126 results about "Curvelet transform" patented technology

Fast digital implementations of the second generation curvelet transform for use in data processing are disclosed. One such digital transformation is based on unequally-spaced fast Fourier transforms (USFFT) while another is based on the wrapping of specially selected Fourier samples. Both digital transformations return a table of digital curvelet coefficients indexed by a scale parameter, an orientation parameter, and a spatial location parameter. Both implementations are fast in the sense that they run in about O(n2 log n) flops for n by n Cartesian arrays or about O(N log N) flops for Cartesian arrays of size N=n3; in addition, they are also invertible, with rapid inversion algorithms of about the same complexity.

Method for identifying geologic features, such as hydrocarbon indicators, from geophysical data, such as seismic data, by taking a curvelet transform of the data. After the curvelet representation of the data is computed (350), selected geophysical data attributes and their interdependencies are extracted (355), from which geological features may be identified (360), either from attribute data volumes that are created or directly from the curvelet representation.

The invention provides a high-precision three-dimensional shape measurement method for a jump object. The method is characterized in that the fringe projection profilometry, in combination with curvelet transform, is applied to three-dimensional shape measurement of the jump object. The fringe profilometry based on structured light projection is a high-precision, quick, noncoherent optical full-field object three-dimensional shape measurement technique, and experimental devices adopted are simple, which mainly include a computer, a projector and a digital camera. The projector is used for projecting a plurality of sets of sine fringe patterns different in frequency to the jump object to be tested; the digital camera is used for recording deformed fringe patterns and transmitting the deformed fringe patterns to the computer, and then the deformed fringe patterns are denoised by virtue of curvelet transform and the phase distribution of the deformed fringes is demodulated in combination with a phase shifting method, and unwrapped phase distribution is obtained by using a phase unwrapping method. At last, three-dimensional data of the jump object is obtained according to a phase-height relation. The high-precision three-dimensional shape measurement method has the major gain that a high-precision fast full-field measurement technique for the three-dimensional shape measurement of the jump object.

A deblocking method and apparatus using an edge flow-directed filter and curvelet transform for deblocking Block Discrete Cosine Transform (BDCT) compressed images. The deblocking method includes: extracting an edge flow vector from a Block Discrete Cosine Transform (BDCT) compressed image to identify a direction of changes in image attributes; performing a dyadic discrete wavelet transform on the BDCT compressed image; selectively filtering a highpass wavelet image among dyadic discrete wavelet transform compressed images according to an edge flow direction of an edge pixel; performing a curvelet transform on an image including the selectively filtered highpass wavelet image and wavelet images other than the highpass wavelet image; and estimating an original image before being BDCT compressed using the curvelet transform.

The invention discloses a self-adaptive de-noising and characteristic enhancing method of an SAR (Synthetic Aperture Radar) image, which mainly aims at overcoming the defects that the traditional method has poor de-noising property and can not self-adaptively select parameters in the experiment. The method comprises the following steps: firstly, carrying out logaritmim and then ME-curvelet transformation on an original SAR image; secondly, self-adaptively selecting and optimizing parameters in improved gain functions by adopting an improved PSO (Particle Swarm Optimization) algorithm according to the provided evaluation criterion; and finally, carrying out the nonlinear transformation on ME-curvelet coefficients, ME-curvelet inverse transformation and exponential transformation by adopting the improved gain functions to obtain the final SAR image subjected to the de-noising and characteristic enhancement. By using the method, the noise can be removed while the characteristics are enhanced, the complexity for processing can be reduced and the better de-noising and characteristic enhancing effects of the SAR image can be achieved.

The invention brings forward a frequency domain three-dimensional irregular earthquake data reconstruction method. The method is characterized in that first of all, three-dimensional earthquake data in a time domain is converted to a frequency domain by use of Fourier transform, and then, a projection onto convex set (POCS) algorithm is employed and curvelet transform capable of describing localized features of the earthquake data is introduced; and in an iteration process, a new threshold parameter attenuating according to an index rule is brought forward, and each frequency slice is individually reconstructed by use of a soft threshold operator, such that the iteration frequency is reduced, the reconstruction precision is improved, and the purpose of reconstructing the three-dimensional earthquake data is realized. According to the invention, the new threshold parameter attenuating according to the index rule is brought forward and each frequency slice is reconstructed in individually by use of the soft threshold operator, such that the disadvantage of quite slow convergence speed of a conventional threshold parameter is overcome, the calculation complexity of an algorithm is reduced, the calculation efficiency is substantially improved, and the operation time is reduced.

The invention relates to a visible light and infrared image fusion method based on Curvelet transformation. The method includes the steps: firstly, subjecting an original image to quick dispersion Curvelet transformation to obtain a series of low-frequency and high-frequency sub-band factors of different lengths and directions, and calculating a focus evaluation value of each sub-band; secondly, using a local variance weighting strategy for the low-frequency sub-bands to complete reserve low-frequency information of the infrared image, and adding low-frequency characteristics of the visible light image; thirdly, using a four-step correlation coefficient matching strategy for the high-frequency sub-bands, and selecting a proper high-frequency coefficient; and finally obtaining a fused image through Curvelet inverse transformation. Using the method can effectively integrate useful information of original images, and compared with the traditional wavelet transformation method, pyramid method and the like, the visible light and infrared image fusion method has better fusion performance.

The invention discloses an SAR (Synthetic Aperture Radar) image segmentation method based on multi-scale feature fusion, comprising the steps of: firstly, extracting the textural features of an image by utilizing fast discrete curvelet transform (FDCT), extracting the statistic features of the image by utilizing stationary wavelet transform (SWT), then, fusing the two type of multi-scale features into high-dimensional feature vectors, and finally, segmenting by adopting a fuzzy C-mean clustering method. At the same time of eliminating broken pieces in a homogenizing area, the boundary is more precise and smoother. The method is favorable for effectively preventing the extraction of textural information from the interference of speckle noises. By fusing SWT coefficient statistical features, the textural information and the grey statistical information of the SAR image are effectively utilized and a favorable segmentation effect can be achieved.

The invention provides a sparse sampling method in seismic data regularization and belongs to the field of seismic exploration data regularization. The method includes the following steps: (1) obtaining a seismic signal sparse expression: selecting Curvelet conversion as a seismic signal sparse expression; (2) designing a random seismic channel sparse sampling scheme which meets Bernoulli distribution; (3) applying the sampling scheme obtained through step (2) to carry out resampling on original seismic data so that undersampled seismic data which meets a Bernoulli distribution rule is formed; (4) applying a restructuring algorithm to generate complete reconstruction data. Through use of the method, a complete seismic channel reconstruction result can be obtained through fewer seismic channels in a compressed sensing method.

The invention relates to a no-reference image quality evaluation method based on Curvelet transformation and phase coincidence. The no-reference image quality evaluation method based on Curvelet transformation and phase coincidence comprises the following steps: (1), images are transformed to a Curvelet domain and a phase coincidence domain; (2), a series of natural scene statistical characteristics are extracted from the Curvelet domain and the phase coincidence domain; the series of natural scene statistical characteristics comprise logarithm histogram peak value coordinates of Curvelet coefficients and phase coincidence coefficients, direction energy distribution characteristics and dimension energy distribution characteristics; and (3), a two-step frame is used, the series of characteristics extracted in the step 2 and a support vector machine are utilized for firstly classifying distorted images of unknown types, and then nonlinear regression of a specific type is conducted on the distorted images according to a classification result, and DMOS is forecasted according to an objective quality evaluation result of the images. The no-reference image quality evaluation method based on Curvelet transformation and phase coincidence has the advantages of being high in human eye subjective consistency, small in time complexity, and high in application value.

The invention relates to a seismic wave attenuation compensation method in a curvelet domain. The method comprises the following steps of (1) collecting a seismic signal and carrying out preprocessing to obtain a time domain signal x(t) of an earthquake, (2) carrying out curvelet transform on the time domain signal x(t) to obtain a curvelet domain signal psi j, l (omega j, Tk), (3) carrying out smoothing process on the curvelet domain signal psi j, l (omega j, Tk) to obtain a curvelet coefficient Phi j, l (omega j, Tk), (4) carrying out threshold iterative processing on the curvelet coefficient Phi j, l (omega j, Tk) to obtain a denoised signal Psi j, l (omega j, Tk), (5) carrying out pointwise recursion to obtain the deep and shallow layer energy ratio delta j, ll (omega j, Tk) of the denoised signal Psi j, l (omega j, Tk) in each frequency band compensation angle, (6) calculating all frequency band compensation factor coefficient matrices Dj, ll (omega j, Tk) in a compensation angle, (7) weighing the reciprocal values of the compensation factor coefficient matrices Dj, ll (omega j, Tk) in all frequency band compensation angles to obtain a signal Cj, l (omega j, Tk) after curvelet wave weighing, (8) carrying out inverse curvelet transform on the signal Cj, l (omega j, Tk) after the curvelet wave weighing to obtain a time domain signal xx(t) after frequency division directional compensation. The method can be widely applied to the seismic data processing.

The invention relates to a weak seismic signal reconstruction method based on curvelet transform and belongs to the technical field of petroleum and seismic exploration data processing. The method is characterized by comprising the steps of (1) determining a noise model and the parameters of the noise model, (2) reading seismic data, (3) conducting curvelet decomposition on the read seismic data to obtain the curvelet coefficient of seismic data, (4) conducting curvelet decomposition on the obtained noise model to obtain the curvelet coefficient of the noise model, (5) filtering the curvelet coefficient of the seismic data in a curvelet domain according to a set filtering threshold to filter out the noise part in the curvelet coefficient, and (6) conducting curvelet inverse transform on the filtered curvelet coefficient to obtain seismic signals with noise eliminated through reconstruction. By means of the method, filtering and denoising are conducted on the seismic signals, noise interference is filtered out precisely while effective seismic reflection signals are protected, effective weak signals are highlighted, and the purpose of detecting and recognizing deep weak seismic signals is realized; exploration precision is improved, exploration cost is reduced, using is convenient and economic efficiency is high.

The invention provides an angular part superposition seismic data-based fluid identification method, which comprises the following three steps of: 1) rapidly estimating an impedance reflection coefficient, namely starting from Shuey approximation (Shuey, 1985) in a Zoeppritz equation, deducing a formula for accurately and rapidly estimating the impedance reflection coefficient of longitudinal and shear waves, and calculating a seismic attribute used for fluid identification; 2) extracting a new fluid identification attribute based on the angle gather part superposition seismic data, namely starting from a fluid factor formula provided in Smith and Gidlow (Smith, 2003), and obtaining the combination of the angle gather part superposition seismic data for fluid identification and reservoir prediction; and 3) performing a curvelet transform and Bayesian theory-based fluid identification, namely equalizing each angle superposition gather data by using a wavelet equalization method on the basis of the steps 1) and 2) so as to remove difference of different angles and keep the difference of different angle gather data caused by the fluid, obtaining abnormality at a target layer by using Bayesian wave field separation technology in a curvelet domain, naming the abnormality as angle fluid gather, and solving various attributes to obtain the abnormality at the target layer, wherein the attribute obtained by employing various combinations of the angle gather part superposition seismic data can better distinguish gas / oil-containing and water-containing reservoirs, particularly the attribute section obtained from the third power of the angle gather can better distinguish the gas / oil-containing and water-containing reservoirs.

The invention discloses a Treelet curvelet domain denoising- based method for change detection of a remote sensing image. The method comprises the following steps: (1), reading data; (2), carrying out median filtering; (3), constructing a logarithm difference image; (4), constructing an absolute value difference image; (5), carrying out fast discrete curvelet decomposition; (6), carrying out classification on curvelet transform coefficients; (7), assigning all the curvelet transform coefficients of a Fine scale layer a value of zero ; (8), carrying out denoising on a Detail scale layer; (9), carrying out curvelet transform; (10), calculating a change proportion threshold; (11), carrying out classification; and (12) obtaining a change detection result graph. According to the invention, themethod has good robustness on noises; marginal information of a change area can be well maintained and fake change information is reduced; and the method has a high detection precision; moreover, themethod can be applied to fields including disaster monitoring, forest coverage rate assessment and city planning and the like.

The invention relates to the technical field of seismic exploration, in particular to a combined attenuation random noise processing method based on curvelet transform and total variation. The method comprises the following steps: acquiring single-shot earthquake data; performing curvelet transform on the acquired single-shot data or stacking of single-shot data; performing multi-scale curvelet threshold denoising; denoising by adopting a total-variation denoising technology; outputting earthquake data through graphic display software. According to the method, an optimal threshold is selected according to the distribution rule of random noise in a curvelet domain to maximize the signal to noise ratio of data, and the aim of optimally denoising is fulfilled through curvelet transform; according to the total variation minimization technology; a curvelet coefficient is adjusted through a total variation minimization technology, thereby overcoming the defect of pseudo-curve caused by separate use of curvelet transform, and making displayed stratum data more real and reliable in order to further perform stratum analysis and obtain more accurate analysis results about the oil content and ore content.

The invention discloses an image structure model-based compressed sensing image reconstruction method, which mainly solves the problems that image structure information is not considered and blind iteration is carried out in the conventional method. The method comprises the following steps of: inputting an image A, and performing Fourier transform on the image A to obtain a Fourier coefficient matrix X1 of the input image A; sampling the Fourier coefficient matrix X1 according to a density variable sampling model for fully sampling Fourier coefficients at low frequency to obtain an observation vector f; performing inverse Fourier transform on the observation vector f to obtain a transformed image X2; performing edge detection on the transformed image X2 to obtain an edge detection image X3; performing Wavelet transform and Curvelet transform on the edge detection image X3, finding an edge position and positions of large coefficients, and finding corresponding coefficients in the transformed image X2 according to the obtained positions; and performing Wavelet-curvelet frame-based Split Bregman reconstruction algorithm to iterate for 20 times and finally obtaining the required reconstructed image. The method has the advantages of higher accuracy, better effect and shorter time for image reconstruction.

The invention discloses a defective condition detection method based on curved and dotted singularity characteristics of an insulator of an overhead contact system of a high-speed rail. The method comprises the steps as follows: the overhead contact system is shot at night, so that an image of a to-be-detected insulator is obtained; the image of the to-be-detected insulator is subjected to contrast ratio enhancement and Gaussian filter and noise reduction processing; an approximate direction of the insulator in the image is determined through Radon transformation; then the insulator is determined and an angle is finely adjusted by using the curved singularity characteristics of the second generation curvelet transform; and finally a defective condition of the insulator is judged by the curved singularity characteristics of the second generation curvelet transform and the dotted singularity characteristics of the wavelet transform. The method can distinguish the insulator rapidly and correctly, and the state whether the insulator is damaged, a defective foreign matter blended state, and a type of the defective state are judged effectively. The actual input quantity is the image to-be-detected, the actual output quantity is a picture of the insulator which is positioned and result data of the defective state detection, the distinguishing effect is good, and the judgment result is accurate and reliable.

The invention discloses a FAST-AIC-algorithm-based automatic first-arrival collecting method for micro-seismic signals. The method includes: providing a new identification method of micro-seismic events according to the fact that the propagation path and the travel time information of hydraulic fracturing perforation signals are similar to those of the micro-seismic signals; using a hydraulic fracturing wave detector arrangement manner and a single-channel signal local extreme value to design a precise time window near a micro-seismic triggering moment; using Curvelet conversion to filter data in the precise time window; performing first-arrival collecting in the precise time window through a FAST-AIC algorithm, wherein the FAST-AIC algorithm is obtained by optimizing and converting a traditional AIC algorithm, the linear combination manner of the arithmetic sum and the quadratic sum of a discrete real number sequence is obtained, repeated calculation is reduced, and calculation efficiency is increased greatly. The new method is used to perform first-arrival collecting on analog data and actual hydraulic fracturing micro-seismic monitoring data to verify the feasibility and correctness of the new method. Compared with the first-arrival collecting result of a conventional micro-seismic method, the new method has evident advantages.

The invention relates to a hyperspectral image band noise removing method which comprises the steps that 1. an image to be processed and two reference images are selected, wherein the noise of the two reference images is lower than the noise of the image to be processed, and the two reference images are placed on the two sides of the band of the image to be processed in the band range respectively; 2. the image to be processed and the reference images are subjected to Curvelet forward transformation, multi-scale multi-direction Curvelet transformation coefficients are obtained, and the Curvelet transformation coefficients comprise high-frequency coefficients and low-frequency coefficients; 3. the high-frequency coefficients of the two reference images are subjected to weighting processing, then first coefficients are obtained, and the first coefficients are used for replacing the high-frequency coefficients of the image to be processed; and 4. the Curvelet transformation coefficients obtained by replacing of the image to be processed are subjected to Curvelet inverse transformation, and accordingly noise removing images are obtained. The method is used for noise removing, edges and details are not lost during image noise removing, and image quality is improved.

The invention discloses a visible light and infrared image fusion algorithm based on UDCT (Uniform Discrete Curvelet Transform) and a PCNN (Pulse Coupled Neural Network), which relates to the technical field of image processing and solves the technical problems that the fused image is not clear and details are unobvious as the similarity of low-frequency information of a to-be-fused image can not be judged in the prior art, and the detail richness degree of a source image can not be judged. The algorithm of the invention mainly comprises steps: (1) after source images of the visible light image and the infrared image are subjected to UDCT decomposition, UDCT subband coefficients with different scales in different directions can be obtained, and the UDCT subband coefficients comprise low-frequency and high-frequency UDCT coefficients; and (2) according to a specific rule, a different mode is adopted for each scale layer for fusion processing, a low-frequency coefficient fusion rule is adopted for the low-frequency coefficients, a high-frequency coefficient fusion rule is adopted for the high-frequency coefficients, and finally, the UDCT coefficient for each layer after fusion can be acquired; and a reconstructed image obtained after inverse transforma on the UDCT coefficient for each layer after fusion is the fused image.

The invention provides a seismic data combined denoising method based on curvelet transform and singular value decomposition. The combined denoising method based on curvelet transform and singular value decomposition comprises carrying out direction control and noise variance reduction through curvelet transform; partially flattening lineups through improved singular value decomposition, sequentially processing each data point and carrying out effective suppression on the noise of the whole seismic section; and combining the curvelet transform and singular value decomposition through quadtree decomposition. The technique, combined with good denoising effect of singular value decomposition and anisotropy and multi-scale characteristics of curvelet transform, can improve the signal to noise ratio of the seismic data, protect the effective signals while suppressing the random noise, can effectively remove image artifacts, and keep dynamical features of the seismic wave, and is an effective noise suppression technique.

The invention provides a second generation curvelet transform-based static human detection method, which mainly solves the problem of high detection false-alarm rate in the conventional human detection technology. The detection process comprises the following steps of: acquiring a negative sample through bootstrap operation of the negative sample, and forming a training sample set by the negativesample and other positive samples in a database; calculating curvelet transform-based feature vectors of all training samples to form a training sample feature set; performing classification trainingon the sample feature set by adopting an AdaBoost algorithm to obtain a classifier; inputting a to-be-tested image with any size, calculating curvelet transform-based feature vectors of all scan window images in the to-be-tested image; inputting the curvelet transform-based feature vectors of all scan window images into the obtained classifier for classification; and according to classification results, combining all scan windows classified as human by utilizing a main window merging method to form the final human detection result. The method has the advantages of high detection accuracy and low false-alarm rate, and can be used for classifying and detecting human in the image.

The invention discloses a method for extracting texture based on a lung nodules three orthogonality position computed tomography (CT) image, which includes: a building a lung nodules three orthogonality position CT image system; and b extracting lung nodules three orthogonality position texture characteristic parameters through a disperse Curvelet transformation method. The invention further discloses a method for forecasting lung cancers, which includes: a extracting lung CT image texture characteristics; b extracting three orthogonality position CT image and video signs; and c building a forecasting model based on multi-dimensional characteristic parameters including lung nodules three orthogonality position CT image texture characteristics and video signs through a Gradient Boosting algorithm. The method for extracting texture based on lung nodules three orthogonality position CT image and the method for forecasting lung cancer can be used for early diagnosis of pulmonary nodules.

The invention discloses a multidimensional vein extracting method based on a brain nuclear magnetic resonance image. An area-of-interests in the brain nuclear magnetic resonance image is segmented through a region growing method. Vein characteristic parameters of the area-of-interests are extracted through a Curvelet conversion and Contourlet conversion method. The people comprise an Alzheimer patient group, a mild cognitive impairment patient group and normal old people group, the vein characteristic parameters of the area-of-interests comprise entropy, gray average, correlation, energy, the homogeneity degree, variance, the maximum probability, deficit distance, an inverse gap, clustering tendency, contrast, a sum mean value, a difference mean value, a sum entropy and a difference entropy. The area-of-interests comprises a entorhinal cortex are and a sea horse area.

The invention discloses a method for detecting a pantograph crack fault based on a curvelet domain moving parallel window, and the method comprises the following steps of: acquiring pantograph images through image pickup systems along a train track, performing a series of preprocessing operations on the acquired images to obtain slipper images; performing second-generation curvelet transform on the slipper images, converting images in a spatial domain into a curvelet decomposition coefficient matrix in a curvelet domain, analyzing, classifying and threshold-processing the curvelet decomposition coefficient matrix by using a moving parallel window method, filtering off curvelet coefficients corresponding to all the inherent image elements, except cracks, on the slipper images, and finally,performing inverse transform on the processed curvelet decomposition coefficient matrix so that slipper crack images can be extracted to implement the detection and recognition for pantograph slippercrack fault. Aiming at the singularity characteristic of pantograph slipper crack lines, the method provided by the invention can be used for effectively capturing the linear characteristics of the crack images by means of curvelet transform, and precisely and effectively extracting crack characteristics to implement the detection for the pantograph crack fault.

The invention discloses a seismic signal completion method based on a synchronous sparse low-rank tensor completion model. The method comprises the following steps that: a four-dimensional earthquakeis preprocessed; a tensor completion model is established; the objective function of the tensor completion model is constructed through a truncated tensor nuclear norm regularization method; and the constructed objective function of the tensor completion model is solved through an imprecise augmentation Lagrange multiplier method. According to the method, a tensor model based on a transform domainis established; a tensor to be recovered is converted into a Curvelet transform domain, so that the sparsity and low rank of the tensor to be recovered is improved; and the accurate and efficient recovery of four-dimensional missing seismic data is realized through an ADMM algorithm.

The invention relates to an automobile anti-halation method fusing visible and infrared images by means of enhanced HIS-Curvelet transformation in order to solve the automobile halation problem at night. The technical scheme comprises acquiring visible and infrared images of the road condition in front of an automobile at night; filtering and denoising the two kinds of images; taking the infraredimage as a reference image to register the visible image; converting the visible image to an IHS color space to obtain three components including the luminance I, the hue H and the saturation S; carrying out Curvelet decomposition on the luminance component I and the enhanced infrared image to obtain high and low frequency coefficients; employing a designed weight automatic adjusting strategy to fuse the low frequency coefficients; employing a modulus value maximization strategy to fuse the high frequency coefficients; carrying out Curvelet reconstruction on the fused high and low frequency coefficients to obtain a new luminance signal component I'; and carrying out HIS inverse transformation on the I', the original hue H and the saturation S to obtain a final fused image. According to theinvention, high-luminance halation information is rejected, and the image definition is effectively improved.

The invention discloses a method for suppressing the interference of a neutral line of a prestack seismic signal based on curvelet transform. The method comprises the following steps of: firstly, performing sparse representation on the seismic signal through curvelet transform; secondly, analyzing all coefficient matrixes to obtain frequency layers and directional ranges in which linear interference and effective signals are respectively located, wherein three cases are disposed as follows: 1, for the frequency layers and the direction in which only the linear interference is located, the corresponding curvelet coefficient is set to be 0; 2, for the frequency layers and the direction in which the effective signals and linear interference coexist, the threshold of the curvelet coefficient is suppressed; and 3, for the frequency layers and the direction in which the linear interference is not located, the coefficient keeps unchanged; and finally, performing curvelet transform to obtain a suppressed prestack seismic signal. By the method, the linear interference can be effectively suppressed, and the damage to the effective signals can be reduced to the greatest extent. Moreover, the method has the advantages of simplicity and rapidness.

The invention relates to a method for extracting multi-dimensional texture of nodi from medical images. The images are segmented by using a region growing method, content texture characteristic parameters of nodi are extracted by adopting a gray level co-occurrence matrix method, and edge texture characteristic parameters of the nodi are extracted by adopting a Curvelet conversion method. The method can enhance the edge information of the nodi and maintain the edge information thereof better so as to improve the accuracy of judging the properties of the nodi.