390 results about "Contourlet" patented technology

The invention provides a compressed sensing theory-based reconstruction method of a magnetic resonance random sampled K space data image. The reconstruction method applies a contourlet conversion and iterative soft thresholding method to realize reconstruction of a magnetic resonance image. The method comprises the following steps: collecting K space data in a magnetic resonance image scanner according to a preset observation matrix phi to generate a measurement value, and keeping y; acquiring y from a coil of the magnetic resonance image scanner, and transmitting y to a computer; and finallyconstructing a same phi, constructing any orthogonal transformation psi, and recovering from y by adopting a compressed sensing theory-based magnetic resonance random sampled K space data image reconstruction method according to reconstruction. According to the method, scanning time is saved, quick imaging is realized, high-quality reliable image information is provided to medical nuclear magnetic resonance imaging detection, and solid theoretical and practical foundation is established for further development and large-scale popularization and application of the medical imaging detection technology.

Age-estimation of a face of an individual is represented in image data. In one embodiment, age-estimation techniques involves combining a Contourlet Appearance Model (CAM) for facial-age feature extraction and Support Vector Regression (SVR) for learning aging rules in order to improve the accuracy of age-estimation over the current techniques. In a particular example, characteristics of input facial images are converted to feature vectors by CAM, then these feature vectors are analyzed by an aging-mechanism-based classifier to estimate whether the images represent faces of younger or older people prior to age-estimation, the aging-mechanism-based classifier being generated in one embodiment by running Support Vector Machines (SVM) on training images. In an exemplary binary youth / adult classifier, faces classified as adults are passed to an adult age-estimation function and the others are passed to a youth age-estimation function.

The invention discloses a remote sensing image classification method based on an attention mechanism deep Contourlet network, and the method comprises the steps: building a remote sensing image library, and obtaining a training sample set and a test sample set; then, setting a Contourlet decomposition module, building a convolutional neural network model, grouping convolution layers in the model in pairs to form a convolution module, using an attention mechanism, and performing data enhancement on the merged feature map through a channel attention module; carrying out iterative training; performing global contrast normalization processing on the remote sensing images to be classified to obtain the average intensity of the whole remote sensing images, and then performing normalization to obtain the remote sensing images to be classified after normalization processing; and inputting the normalized unknown remote sensing image into the trained convolutional neural network model, and classifying the unknown remote sensing image to obtain a network output classification result. According to the method, a Contourlet decomposition method and a deep convolutional network method are combined, a channel attention mechanism is introduced, and the advantages of deep learning and Contourlet transformation can be brought into play at the same time.

The invention relates to an image fusion of sequence infrared and visible light based on region segmentation. The invention is characterized in that infrared images are segmented into different regions according to the interframe objective change situation and gray scale change degree thereof; the images are dissembled to different frequency domains in the different directions by utilizing non-sub-sampling Contourlet transform; different fusion rules are selected based on the characteristics of different regions in the different frequency domains; and image reformation is carried out for the processed coefficients to obtain the final fusion result. The method takes the information of the characteristics in one of the regions into consideration, so the algorithm is capable of effectively reducing the rate of fusion image error due to the noise and low matching precision, and has stronger robustness.

The invention discloses a blending method of infrared and multi-colored visible light images based on the information of multi-colored transmission and entropy. The process of the method is as follows: three channel images of R, G, and B of the multi-colored visible light images are calculated to obtain a typical value, thus obtaining visible light images with gray scale; the visible light images with gray scale and infrared images are decomposed by adopting non sampling Contourlet conversion; low frequency sub-band coefficient blending rules are constructed based on the infrared images and visible light physical characteristics, bandpass direction sub-band coefficient blending rules are constructed based on the combination of the entropy of local region direction information and region energy, the coefficient of transformation of source images are combined, and the coefficient of transformation combined carries out the non sampling Contourlet conversion to obtain blending image with gray scale; the multi-colored information of the visible light images is transmitted to the blending images by adopting a multi-colored transmission method based on 1 alpha beta color space, thus obtaining the multi-colored blending images. The blending method not only can effectively extract the abundant background information in the visible light images and the target information in the infrared images, but also can keep nature multi-colored information in the visible light images.

The invention relates to an image quality assessment method, in particular to a non-reference image quality assessment method based on information entropy characters, and belongs to the field of image analyzing. The method comprises the first step of carrying out Contourlet conversion on a distorted image to obtain N*M conversion sub-bands, the second step of dividing each conversion sub-band and the unconverted original distorted image, the third step of calculating null domain information entropy and frequency domain information entropy on each block coefficient matrix, and the fourth step of screening the blocking characters and calculating a mean value to obtain the quality character value of each conversion sub-band. The method of a support vector machine and the method of non-reference image quality assessment are utilized for testing on a test set, and quality prediction and assessment are carried out through quality character vectors corresponding to a disaggregated model, an evaluation model and the test set all of which are obtained through training. The non-reference image quality assessment method has the advantages of being high in subjective consistency, small in time complexity and good in university, can be embedded into application systems related to image quality, and has very high application value.

The invention discloses a multi-focus image fusing method based on a dual-channel PCNN (Pulse Coupled Neural Network), which belongs to the technical field of image processing. The method comprises the following steps of: performing NSCT (Non-Subsampled Contourlet Transform) on two images respectively to obtain a plurality of sub-images of different frequencies; fusing by correspondingly adopting the dual-channel PCNN, and determining each band pass sub-band coefficient of a fused image; and performing reverse NSCT to obtain the fused image. Due to the adoption of the multi-focus image fusing method, the defects of the conventional multi-focus image fusing method are overcome, and the fusing effect is improved.

The invention discloses a polarized SAR image classification method based on a nonsubsampled contourlet convolutional neural network, and mainly at solving the problems that influence of speckle noises is hard to avoid and the classification precision is low in the prior art. The method comprises the steps that a polarized SAR image to be classified is denoised; Pauli decomposition is carried out on a polarized scattering matrix S obtained by denoising; image characteristics obtained via Pauli decomposition are combined into a characteristic matrix F, and the characteristic matrix F is normalized and recorded as F1; 22*22 blocks surrounding the F1 are taken for each pixel point to obtain a block based characteristic matrix F2; a training data set and a test data set are selected from the F2; the nonsubsampled contourlet convolutional neural network is established to train the training data set; and the trained nonsubsampled contourlet convolutional neural network is used to classify the test data set. The polarized SAR image classification method improves the expression capability and the classification precision of the features of the polarized SAR image, and can be used for target identification.

The invention discloses a method for segmenting HMT images which is based on the nonsubsampled Contourlet transformation. The method mainly solves the problem that the prior segmentation method has poor area consistency and edge preservation, and comprises the following steps: (1) performing the nonsubsampled Contourlet transformation to images to be segmented and training images of all categories to obtain multi-scale transformation coefficients; (2) according to the nonsubsampled Contourlet transformation coefficients of the training images and the hidden markov tree which represents the one-to-one father and son state relationship, reckoning the model parameters; (3) calculating the corresponding likelihood values of the images to be segmented in all scale coefficient subbands, and classifying by examining possibility after integrating a labeled tree with a multi-scale likelihood function to obtain the maximum multi-scale; (4) updating category labels for each scale based on the context information context-5 model; and (5) with the consideration of the markov random field model and the information about correlation between two adjacent pixel spaces in the images to be segmented, updating the category labels to obtain the final segmentation results. The invention has the advantages of good area consistency and edge preservation, and can be applied to the segmentation for synthesizing grainy images.

The invention discloses a morphology and PCA (principal component analysis) based contourlet fusion method for infrared and visible light images. The method includes: firstly, subjecting two images to morphology-hat transformation respectively and subjecting the two obtained images to contourlet decomposition to form a high-frequency image and a low-frequency image respectively; then, fusing the low-frequency image by a PCA method; fusing the high-frequency image by means of average gradient adaptive weighted processing; reconstructing a fused image by means of contourlet reverse transformation. The morphology and PCA based contourlet fusion method for the infrared and visible light images is used for image fusion of the infrared and visible light images, so that the fused image is high in contrast ratio, rich in information and clear in texture detail.

The invention provides an infrared image and visible image fusion method. The method comprises the following steps of: firstly decomposing an infrared image and a visible image by adoption of non-subsampled double-tree contourlet transformation NSDTCT so as to obtain a low-frequency sub-band and a high-frequency sub-band; constructing a fused sub-band coefficient; in low frequency, calculating local area energy, and replacing a low-frequency sub-band coefficient in fuzzy logic (constructed by a fuzzy membership and a Gaussian function) by the local area energy to calculate a weight coefficient, so as to calculate a fused low-frequency sub-band coefficient; in high frequency, taking pixel definition and significance as fusion rules externally input by a pulse granted cortex model SCM so asto calculate a fused high-frequency sub-band coefficient; and finally obtaining a fused image via inverse NSDTCT. Compared with the traditional image fusion method, the method subjectively and objectively obtains relatively ideal effects in infrared image and visible image fusion, and provides a new thought for infrared image and visible image fusion.

The invention discloses a multi-focus image fusion method based on non-subsampled Contourlet transform and a depth information incentive PCNN (Pulse Coupled Neural Network). The method comprises the following steps of generating a low-frequency sub-band image and a series of high-frequency sub-band images after carrying out multi-scale and multidirectional non-subsampled Contourlet transform on input multi-focus source images; adopting edge information energy based on sub-band coefficients to low-frequency sub-bands to obtain low-frequency sub-band coefficients and adopting a modified PCNN model to high-frequency sub-bands to determine each band-pass sub-band coefficient; and lastly, obtaining a fused image through non-subsampled Contourlet inverse transform. The modified PCNN is mainly embodied in that a factor combined with image depth information through adopting SML (Sum-Modified-Laplacian) capable of describing an image direction and texture information well is taken as input of a model, and most of PCNN-based algorithms take pixel gray values as model input items. The method can be well applied to the field of image fusion, and an experimental result shows that a fusion result which more conforms to an eye vision rule can be provided from both the objective evaluation index and the subjective vision effect.

The invention discloses a method for extracting roads from a remote sensing image, which belongs to the technical field of image processing and solves the problem that the existing technology is not precise in detection and positioning of roads, and has a large number of false targets and bad continuity. The specific realization process comprises the following steps of: firstly implementing pretreatments including adaptive histogram equalization and Frost de-noising on the input images; then implementing three layers of non-sub-sampled contourlet transform thereon, decomposing each layer into eight directions, extracting the model maximum value of each direction sub-band of the first layer and the second layer as the linear characteristic vectors of roads; clustering the obtained characteristic vectors by using fuzzy C means clustering algorithm to obtain the initial extraction results of roads; and finally implementing non maximum value inhibition and road post treatment based on the spatial relationship to the initial extraction to obtain the final road extraction result. The invention has the advantages of accurate road positioning, good integrality, low calculation complexity and no need of training and learning, and is used for analysis and processing of the remote sensing image.

The invention discloses a multi-sensor image adaptive fusion method based on non-lower sampling contourlet, which mainly aims at solving the problem that the existing image fusion method easily causes distortion in fused images. The fusion process comprises the steps that: the source images are respectively subject to a non-lower sampling contourlet decomposition to obtain low pass subbands and high-frequency directional subbands of the source images in various scales; the Fibonacci method is applied to the obtained low pass subband to find out the optimal low-frequency subband fusion weight; a fusion is adaptively carried out by utilizing the optimal low-frequency subband fusion weight to obtain the low pass subband of the fused image; a fusion is conducted by hiring the high-frequency fusion formula to fuse the high-frequency directional subbands of the source image in various scales so as to obtain the high-frequency directional subbands of the image in various scales; finally, an NSCT inverse transformation is applied to the low pass subbands and the high-frequency directional subbands of the image to be fused to obtain the fused image. The method has the advantages of smoothness, clarity and rich detailed information of the fused image, and thus can be applied to the pre-treatment of remote sensing images and aerial images.

The invention provides a visible light and infrared image fusion algorithm based on non-subsample contourlet transform (NSCT) domain bottom layer visual features.Firstly, visible light and infrared images are subjected to NSCT, high and low frequency subband coefficients of the visible light and the infrared images are obtained, then phase equalization, neighborhood space frequency, neighborhood energy and other information are combined, the pixel active levels of the low frequency subband coefficients are comprehensively measured, fusion weights of the low frequency subband coefficients of the visible light and infrared images are obtained respectively, and therefore low frequency subband coefficients of fusion images are obtained; the pixel active levels of the high frequency subband coefficients are measured through the combination of phase equalization, definition, brightness and other information, fusion weights of the high frequency subband coefficients of the visible light and infrared images are obtained respectively, then high frequency subband coefficients of the fusion images are obtained, finally, NSCT reverse transformation is utilized, and final fusion images are obtained.Detail information of source images can be effectively reserved, and meanwhile useful information of the visible light images and the infrared images is synthesized.

The invention relates to an infrared image and visible-light image fusion method based on salient region segmentation. The method comprises the steps that (1) region segmentation is conducted on an infrared image and a visible-light image through a saliency detection method; (2) joint region expression is conducted on the images after region segmentation; (3) multi-scale and multi-direction contourlet decomposition is respectively conducted on the infrared image and the visible-light image; (4) a low-pass sub-band fusion coefficient and a band-pass sub-band direction fusion coefficient are determined according to a fusion rule corresponding to different region selections of the images after joint region expression; (5) coefficient reconstruction is carried out to acquire a fusion image of the infrared image and the visible-light image. According to the method, thermal radiation characteristics of an infrared image and scene detail characteristics of the visible-light image can be reserved in a fusion result, and accordingly the amount of information contained in the fusion image is increased and the quality of the fusion image is improved.

The invention relates to an image denoising method based on non-local means and multi-level directional images, comprising the steps of: firstly, carrying out preprocessing on an image with noise in an empty space by utilizing a non-local mean arithmetic of a small window and the similarity of the local structure of the image, and mapping the local window to a low-dimensional space to improve the speed of the arithmetic by utilizing principal component analysis (PCA); then carrying out multi-scale multi-direction sparse decomposition on the preprocessed image through NSCT (Non-subsampled Contourlet); eliminating low-frequency noise by adopting Wiener filtering in an NSCT transform domain by utilizing the neighborhood statistical property of the coefficient; and obtaining the denoised image through NSCT inverse transformation. The method improves the quality of the denoised image, provides more comprehensive and accurate targets and background information, and achieves relatively ideal denoising effect. The invention has wide application prospect in systems in the military field and the non-military field such as optical imaging, target detection, safety monitoring, and the like.

The invention provides a small infrared aerial target detection method based on non-downsampling contourlet transformation. The method includes the following steps of 1, non-downsampling contourlet transformation, wherein non-downsampling contourlet transformation first-level decomposition is performed on a small infrared target image, and a band pass sub-band is discomposed into four-direction high-frequency sub-bands; 2, background suppression, wherein low-frequency influences are removed, and thresholding processing is performed on a high-frequency portion; 3, coefficient mapping, wherein coefficients left by the four-direction high-frequency sub-bands are mapped to a gray level space in a linear mode; 4, high-frequency image segmentation, wherein four-direction high-frequency sub-band images are segmented into binaryzation images; 5, binary high-frequency image noise reduction, wherein small bright noise points in the binary high-frequency images are eliminated; 6, detection of related small targets in dimension, wherein the four-direction high-frequency sub-band images get along with each other to obtain a small target single-frame detection result; 7, small target sequence detection, wherein comprehensive vote is performed on multi-frame images to intercept and capture small targets. According to the method, the problem of small aerial target detection under the complicated infrared background is solved.

The invention relates to a high-robustness digital watermarking method making a color QR code embedded in a color image. Based on a color QR code, a Contourlet transformation technology, a DCT (discrete cosine transformation) technology and an SVD (singular value decomposition) transformation technology, the color QR code is adopted as original watermark information. The method includes the following steps that: a color host image and the color QR code are water-marked in an RGB space so as to be subjected to three-channel decomposition, and three-channel watermarks obtained after the decomposition are subjected to chaotic encryption separately; Contourlet transformation is performed on each channel image of the color host image, and DCT is performed on the low-frequency components of the channel images, and the direct-current components of the channel images are adopted to construct new matrixes, SVD transformation is performed on the new matrixes, so that singular value matrixes can be obtained; and the watermarks are embedded into corresponding channels, SVD inverse transformation, inverse DCT, and Contourlet inverse transformation are carried out, so that the color image containing the watermarks can be obtained. Compared with an existing algorithm for embedding color watermarks into a color image, the high-robustness digital watermarking method of the invention has the advantages of high imperceptibility and high robustness.

The invention discloses a degraded reference image quality evaluation method based on Contourlet transformation. The method utilizes Contourlet transformation to catch the direction information of an image, effectively extracts the average value and the standard deviation of a transform coefficient matrix of an image Contourlet transformation domain sub-band reflecting the texture structure information of a reference image and a tested image as statistical attribute values of the image; by comparing the similarity of the texture structure information between the reference image and the tested image, the quality value of the tested image is finally obtained; image quality evaluation carried out by utilizing the similarity of the texture structure information between images does not require the reference image of very good visual quality, and the result of image quality evaluation purely reflects the similarity between the reference image and the tested image, namely, the result of evaluation can objectively reflects the influences of image processing or compression algorithm on the change of image quality.

The invention belongs to the application fields of seismic data oil gas predication, oil gas resource exploration and signal processing, and provides a Contourlet transformation based seismic multi-attribute fusion method. Effectiveness of description attribute information is improved by means of expression of multiresolution, multidirection, localization and anisotropy of signals to make full use of existing various attribute information, accuracy in seismic target calibration is improved, and accordingly reservoir predication and fluid recognition can be completed more accurately.

The invention relates to a fusion method of SAR (Synthetic Aperture Radar) images and visible light images on the basis of NSCT (Non Subsampled Contourlet Transform). The fusion method is characterized by comprising the following steps of: firstly, carrying out NSCT decomposition on the SAR images and the visible light images respectively; then adopting different fusion rules to carry out fusion treatment on NSCT low-frequency and high-frequency subband coefficients, wherein according to the decomposition coefficient characteristics of noise and signals in an NSCT domain, carrying out hard-threshold denoising on the NSCT high-frequency subband coefficient of the SAR images under the maximum decomposition scale, then respectively adopting different fusion rules to carry out fusion processing on the NSCT high-frequency subband coefficient under the maximum decomposition scale and other decomposition scales by adopting the coefficients with threshold processing as the basis; and finally,carrying out NSCT reverse transformation on the fused NSCT coefficients and obtaining fused images. The fusion method takes denoising as the basis of the fusion rule design, considers noise suppression while fusion treatment is carried out, is simple and easy to operate, can be used for obtaining a good fusion effect and is especially more applicable to the SAR images and the visible light imageswith serious spot and noise pollution.

The invention discloses a contourlet-transform-based anti-geometric attack digital image watermarking method, mainly solving the problem of poor robustness of the existing similar method. The invention is technically characterized in that the method completes watermark synchronization through a geometric moment method to realize watermark embedding and extraction in a contourlet transform domain, that is, to calculate a first-order origin moment containing a watermark image I when the watermark is embedded, and reserve the first-order origin moment for the estimation of the geometric attack parameters during extraction; during watermark extraction, to estimate the geometric transformation parameters through the attacked first-order origin moment and a second-order center moment containing watermarks, and to recover the attacked watermarks through contourlet transform. The method of the invention has the advantages of strong anti-geometric attack ability, good watermark extracting effect and self-positioning watermark embedded location, which can be used for safety protection of digital multimedia product copyright.

The invention discloses a high-resolution remote sensing image registration method with control points distributed in an adaptive manner. By the use of the high-resolution remote sensing image registration method, a multi-scale J-image is introduced into image registration. A self-adaptive control points extracting method based on a partitioning strategy is provided to extract the control points of the multi-scale J-image image, thus the defect that the control points only sense specific directional high-frequency information is overcome. A self-adaptive control points extracting strategy is defined so as to limit control point distribution. The control points are subjected to multi-scale matching by NMI (normalized mutual information measure), allowing registration function smooth. Geometric correction is realized by adopting Delaunay triangle local transformation. In the following text, basic principle and key steps of the algorithm are introduced, and three remote-sensing image groups of different types are subjected to experiment and analysis and comparison experiments with various registration methods based on wavelet transform and NSCT (non-subsampled contourlet transform).

The invention discloses a neural network based sonar image super-resolution reconstruction method which is used for performing super-resolution reconstruction on a sonar image r to be reconstructed at super resolution. The method comprises the following steps of: performing nonsubsampled contourlet decomposition and neural network training on a high-resolution sonar image and four degraded sample images; performing cubic interpolation on the sonar image r to be reconstructed at super resolution and taking the interpolated image as a high-resolution low-pass sub-band coefficient; and performing nonsubsampled contourlet decomposition on the sonar image r again, inputting the sub-band coefficient of each band-pass direction of the sonar image r to be reconstructed at super resolution into a trained neural network to acquire the high-resolution sub-band coefficient of each band-pass direction, and finally performing nonsubsampled contourlet decomposition to acquire a super-resolution reconstructed sonar image R. The sonar image reconstructed at super resolution has a better edge, a detail keeping effect and a better visual effect and contributes to processing such as sea bottom survey, subsequent underwater target positioning and recognizing and the like.

The invention discloses a method for identifying vehicle brand types on the basis of a cascading integrated classifier, and provides a vehicle brand type identification method based on a cascading integrated classifier. A vehicle face image is positioned and segmented; vehicle face image features are extracted respectively by use of a gradient orientated histogram and Contourlet transformation, and a first-stage integrated classifier for vehicle brand type identification is constructed; and a multilayer nerve network is taken as a sub-classifier of a second-stage integrated classifier, and a vehicle brand type which a vehicle belongs to is determined through the first-stage integrated classifier and the second-stage integrated classifier. Through such steps, the precision and the reliability of an identification system are greatly enhanced, and an important parameter basis is provided for effectively solving such traffic problems of vehicle plate duplication, traffic offences and the like.

The invention discloses a method of detecting flood disaster changes through object-level high-resolution SAR (synthetic aperture radar) images and aims to solve the problem that a false target possibly existing in an SAR image and similar to a water region in geometrical characteristics and spectral characteristics causes great 'pseudo-changes' and causes difficulty and disturbances to flood disaster change detection. Each time-phase image is subjected to contourlet transform; image noise is suppressed at the premise of keeping image edge characteristics, an optimal decomposition scale is selected through simple sample training, and the position of a mark point in a possible area of a water body is quickly acquired through block histogram statistics, on the optimal decomposition scale. Contour information of the water body is acquired through a mark-point-based watershed segmentation and region merging strategy, and interference of the false target is further eliminated through discrimination rules based on multiple features. Finally, water contours extracted from multiple time-phase images are directly compared according to registration results, and a region having water body changes is obtained.

The invention discloses an image fusion method based on an imaging mechanism and non-sampled Contourlet transform. The method comprises the following steps of: first, carrying out the multi-scale and multi-direction decomposition to a source image and obtaining sub-band coefficients of different frequency domains via the non-sampled Contourlet transform; next, building a fusion rule based on the direction vector norm to the low frequency sub-band coefficients and building a fusion rule based on the union of the local directional contrast and the direction vector standard deviation to the band-pass direction sub-band coefficients; then, respectively combining the low frequency sub-band coefficients of the source image and each band-pass direction sub-band coefficients according to the built fusion rules and obtaining the non-sampled Contourlet transform coefficients of the fusion images; finally, rebuilding the fusion images via the non-sampled Contourlet inverse transform. The invention has the advantages of good fusion effect, little infection of the registration error on the fusion capability and being capable of effectively avoiding transferring the noise to the fusion images, and is applicable to the subsequent treatments and the image displays of the imaging systems.

The invention discloses a synthetic aperture radar (SAR) image change detection difference chart generation method based on contourlet transform. A realization process mainly comprises the following steps of: firstly, constructing a logarithmic ratio image and a mean ratio image on two SAR images which are preprocessed and obtained at different time and in a same region; generating corresponding Contourlet coefficients by Contourlet transform processing; respectively calculating the coefficients of the two images in a high-frequency mode and a low-frequency mode according to different fusion rules; performing inverse Contourlet transform on the fused Contourlet coefficients to generate a change difference chart. The different characteristics of high frequency and low frequency are respectively extracted by the mean ratio image and the logarithmic ratio image, and complementation information of the source images is fully utilized by the image fusion based on the Contourlet transform, so that the SAR image change detection can have a better result, the detection error ratio is low, the image noise is inhibited, and the analysis precision is improved. Compared with other difference chart generation methods, the method disclosed by the invention is high in noise inhibition and good in edge maintenance, and can reserve change information to the maximum extent.

The invention provides a pseudo-acoustic curve construction method based on nonsubsampled Contourlet transformation. The method comprises the following steps: reading logging data; carrying out pretreatment on read logging data curves, wherein the pretreatment comprises curve correction, data standardization and normalization processing; carrying out multistage nonsubsampled Contourlet transformation on the logging data curves respectively, carrying out weight fusion according to a coupling degree between the logging data curves so as to generate a high frequency fusion curve and a low frequency fusion curve; carrying out nonsubsampled Contourlet inverse transformation on the high frequency fusion curve and the low frequency fusion curve so as to generate a pseudo-acoustic curve.