determining if a patient is experiencing a cardiac event, such as an arrhythmia.

A noise suppression circuit for a communications channel (12) comprises a noise reference extraction device (14), for example a hybrid transformer or circuit, for extracting from an input signal (S) a reference signal (NCM) corresponding to a noise component in the input signal and supplying the noise reference signal to a noise estimation unit (16) which derives therefrom a noise estimate (Yj) which is subtracted from the input signal to produce a noise-suppressed output signal (DOUT). The noise suppression circuit comprises a first analog-to-digital converter (24) for digitizing the input signal at a first sampling rate (Fs) and a second analog-to-digital converter for sampling the noise reference signal (NCM) at a second, lower sampling rate (FS/M), the ratio (M) between the two sampling rates being an integer. A decimator (40) decimates the input signal to produce a decimated signal (Dj+Nj). An adaptive filter (34) produces a noise estimate signal (Y'j) that is subtracted from the decimated signal to produce an error signal (epsij) which is used by adaptive filter (34) to adjust its coefficients. An interpolator (36) interpolates the interim noise estimate signal (Y'j) by the same integer (M) to provide a noise estimate signal (Yj) which is subtracted from a digitized and delayed version of the input signal to produce the noise-suppressed output signal (DOUT).

The invention discloses a method for acquiring and predicting road traffic flow. According to the method, an annular sensing coil, a vehicle detection module, a traffic flow acquisition module and traffic flow data pre-processing and prediction software are adopted. The road traffic flow data pre-processing and prediction software runs in an upper computer (a personal computer (PC)) and is used for reading traffic flow data in the traffic flow acquisition module (a security digital (SD) card) through a network interface. In order to improve the reliability of prediction, a method for pre-processing and predicting the road traffic flow data comprises the following steps of: firstly, de-noising the traffic flow data by using a wavelet analysis method and a least square method; and secondly, establishing a traffic flow prediction model by using an improved back propagation (BP) neural network, so that prediction of the traffic flow is realized, and reference is provided for a control timing scheme for road traffic optimization and road traffic planning. The method has the advantages that: road traffic parameters such as vehicle flow, average vehicle speed, occupancy and traffic density in a rated period can be obtained, so that the prediction of the road traffic flow is realized, and the accuracy of data acquisition and the accuracy of prediction of the road traffic flow are improved.

The subject invention pertains to a method and apparatus for producing sensitivity maps with respect to medical imaging. The subject invention relates to a method for applying an inpainting model to correct images in parallel imaging. Some images, such as coil sensitivity maps and intensity correction maps, have no signal at some places and may have noise. Advantageously, the subject invention allows an accurate method to fill in holes in sensitivity maps, where holes can arise when, for example, the pixel intensity magnitudes for two images being used to create the sensitivity map are zero. A specific embodiment of the subject invention can accomplish de-noise, interpolation, and extrapolation simultaneously for these types of maps such that the local texture can be carefully protected.

The invention discloses a high-speed weak target flight path detection method of an image field, relating to the field of moving object tracking, and mainly solving the problem of extracting a moving object flight path and a target kinematic parameter from data with a low signal-to-noise ratio. The detection process is as follows: firstly, obtaining a time-Doppler image by mixing and accumulating target multiframe data; carrying out edge extracting on the basis of phase equalization model; carrying out skeletonization and binary image de-noising treatment on the edge detection result with a morphology method to realize edge center positioning as well as noise inhibition; and finally, using a least square method to detect the target flight path and estimate a target parameter. The invention can accurately estimate the advantages of a curve flight path parameter, and can be used for the data processing field for monitoring systems, such as radars and the like to realize high-speed weak target detection and tracking.

The invention discloses a partial echo compressed sensing-based quick magnetic resonance imaging (MRI) method. The conventional imaging method has low speed and high hardware cost. The method comprises the following steps of: acquiring echo data of a random variable density part, namely intensively acquiring data in a central area of a k-space and acquiring the data around the k-space randomly and sparsely to generate a two-dimensional random mask, adding the two-dimensional random mask into every data point which needs to be acquired on a frequency coding shaft to form a three-dimensional random mask, and acquiring the data of the k-space according to the generated three-dimensional random mask; re-establishing by projection onto convex sets based on a wavelet domain which is de-noised by soft thresholding; and nonlinearly re-establishing a minimum L1 normal number based on finite difference transformation, namely sparsely transforming an image space signal x, determining an optimization objective and solving the optimization objective. By the method of the invention, partial echo technology and compressed sensing technology are combined and applied to data acquisition of MRI, sothat echo time is shortened, and data acquisition time is shortened at the same time.

The invention relates to an electromyographic signal gait recognition method for optimizing a support vector machine based on a genetic algorithm. According to the electromyographic signal gait recognition method, the penalty parameter and the kernel function parameter of the support vector machine are optimized with the genetic algorithm, the performance of the support vector machine is accordingly optimized, and the efficiency and the accuracy of the support vector machine for recognizing lower limb movement gaits based on electromyographic signals are improved. The electromyographic signal gait recognition method includes the steps of firstly, carrying out de-noising processing on the collected lower limb electromyographic signals with a wavelet modulus maximum de-noising method; secondly, extracting the time domain characteristics of the de-noised electromyographic signals to form characteristic samples; thirdly, optimizing parameters of the support vector machine with the genetic algorithm to obtain a set of optimal parameters with the minimum errors, and constructing a classifier through the parameters; finally, inputting a characteristic sample set into the optimized classifier for gait recognition. The electromyographic signal gait recognition method is easy to operate, rapid in calculation and high in recognition rate, and has the application value and the broad prospects in the human body lower limb gait recognition field.

A method includes estimating structure models for a voxel(s) of a spectral image based on a noise model, fitting structure models to a 3D neighborhood about the voxel(s), selecting one of the structure models for the voxel(s) based on the fittings and predetermined model selection criteria, and de-noising the voxel(s) based on the selected structure model, producing a set of de-noised spectral images. Another method includes generating a virtual contrast enhanced intermediate image for each energy image of a set of spectral images corresponding to different energy ranges based on de-noised spectral images, decomposed de-noised spectral images, an iodine map, and a contrast enhancement factor; and generating final virtual contrast enhanced images by incorporating a simulated partial volume effect with the intermediate virtual contrast enhanced images. Also described herein are approaches for generating a virtual non-contrasted image, a bone and calcification segmentation map, and an iodine map for multi-energy imaging studies.

The invention provides an optical fiber vibration identification system based on a phi-OTDR technology and an optical fiber vibration identification method thereof. The monitoring distance of the system is greatly enhanced through a dual-path detection structure; the adaptability of the system for environmental noise changes is enhanced by the method of characteristic threshold dynamic updating, and a vibration event is accurately positioned; background noise in signals can be greatly reduced through spectral subtraction noise reduction under the condition of maintaining the signal characteristic and energy of the vibration signals, and the signal-to-noise ratio of the signals and the sensitivity of system detection can be enhanced; and multi-characteristic parameter mode identification is performed on the vibration signals from the time domain and the wavelet domain so that the influence of other complex time-dependent interference noise can be effectively avoided, the correct rate of vibration event detection and vibration type classification can be enhanced, the false alarm rate of the system can be reduced, the detection performance of a vibration detection system based on the OTDR technology in the actual complex noise environment can be enhanced, and the national major project application requirements in the aspects of boundary safety and long-distance pipeline safety can be met.

The invention relates to a road network grid matching method, which comprises: obtaining GPS original point data and de-noising the GPS original point data to gain GPS point data; partitioning the road network into grids according to the GIS road network structure information to get road network grid units; and matching the GPS point data to the corresponding road network grid units to obtain grid matching data. The invention also relates to an information acquisition method for real-time road conditions, which comprises: matching road network grids to get grid matching data; integrating the grid matching data with GPS traffic flow data; and filtering the integrated data according to types of driving routes to obtain the information of real-time road conditions. The invention provides theroad network grid matching method based on the GPS road network grid partitioning technique aiming at large concurrent GPS data for achieving real-time treatment of the large concurrent GPS data; andacquires information of real-time road conditions and real-time guiding routes by using the road network grid matching method.

The invention provides a bivariate nonlocal average filtering de-noising method for an X-ray image. The method is characterized by comprising the following steps: 1) a selecting method of a fuzzy de-noising window; and 2) a bivariate fuzzy adaptive nonlocal average filtering algorithm. The method has the beneficial effects that in order to preferably remove the influence caused by the unknown quantum noise existing in an industrial X-ray scan image, the invention provides the bivariate nonlocal fuzzy adaptive non-linear average filtering de-noising method for the X-ray image, in the method, a quantum noise model which is hard to process is converted into a common white gaussian noise model, the size of a window of a filter is selected by virtue of fuzzy computation, and a relevant weight matrix enabling an error function to be minimum is searched. A particle swarm optimization filtering parameter is introduced in the method, so that the weight matrix can be locally rebuilt, the influence of the local relevancy on the sample data can be reduced, the algorithm convergence rate can be improved, and the de-noising speed and precision for the industrial X-ray scan image can be improved, so that the method is suitable for processing the X-ray scan image with an uncertain noise model.

The present invention discloses a three-dimensional reconstruction method and system capable of maintaining sharp features. The method includes the following steps that: 1) a two-dimensional image filtering method extended to a three-dimensional space is adopted to perform smoothing de-noising on inputted roughly-registered point cloud; 2) an improved region growth method is adopted to perform outlier removal on the smoothed roughly-registered point cloud; 3) a kd-tree (k-dimensional tree) acceleration-based ICP (iterative closest point) algorithm is adopted to perform precise registration on the outlier-removed roughly-registered point cloud; 4) a neighborhood search and boundary point detection-based fusion method is adopted to fuse the precisely-registered point cloud; and 5) a feature point detection and adaptive step size update-based method is adopted to perform surface reconstruction on the fused precisely-registered point cloud. The three-dimensional reconstruction system is composed of a point cloud preprocessing module, a point cloud combining module and a surface reconstruction module. The three-dimensional reconstruction system which is realized based on the method of the invention can maintain the sharp features of the edge of a reconstructed model, and therefore, reconstruction speed is considered with accuracy ensured.

The invention discloses a gastrointestinal tumor microscopic hyper-spectral image processing method based on a convolutional neural network, comprising the following steps: reducing and de-noising the spectral dimension of an acquired gastrointestinal tissue hyper-spectral training image; constructing a convolutional neural network structure; and inputting obtained hyper-spectral data principal components (namely, a plurality of 2D gray images, which are equivalent to a plurality of feature maps of an input layer) as input images into the constructed convolutional neural network structure using a batch processing method, and by taking a cross entropy function as a loss function and using an error back propagation algorithm, training the parameters in the convolutional neural network and the parameters of a logistic regression layer according to the average loss function in a training batch until the network converges. According to the invention, the dimension of a hyper-spectral image is reduced using a principal component analysis method, enough spectral information and spatial texture information are retained, the complexity of the algorithm is reduced greatly, and the efficiency of the algorithm is improved.

Current methods of embedding hidden data in an image inevitably distort the original image by noise. This distortion cannot generally be removed completely because of quantization, bit-replacement, or truncation at the grayscales 0 and 255. The distortion, though often small, may make the original image unacceptable for medical applications, or for military and law enforcement applications where an image must be inspected under unusual viewing conditions (e.g., after filtering or extreme zoom). The present invention provides high-capacity embedding of data that is lossless (or distortion-free) because, after embedded information is extracted from a cover image, we revert to an exact copy of the original image before the embedding took place. This new technique is a powerful tool for a variety of tasks, including lossless robust watermarking, lossless authentication with fragile watermarks, and steganalysis. The technique is applicable to raw, uncompressed formats (e.g., BMP, PCX, PGM, RAS, etc.), lossy image formats (JPEG, JPEG2000, wavelet), and palette formats (GIF, PNG).

Raw cone beam tomography projection image data are taken from an object and are denoised by a wavelet domain denoising technique and at least one other denoising technique such as a digital reconstruction filter. The denoised projection image data are then reconstructed into the final tomography image using a cone beam reconstruction algorithm, such as Feldkamp's algorithm.

The invention discloses a method for extracting a Rayleigh surface wave frequency dispersion curve. The method comprises the following steps: a, performing Fourier transform on noise cross-correlation signals recorded by a seismometer pair which are spaced by R to obtain a frequency spectrum W(f); b, performing Gauss narrow-band filtering on the obtained frequency spectrum to obtain WG(f); c, extracting a real part curve of the WG(f), finding out null points of the real part curve, and recording fn as the frequency of the nth null point; d, finding out a root of a first-class zero Bessel function as Zn; e, solving a Rayleigh surface wave phase velocity value V(f) being equal to 2pifnR/Zn at a single frequency point according to the relation between a real part curve of a cross-correlation spectrum and the first-class zero Bessel function; f, connecting and drawing the solved phase velocities of single frequency points into a smooth curve as a frequency dispersion curve of Rayleigh surface waves. By using the relation between the Bessel function and the cross-correlation spectrum, the calculation efficiency of the frequency dispersion curve is effectively improved.

A noisy image is transformed through a wavelet transform into multiple scales. The wavelet coefficients are classified at each scale into two categories corresponding to irregular coefficients, edge-related and regular coefficients. Different denoising algorithms are applied to different classes. Alternatively, a denoising technique is applied to the finest scale, and the wavelet coefficients at all scales are denoised through anisotropic diffusion.

The disclosed picture removing-noise method based on multi-template mixed filter comprises: dividing the objective picture into MXN blocks without superposition to contain every pixel in some block; defining one group of filters including the mean filter and mid-value filter, every with different removing-noise strength; defining a numerical sequence with number standing for picture information size; obtaining the noise variance of the picture, calculating the uniformity level for the picture block to thereby select one filter for finishing the removing-noise. This invention simplifies calculation, and reduces real running time.

Systems and methods for multispectral imaging are disclosed. The multispectral imaging system can include a near infrared (NIR) imaging sensor and a visible imaging sensor. The disclosed systems and methods can be implemented to de-noise a visible light image using a gradient scale map generated from gradient vectors in the visible light image and a NIR image. The gradient scale map may be used to determine the amount of de-noising guidance applied from the NIR image to the visible light image on a pixel-by-pixel basis.

The invention provides an image character separation method, which is used to convert the image character detection results from a character area image to an OCR software recognizable binary image, and comprises following steps: (I) carrying out color component selection for the character area image; the characters are clearest among the selected color components; (II) carrying out binaryzation for the character area image on the base of the color component selected in step (I); (III) carrying out de-noising for the binary image derived in step (II). The invention can self-adaptive select the color component appropriate for binaryzation and thus achieving a better binaryzation effect; at the same time de-noising through a color-based clustering method can help acquiring a clearer binary character image with low noise and a better image character recognizing result.

The invention discloses a frame loss compensation method and a frame loss compensation device for a transform domain. The method comprises the following steps: the frequency-domain coefficient of a current lost frame is calculated by using the frequency-domain coefficient of a previous frame or frequency-domain coefficients of multiple previous frames of the current lost frame, and frequency domain-time domain transform is performed to obtain an initial compensation signal; and waveform regulation is performed to obtain a compensation signal so as to reduce the operation complexity and achieve a better compensation effect. Or, for all or part of frequency points of the current lost frame, extrapolation is performed on phases and amplitudes of corresponding frequency points of multiple previous frames to obtain phases and amplitudes of corresponding frequency points of the current lost frame and to further obtain frequency-domain coefficients of the corresponding frequency points, and frequency domain-time domain transform is performed to obtain a compensation signal, thus greatly improving the tone frame compensation effect. The method can be selected through a judging algorithm to compensate the current lost frame to achieve a better compensation effect. A voice signal frame and a music signal frame are processed in a differentiated mode, and a good compensation effect can be achieved in a variety of scenarios. Through gain adjustment, the compensation energy is stabilized and the compensation noise is reduced.

The invention discloses a method for classifying electric energy quality mixed disturbances based on multi-feature quantity of time-frequency domain. Voltage dip, voltage swell, short-term voltage interruption, impulsive transient, oscillatory transient, harmonic waves and flickering electric energy quality disturbances and mixed disturbances of a combination thereof are classified. The method for classifying the electric energy quality mixed disturbances concretely comprises the steps of: firstly, processing a disturbance signal by using an EEMD (Ensemble Empirical Mode Decomposition) and MIST (modified incomplete S-transform), and extracting nine time-frequency domain characteristic values; and then, inputting characteristic quantity to a blocked automatic classifying system to recognize the disturbances. By using the method, the mutual interference among single disturbances is fully considered and is effectively inhibited through the complementary time-frequency domain characteristic values. A simulation result shows that, under conditions of certain noises, the method can be used for effectively classifying the voltage dip, the voltage swell, the short-term voltage interruption, the impulsive transient, the oscillatory transient, the harmonic waves and the flickering electric energy quality disturbances and the mixed disturbances of the combination thereof.