149results about How to "Achieve denoising" patented technology

The invention discloses a vibration event mode identification method. The method includes acquiring and obtaining the original vibration signals, including the vibration signal and the non-vibration signal, via a vibration sensor; separating the vibration signal in the original vibration signals; denoising the vibration signal; performing feature extraction on the denoised vibration signal and obtaining the characteristic vector including three characteristics of: A performing wavelet packet decomposition on the time-frequency domain and obtaining the energy characteristic vector, B performing the cepstrum analysis and extracting the cepstrum parameter characteristics, and C extracting the signal characteristic on the time domain; and establishing an identification model composed of two grades of classifiers, wherein the first grade of classifier is a classifier based on SVM and divides the vibration events into non-intrusive events and intrusion events by taking the characteristic vector extracted from the vibration signal as the input, and the second grade of classifier performs the identification based on the artificial nerve network for the intrusion events to obtain the classification result.

The invention provides an automatic blog writer interest and character identifying method based on a support vector machine. The automatic blog writer interest and character identifying method includes building an interest classified training sample set and a character classified training sample set at first; respectively processing the two training sample sets by a Chinese morphology analyzer to obtain a candidate interest feature item set and a candidate character feature item set; then analyzing the two candidate feature item sets by the aid of a statistics method; building an interest classified feature item set and a character classified feature item set; displaying the interest classified training sample set and the character classified training sample set into vector forms by the two feature item sets; and finally respectively using two groups of training interest classifiers and character classifiers. The classifiers are used for identifying interests and characters of other writers. By the aid of the automatic blog writer interest and character identifying method, the interests and the characters of the writers can be accurately identified, the method is applied to various personal services based on information of the writers, service providers can sufficiently know users, service quality is improved, and the method has an extremely high practical value.

The invention discloses a low-dose energy spectrum CT image denoising method. The low-dose energy spectrum CT image denoising method includes the steps of (1) obtaining low-energy CT projection data and high-energy CT projection data of an imaged object under low-dose rays, carrying out CT image reconstruction on the low-energy CT projection data and the high-energy CT projection data to obtain a low-energy CT image (please see the specification) and a high-energy CT image (please see the specification), wherein H represents high energy, and L represents low energy; (2) building a mathematical model used for energy spectrum CT image denoising according to a base material decomposition model met by the reconstructed data in the step (1); (3) using generalized total variation as regularization prior, and building an objective function used for image denoising in cooperation with the mathematical model obtained in the step (2); (4) solving the objective function which is built in the step (3) and used for energy spectrum CT image denoising with a splitting Bregman algorithm, and completing energy spectrum CT image denoising. According to the low-dose energy spectrum CT image denoising method, the base material decomposition model met by the high-energy image and the low-energy image in an energy spectrum CT is used, energy spectrum CT image information and base material image information are combined, and energy spectrum CT image denoising is achieved.

The embodiments of the invention provide a shifting register unit and a driving method thereof as well as a gate drive circuit and a display device, and relates to the technical field of display, aiming at solving the problems of poor stability, short working life and high power consumption of the existing (gate driver on array) GOA circuit at the same time. The shifting register unit comprises apull-up control module, a pull-up module, a pull-down control module, a pull-down module, a reset module and M de-noising modules, wherein the de-noising modules are connected with a second control terminal, a pull-up node, a signal output end and a first voltage terminal, and are used for pulling the electric potentials of the pull-up node and the signal output end down to the first voltage terminal under the control of the second control terminal. The shifting register unit is used for outputting gate line scanning signals for a gate line connected with the shifting register unit.

The invention relates to a discriminative dictionary learning based multi-source image fusion denoising method. The method includes: acquiring multi-source images as training samples, and learning thesamples through a K-SVD algorithm to obtain an initial cartoon dictionary and an initial texture dictionary, introducing weighting nuclear norm constraint to bring forward a new dictionary learning model, performing new dictionary learning model learning to obtain a cartoon dictionary and a texture dictionary, decomposing to-be-fused images through an MCA algorithm to obtain a cartoon component and a texture component, introducing weighting Schatten sparse nuclear norm constraint to the cartoon component, adding grey level histogram gradient protection to the texture component to bring forward a new image decomposition model, iterating the model to obtain a cartoon sparse coding coefficient and a texture sparse coding coefficient, respectively fusing to obtain a cartoon component and a texture component according to a principle of maximum sparse coding coefficient l1 norm values of corresponding components, and adding the cartoon component and the texture component to obtain a final fusion image. The method has advantages that image fusion and denoising are realized, false information transferring is avoided, time consumption is reduced, and fusion and denoising performances are improved.

The invention discloses a denoising and sharpening method by aiming at a multimode image, which comprises the following steps: 1, carrying out Dual bilateral filtering to an input noise multimode image group dijk to obtain a first level of denoised image group g'ijk; 2, taking the first level of denoised image group g'ijk as a reference image, and carrying out referenced Dual bilateral filtering to the original noise image group dijk to obtain a second level of denoised image group g''ijk; 3, carrying out edge-preservation cross unsharpened masking filtering to the second level of denoised image group g''ijk to obtain a sharpened and enhanced image group f'ijk; and 4, taking the g''ijk as a reference image, and carrying out referenced Dual bilateral filtering to the sharpened and enhanced image group f'ijk to obtain a final result fijk. According to the characteristic that information among component images in the multimode image group has certain relativity, the multimode image can be subjected to noise suppression and edge selectivity sharpening, and the subjective visual quality is improved.

The invention discloses a denoising method of a strong noise pollution image on the basis of partial differential equation, mainly solving the problem of poor traditional denoising effect of the strong noise pollution image. The realization process comprises: (1) pretreating an input noise image u0, and recording the result as u; (2) calculating the partial derivative sum of the image u; (3) calculating the gradient module value of the image u; (4) according to the gradient and the gradient module value, building the partial differential equation; (5) calculating diffusion coefficients and psi in the partial differential equation; (6) utilizing the coefficients and psi, solving the partial differential equation to obtain a filter image; (7) calculating the peak signal to noise ratio PSNR of the filter image; and (8) repeating steps 2 to 7, when the PSNR value of the filter image output iteratively one time is smaller than that output iteratively in the previous time, stopping iteration, and outputting the previously iterated filter image. The invention has simple calculation and high operation speed, can better keep image texture details while smoothening strong noise and can be used for denoising a natural image with strong noise pollution.

The invention provides a seismic data processing method based on the alternating direction method and the total variation theory. The method comprises the steps of building a seismic data model based on total variation, using the classic quadratic penalty function method to convert the model into a solving problem meeting the requirement of iteration and alternating minimization, and performing iteration through the alternating direction method to get seismic data after recovery. By using the technology, seismic data can be de-noised and de-blurred (de-convoluted), the signal-to-noise ratio and resolution of seismic data can be improved effectively, and a good processing effect can be achieved even if the signal is seriously polluted by noise.

The invention discloses a multi-frame low-resolution image super-resolution reconstruction method based on a convex combination mode, belongs to the field of computer vision, mainly solves a problem of image processing precision and provides an optimization algorithm. The method comprises the following steps of applying joint motion estimation and super-resolution reconstruction methods to an input multi-frame low-resolution (LR) image sequence to obtain a joint high-resolution (HR) image Ijoint; applying a reconstruction method based on improved kernel regression to the input multi-frame LR image sequence to obtain a kernel regression HR image Ikernel; and applying a convex combination frame provided by the invention and deciding a fused region according to an airspace fusion rule, deciding frequency components that need to be fused according to a frequency domain rule and deciding weight distribution of fusion of two methods according to the global weight, and then fusing the HR images obtained by the two steps through the three aspects to obtain a final reconstructed HR image.

Disclosed are an image denoising method and device and an image processing system. The method comprises determining similar blocks of a first image block in an image; based on the first image block and the similar blocks of the first image block, determining a second image block; based on the difference value of the first image block and the second image block, determining a third image block; performing wavelet transformation on the third image block, shrinking the coefficient of the wavelet transformation, and obtaining a processed third image block through inverse wavelet transformation; based on the processed third image block and the second image block, obtaining denoised image data of the first image block. The image denoising method is low in calculating amount and complexity and can eliminate color noise and brightness noise in the image simultaneously.

The invention discloses an algorithm for carrying out feature extraction, classification and identification on burial area invasion vibration signals. The algorithm is suitable for multi-grating sensing and detection systems, and comprises the following steps of: acquiring vibration signals through a multi-grating sensor network; carrying out decomposition by utilizing an EEMD algorithm so as to obtain an IMF component; calculating an EEMD energy entropy; eliminating interferences of un-artificial signals; carrying out feature extraction; and finally carrying out classification, identification and early warning on invasion signals by adopting a support vector machine which is optimized through a particle swarm algorithm. According to the algorithm disclosed by the invention, the alarm correctness is improved, the false alarm rate is reduced and a practical value is provided.

The invention relates to a curvature variation based wavelet image denoising algorithm. The curvature variation based wavelet image denoising algorithm is characterized by comprising the steps of 1, algorithm description, namely, performing wavelet transformation for an input image to be denoised, introducing a horizontal set curvature as a correction factor into a variation model, and creating the curvature variation based wavelet image denoising algorithm; 2, algorithm verification, namely, the first item for the curvature variation model is a dispersion item in the image smoothing process, and the second item of the curvature variation model is designed to be the control function of the image structure, to maintain the integral structure of the image; 3, algorithm simulation, namely, performing the simulation algorithm of MATLAB software, and analyzing the timeliness and complexity of the algorithm through the simulation result. With the adoption of the algorithm, the processed image can be clear and close to the original image; the signal to noise of the denoised image is increased by about 15dB by being compared with that of a TV model; the classic wavelet threshold denoising algorithm is increased by about 25dB, and moreover, the definition is greatly improved.

The invention discloses a micro-seismic signal multi-scale denoising method and device and a readable storage medium, and the method comprises the steps: 1, obtaining a micro-seismic signal, carryingout the EMD or EEMD decomposition, and filtering the high-frequency noise in the decomposed signal; 2, respectively constructing a Hankel matrix of each IMF component; 3, carrying out singular value decomposition is based on the Hankel matrix of each IMF component to obtain a score vector of principal component analysis, carrying out primary denoising, and carrying out soft threshold secondary denoising on each component signal and residual component after primary denoising; and 4, superposing the component signal subjected to secondary denoising and the residual component to obtain a denoisedmicro-seismic signal. According to the method, singular value decomposition is associated with principal component analysis, information of singular value decomposition serves as a score vector of principal component analysis, the PCA calculation process is simplified, and the defect that denoising cannot be conducted on a single column vector through the score vector of singular value decomposition is overcome.

The invention is mainly used for the optical fiber sensing temperature measurement field, and especially relates to a long-distance high-spatial-resolution Raman temperature measurement sensor and a realization method thereof. The long-distance high-spatial-resolution Raman temperature measurement sensor is characterized in that the long-distance high-spatial-resolution Raman temperature measurement sensor comprises an ultra-narrow-linewidth laser, a circulator, an acoustic-optic modulator, a reflector, a radio frequency generator, a 1*2 optical splitter (1:1), two wavelength division multiplexers, a Raman amplifier, an EDFA, two 2*1 couplers, a photoelectric detector, a signal processing and collection unit and a microprocessor unit. The beneficial effects are that the invention discloses the realization method of the long-distance high-spatial-resolution Raman temperature measurement sensor; the monitoring distance of the Raman temperature measurement sensor based on OTDR is generally smaller than 10 km, and spatial resolution is generally larger than 1 m; and the invention provides the realization method of the Raman temperature measurement sensor based on OFDR location technique, wherein the method can greatly improve measuring distance and spatial resolution index of the system, and the method can enable the monitoring distance of the Raman temperature sensor to reach 20 km, and spatial resolution index to reach the centimeter scale.

The invention relates to an energy functional based image smoothing and sharpening algorithm. The energy functional based image smoothing and sharpening algorithm comprises the following steps of 1 converting an anisotropic diffusion equation into a minimized energy functional, 2 representing a dynamic equation in an internal coordinate mode, 3 establishing the energy functional (shown in the description), 4 establishing a gradient threshold function k = e - at of an image, 5 introducing a fidelity item (shown in the description) to establish the energy functional based image smoothing and sharpening algorithm and 6 adopting a central difference numerical algorithm to further process a result obtained in the step 5. By means of the energy functional based image smoothing and sharpening algorithm, information amount required by the algorithm is low, the algorithm is simple, image denoising is achieved, and polluted images are more approximate to original images.

The embodiment of the invention provides a data noise reduction method and system for bridge structure monitoring. The method comprises the following steps: acquiring an original observation signal; performing pole symmetry modal decomposition on the original observation signal to obtain a decomposed intrinsic mode component and a residual component; separating the decomposed intrinsic mode component and the residual component by adopting a blind source separation algorithm to obtain a separation signal; performing frequency domain conversion on the separation signal to obtain a frequency domain conversion result, and obtaining a noise component according to the noise frequency in the frequency domain conversion result; removing a noise component, and performing reverse reconstruction on the decomposed intrinsic mode component and the residual component to obtain a reconstructed signal component; calculating a Spearman coefficient for decomposing the intrinsic mode component and the original observation signal, and determining a preset threshold; and accumulating the reconstructed signal components according to a preset threshold to obtain signal data without noise information. According to the embodiment of the invention, modal decomposition and blind source separation are combined, and decomposition, denoising and reconstruction of the bridge monitoring data are effectively realized.

The invention discloses a robust depth map structure reconstruction and denoising method based on a guided filter. A structure error region is detected; detecting a place where the input depth map isgreatly different through the guided filtering of the large window and the guided filtering of the small window; an eclosion effect can be achieved due to guided filtering under a large window; the guide filtering of the small window only plays a smoothing role. Therefore,. Therefore, areas with large differences can be regarded as structure error areas. Marking as a potential structure error region, then constructing a weight based on an iterative reweighted least square algorithm, after the weight construction is completed, carrying out overall solution and updating the depth map, judging whether a set iteration frequency is reached or not according to a result, if so, outputting the depth map to end calculation, and otherwise, carrying out structure error region detection again. According to the method, strong noise can be suppressed, structure error regions of the depth map and the color map can be repaired, the consistency of the depth map and the color map is improved, a correctdepth map boundary is recovered, and the method has important guiding significance for improving the quality of the synthesized view.

The invention discloses a compression-sensing-based signal reconstruction and de-noising method of a ground penetrating radar. The method comprises the following steps: establishing a complete dictionary for input signal sparse expression; constructing a sampling matrix based on a correspondence relation between missing ground penetrating radar data and complete data; according to a measurement matrix, the complete dictionary, and measurement data, reconstructing a sparse coefficient by using an orthogonal matching tracking algorithm; and carrying out inverse transformation by using the reconstructed sparse coefficient to construct a complete signal. The provided method is a novel ground penetrating radar data processing method. With the method, the missing ground penetrating radar data can be recovered correctly and noises in the ground penetrating radar can be eliminated effectively. Therefore, the provided method is a multi-purpose processing method.

The invention provides a suppressing method suitable for loess plateau out-of-line ground roll waves. The suppressing method comprises the steps of: 1) performing offset geometric transformation on X to obtain X'; 2) determining an apparent velocity range of ground roll waves; 3) determining a ground roll wave frequency band range on the X'; 4) acquiring G' via G; 5) seeking an orthogonal projection vector matrix of the single shot seismic record G', and performing K-L transformation on the G'; 6) reconstructing a ground roll wave model G'' by utilizing the K-L transformation; 7) acquiring a ground roll wave final model G''' via the G''; acquiring a single shot seismic record X'' after suppressing the ground roll waves; 9) repeating the steps from step 3) to step 8) to obtain a single shot seismic record X''' after suppressing the ground roll waves finally; and 10) performing offset geometric inverse transformation on the single shot seismic record X''' to obtain X'''', and finishing the suppression of the ground roll waves. The suppressing method adopts frequency division processing, automatically adjusts and tracks change features of the ground roll waves under different geological conditions, so as to achieve the wave field separation and de-noising, can increase the continuity of a mesozoic tight oil target layer, and has amplitude fidelity characteristics.

The invention discloses a voice feature extraction control device for a recording pen. The control device comprises a microphone, a high frequency amplifier, a converter, a central controller, a memory and a display; the central controller comprises an identification unit, a calculation unit, a comparison unit and an extraction unit; the microphone is used for recording voice signals; the high frequency amplifier amplifies the recorded voice signals; the converter converts the voice signals into digital signals; the identification unit is used for voice identification and obtains voice feature; the storage unit is used for storage; the calculation unit calculates the appearance frequency; the comparison unit compares the appearance frequency to obtain the highest frequency; the extraction unit extracts voice feature of the highest frequency; and the display is used for display. The voice feature extraction control device uses simple structure to detect and identify voice signals, improves the automation degree, and enables functions of the recording pen to be more complete.