92 results about "Shearlet" patented technology

The invention belongs to an infrared dim-small target detection method based on shear wave conversion in the infrared image processing technology. The method comprises the following steps: processing an original infrared image by employing nonsubsampled Laplace pyramid conversion and a Shearlet filter in succession to obtain high frequency information graphs of various directions under different scales, inhibiting background and noise interference information, enhancing target information and extracting a dim-small target. According to the invention, the nonsubsampled Laplace pyramid conversion and a Shearlet filter are employed to process the original infrared image, through same scale and different scales fusion processing of the obtained the high frequency information graphs, the interference information is inhibited, the target information is enhanced, and the high frequency information graphs are subjected to segmentation to obtain a clear dim-small target graph; thereby the method has the characteristics of a short detection processing flow, small data processing amount, short processing time, capability of effectively raising performance of detecting the infrared dim-small target and obviously distinguishing a target and a complex background in the image, a good effect and the like.

The invention discloses a welding defect extraction method, which belongs to the technical field that a welding technology and a digital image processing technology are crossed. According to the method, an original welding image is dissected by NSST (non-subsampled shearlet transform); for a roughly approximate low-frequency component capable of embodying a defect, the rough region of the detect is extracted by a PCNN (pulse coupled neural network); then, inverse NSST is carried out to the low-frequency component and a high-frequency component subjected to background suppression to obtain a high-frequency characteristic image; after the high-frequency characteristic image is subjected to coarse segmentation, the outline of the defect is optimized by an improved CV (Chan-Vese) model to obtain the fine edge of the defect; and finally extracted results are blended to obtain a finally-extracted defect. The invention also discloses a welding defect detection method adopting the welding defect extraction method. A welding defect structure obtained by the method disclosed by the invention has the advantages of more integral structure and clearer detail and outline, and a more reasonable and accurate reference can be provided for further processing the defect.

The present invention provides a visible-light polarization image fusion method based on non-subsampled shearlets. At first, Strokes calculation is carried out for different polarization images captured by a multi-detector camera at the same time to obtain a polarized degree image, a polarized angle image and a light intensity image by which a polarization feature image of a target is extracted. Then, non-subsampled shearlets transform (NSST) decomposition is performed on the polarization feature image and the light intensity image separately. High and low-frequency fusion coefficients are determined separately in a frequency domain according to window energy and an average value. An initial fusion image is reconstructed by using NSST inverse transform. Finally, the initial fusion image is subjected to target enhancement to obtain final fusion image output. Compared with a conventional target detection method utilizing multi-scale decomposition, the method provided by the present invention increases means for polarization feature extraction and target enhancement, effectively increases details of the fusion image, improves target and background contrast, highlights polarization properties of the target, improves the ability of scene perception and target detection, and is suitable for a target detection system.

The invention discloses a shearlet transform and fast bilateral filter image denoising method, comprising the following steps: 1) using a noise imaging system to collect the envelope signal of a noise image, and establishing a medical ultrasonic image model; 2) performing the step 1 ) The medical ultrasound image model obtained after the logarithmic transformation utilizes a pyramid filter bank to perform multi-scale and multi-directional decomposition, 3) the two-dimensional discrete shearlet transformation coefficient of the high-frequency part in each sub-band image obtained in the step 2) Carry out threshold method contraction processing; 4) Utilize fast bilateral filter to carry out filter processing to the shearlet coefficient of step 2) middle and low frequency part; 5) Perform shearlet inverse transformation processing to all coefficients after step 3) and step 4) processing, obtain Noised medical ultrasound images. The introduction of the fast bilateral filter in the present invention can not only improve the denoising performance but also greatly improve the processing efficiency.

The invention discloses a natural image denoising method based on a non-local mean value of a shearlet region, which mainly solves the problem that the traditional non-local mean value method has poor denoising effect of a natural image corroded by high noise. The method comprises the following implementation steps of: inputting a test image, and adding gaussian white noise with the noise standard deviation of 50; decomposing the image into three layers by utilizing a Laplacian pyramid method, wherein denoising treatment is carried out on the first layer by using a non-local mean value method, the second layer and the third layer are respectively decomposed into four groups of shearlet coefficients by using a shearlet directional filter group firstly, then estimation of a beta value is carried out on each group of shearlet coefficients, and then the denoising treatment of the non-local mean value method under a general Gauss model is carried out on each group of shearlet coefficients; and reconstructing a denoising result to obtain a final denoising result. The invention has the advantages of favorable denoising effect for the natural image corroded by high noise, can restore the original characteristics of the image and be used for variation detection and pretreatment of the image when an object is identified.

The invention relates to a method for fusing Shearlet domain multi-spectral and full-color images based on spectral characteristics. The method comprises the steps of (1) performing IHS transformation for a multi-spectral image; (2) performing Shearlet transformation and decomposition for the BIMFs and the full-color image to obtain a low-frequency sub-band and a plurality of high-frequency sub-band; (3) building a fusion rule and selecting fusion coefficients, namely, decomposing the low-frequency coefficients through laplacian pyramid, selecting the low-frequency fusion coefficients by the comparing and bigger selection method, and fusing the corresponding high-frequency coefficients according to the fusion rule based on PCNN (Pulse Coupled Neural Network); (4) performing IHS reverse transformation to obtain the final fused image. With the adoption of the fusion algorithm, the spectrum information of the images is protected while the space details expression capacity is improved, and thus the fusion effect is improved.

An infrared and visible light video fusion method based on moving target detection belongs to the technical field of video fusion. A moving target is detected through low-rank sparse representation, the target is extracted using the low-rank and sparse time slices along X-T and Y-T planes, the integrity of the target detected is ensured by spatial information, and the robustness and accuracy of detection are improved. Then, the structure information of an image can be better described through non-subsample shearlet transform with a good fusion effect. The method has the advantages of high computational efficiency, unlimited filtering direction, translational invariance, and the like, and improves the fusion effect. In addition, the low-frequency sub-band coefficients are fused according toa sparse representation rule, the high-frequency sub-band coefficients are fused according to a weighting rule taking a target saliency map as a weighting factor, and a better fusion effect is achieved. Therefore, the infrared and visible light video fusion method based on moving target detection presented by the invention is more robust, and has a better fusion effect.

The invention discloses an image denoising method based on shearlet transformation and Wiener filtering. The method comprises the following steps of: (1) source image input; (2) symmetric extension; (3) shear transformation; (4) wavelet packet decomposition; (5) Wiener filtering; (6) inverse wavelet packet transformation; (7) inverse shear transformation; (8) inverse symmetry transformation; (9) image fusion; and (10) denoised image output. By the image denoising method, the defect that the anisotropic information of an image cannot be expressed very well by the wavelet transformation in the prior art is overcome; the problem that a denoising effect is non-ideal because coefficients are subjected to the same processing in different directions by using a single threshold is solved; the advantages that the shearlet transformation has multi-directionality, the output of a filter can be adjusted by the Wiener filtering according to the regional variance of the image and the like are utilized; and therefore, detailed information of the image can be analyzed more accurately in high-frequency coefficients in different directions of the image. Finally, a high-quality denoised image is obtained.

The invention discloses a denoising method for a seismic signal based on the Shearlet transform, which comprises the following steps: 1, two-dimensional seismic section data is read; 2, the two-dimensional seismic section data S is expanded into a square matrix S1 of which the length and the width are odd; 3, a frequency domain orientation filter set is built; 4, multiplying operation is performed on all the transform matrixes and a signal vector respectively, and two-dimensional Fourier inverse transform is performed, so that Shearlet coefficients C<i,j> in various directions and dimensions are obtained; 5, threshold value processing is performed; 6, Shearlet inverse transform is performed on the Shearlet transform coefficients subjected to threshold value processing to obtain the denoised signal. According to the denoising method, the Laplace decomposition is performed on the seismic signal with noise, and then filtering processing is performed by utilizing a Shearlet function to obtain the corresponding Shearlet coefficient; the noise signal is filtered through threshold value processing, and the denoised signal is recovered through sampling Shearlet transform under the inverse condition, so that the better denoising effect is obtained, and the method has an excellent practical value.

The invention provides a Shearlet transformation-based seismic data reconstruction method. According to the method, data comprising missing seismic traces can be reconstructed based on Shearlet transformation and sparse signal theories. Observation data is input, positions of the missing traces are analyzed, and a sampling matrix of the data is obtained; a measurement matrix and a Shearlet transformation matrix are determined; a model matrix is obtained based on the measurement matrix, the sampling matrix and the Shearlet transformation matrix; a most sparse solution is obtained based on a sparse promotion inversion algorithm; reverse sparse transformation is carried out, and reconstructed seismic data is obtained; the reconstructed seismic data is output and saved. Random missing data can be effectively recovered via the Shearlet transformation-based seismic data reconstruction method, complete and regular reconstructed data can be obtained, and strong technical support can be provided for subsequent seismic data processing and interpretation.

The invention discloses a multi-sensor image fusion method on the basis of an IICM (improved intersecting cortical model) in an NSST (nonsubsampled shearlet transform) domain. The multi-sensor image fusion method includes: step 1, building the IICM; step 2, inputting all the multi-sensor source images to be fused to perform NSST and acquiring one low-frequency sub-band image and multiple high-frequency sub-band images from each multi-sensor source image to be fused; step 3, importing all the high-frequency sub-band images and the low-frequency sub-band images into the IICM to complete fusion of the low-frequency sub-band images and fusion of the high-frequency sub-band images; step 4, performing NSST inverse transformation on the high-frequency sub-band images and the low-frequency sub-band images which are fused finally to acquire the final fusion image. The improved model, namely the IICM is provided on the basis of the classical ICM, multi-resolution analysis of the multi-sensor source images and reconstruction of the finally fused image are completed by means of NSST, and problems about fusion of the multi-sensor images are solved reasonably since the IICM is utilized for realizing fusions of the high-frequency sub-band images and the low-frequency sub-band images.

The invention relates to an image denoising method based on a Shearlet contraction and an improved TV model, wherein a TV-denoising model is improved and a novel mixed denoising method by combining the Shearlet contraction is proposed. The method organically combines the sparse representation capability of Shearlet for a high dimension function with the protection capability of the TV-denoising model for an edge, wherein the method obtains a first-denoising image through a hard threshold function contraction, and then improves fidelity terms of a total variation model, and then makes a second denoising of the false Gibbs effect of the first-denoising image by combing the improved total variation model. On the prerequisite of protecting important information such as edges, etc., the method effectively inhibits the false Gibbs oscillation caused by the Shearlet contraction, and realizes a better visual effect and a lower computation complexity.

The invention discloses an SAR image segmentation method on the basis of the HMT model in the Shearlet domain, which pertains to the technical field of image processing and mainly aims at solving the problem that the application of the traditional multi-scale geometrical analysis in SAR image segmentation is easy to result in poor regional uniformity and disorder edges. The segmentation process comprises the steps of extracting feature areas (I0, I1, and the like, and IC) in the SAR image to be segmented, calculating the Shearlet transformation coefficients (S0, S1, and the like, and SC) of the feature areas, utilizing the EM algorithm to obtain the HMT model parameter set (Theta1, Theta2, and the like, and ThetaC) in the Shearlet domain of various feature areas, carrying out Shearlet transformation to the SAR image to be segmented to obtain an image coefficient S, utilizing the feature coefficients (S0, S1, and the like, and SC) to calculate likelihood values (Lhood, Lhood, and the like, and Lhood) corresponding to the SAR image coefficient S in each scale, calculating initial segmentation results (MLseg, MLseg, and the like, and MLseg) of the likelihood values in each scale according to the maximum likelihood rule, carrying out fusion to the initial segmentation results by maximizing a posteriori probability criterion and taking the fused image of the scale at the first level as a final segmentation result. The method has the advantages of high convergence rate, good regional uniformity of segmentation result and completely retained information, and can be applied to SAR image target identification.

The invention discloses a different-source image fusion method based on salient target region extraction and NSST (Non-subsampled Shearlet Transform). The method comprises the following steps that: firstly, extracting the salient target region of an infrared image; carrying out registration on infrared and visible images to obtain an affine transform matrix; through the affine transform matrix, obtaining the position of the salient target region of the infrared image in the visible light image; carrying out NSST on the infrared and visible light image; utilizing different methods to carry outfusion on the NSST results of a target area and a non-target area of two images; and finally, through NSST inverse transform, obtaining a final fusion result. By use of the method, two different-source images of the infrared light and the visible light can be effectively fused, a fusion result can better highlight and keep the target, and the background detail information of the non-target area inthe visible light image can be more favorably kept.

The present invention discloses a cement notch groove pavement image noise reduction enhancement and crack feature extraction method aiming at the problems that the pavement contrast is too low causedby external factors and the payment spots and notch groove autointerference caused by pavement materials. The method comprises the following steps of: employing an improved local adaptive contrast enhancement algorithm to enhance image contrast after graying processing of an original cement pavement image; employing translation invariance Shearlet transform denoising algorithm of an improved P-Mmodel to remove speckle noise caused by pavement materials; employing a cement notch groove pavement image smoothing model established based on an unidirectional total variation UTV model to the imageafter denoising to remove a pavement notch groove influencing feature extraction; and combining a connected domain mark method, a projection method and a rectangular frame method to extract a crack type determination method and a crack feature calculation method to achieve digital description of crack features. The cement notch groove pavement image noise reduction enhancement and crack feature extraction method is systematic and comprehensive, small in calculated amount and easy to apply.

The invention discloses an image fusion method based on shift-invariant shearlets and a stack autoencoder. The implementation steps of the image fusion method includes the following steps that: shift-invariant shearlet transformation is utilized to decompose an image to be fused into a low-frequency subband coefficient and a high-frequency subband coefficient, wherein the low-frequency subband coefficient reflects the basic contour of the image and is fused by using weight averaging, and the high-frequency subband coefficient reflects the edge and texture information of the image. The present invention provides a stack autoencoder feature-based fusion method. According to the stack autoencoder feature-based fusion method, a sliding block division method is adopted to divide a high-frequency subband into different blocks; a stack autoencoder network is trained with the blocks adopted as input; the trained network is adopted to encode the blocks, so that features can be obtained; the features are enhanced through using a spatial frequency, so that an activity measure can be obtained; the fusion of the blocks of the high-frequency subband coefficient is performed through using a principle that the larger numerical value of the activity measure is adopted; after all the blocks are fused, the high-frequency subband can be obtained through using inverse sliding window transformation; and a fused image can be obtained through inverse shift-invariant shearlet transformation. Compared with a traditional fusion method, the fusion method of the invention can better preserve edge and texture information in an original image.

The invention relates to a novel noise image fusion method based on a cycle spinning (CS) technology and Contourlet domain context hidden Markov model (CS-CT-CHMM). First, a source image containing a certain level of Gauss white noise is de-noised by use of the CS-CT-CHMM, wherein the structure of the context is calculated based on neighborhood entropies of a father node, two nearest cousin nodes and a current node of a Contourlet decomposition coefficient; then, Shearlet transform is performed on the de-noised image, and a fusion rule is designed by adopting a weighted average fusion strategy for low-frequency sub-band coefficients and adopting an improved pulse-coupled neural network (IPCNN) model for high-frequency sub-band coefficients; and finally, reverse Shearlet transform is performed to obtain a fused image. The CS-CT-CHMM de-noising method can protect detailed information of the image and suppress the pseudo Gibbs effect while effectively removing noise, while the fusion method combining Shearlet transform and IPCNN is advantageous in terms of image contrast enhancement and the amount of information. Compared with the traditional fusion method, the quality of the fused image is greatly improved.

The invention discloses a color image tamper detection method based on local quaternion index moment through which algorithm time complexity can be greatly reduced and a replication tamper area can be accurately detected. Self-adaptive super-pixel partitioning is performed on an image to be detected after Gaussian smoothing filtering preprocessing by utilizing a super-pixel segmentation algorithm based on entropy rate with combination of non-subsample Shearlet transform; the quaternion index moment is utilized to express Sifer feature point local area features and similar block matching is performed on all the Sifer feature point local area features of each super-pixel block; a suspected tamper area is determined by utilizing the matching feature points in the similar blocks obtained in the step 2; and the replication tamper area is marked by utilizing morphological filtering operation.

The invention relates to a multi-channel satellite cloud picture fusion method based on Shearlet conversion and belongs to the field of weather prognoses. Firstly, two registered satellite cloud pictures are subjected to Shearlet conversion to acquire a low-frequency coefficient and a high-frequency coefficient; secondly, a low-frequency Shearlet domain part is divided again through a Laplacian pyramid, the mean value of the top layer of the Laplacian pyramid is worked out, and then reconstruction of other layers with large gray-level absolute values of the Laplacian pyramid is carried out; in the high-frequency Shearlet domain part, the information entropy, average gradient and standard deviation of each high-frequency sub-picture are worked out and are then subjected to normalization processing, the product of every group of three processed values is worked out, and the sub-picture with the large product serves as a fused sub-picture; the fused sub-picture is subjected to detail enhancement treatment through a non-linear operator; finally, a final fused picture is obtained through Shearlet inverse transformation. The method can be popularized to fusion of three or more satellite cloud picture to achieve multi-channel satellite cloud picture fusion and acquire high-precision typhoon center positioning results.

The invention discloses a new image fusion method based on shift invariance shearlet transformation (SIST). The method includes the implementation steps of firstly, conducting multi-scale multi-direction decomposition on a source image through SIST to obtain low-frequency sub-band coefficients and high-frequency sub-band coefficients; secondly, reflecting the outline information of the image through low-frequency sub-bands, obtaining local structure descriptors for identifying the image resolution through a local structure tensor singular value decomposition method, making the local structure descriptors serve as movement measure functions in the fusion strategy, and adopting large strategy fusion. High-frequency sub-bands display detail information of the image. The invention provides a new edge strength measurement method, and a multi-strategy fusion rule based on the sigmoid function and edge strength measurement is made and used for high-frequency sub-band fusion. Finally, reverse SIST is conducted on the obtained fusion coefficients to obtain a final fusion image. The method overcomes the defect that edge distortion is easily caused by a traditional image fusion method, and more edge and detail information is reserved through the fused image.

The invention discloses a method for denoising a medical ultrasonic image based on a normal inverse Gaussian model, comprising the following steps: 1) using a noise imaging system to collect the envelope signal of the noise image, and establishing a medical ultrasonic image model; Step 1) The medical ultrasound image model obtained after the logarithmic transformation utilizes a pyramid filter bank to perform multi-scale and multi-directional decomposition, 3) For the two-dimensional discrete shearlet of the high-frequency part in each sub-band image obtained in the step 2) The transformation coefficients are subjected to threshold method adaptive shrinkage processing; 4) use the trilateral filter to filter the shearlet coefficients in the low-frequency part of step 2); 5) perform shearlet inverse transformation on all coefficients after step 3) and step 4) processing After processing, the medical ultrasound image after denoising is obtained. The invention can well suppress the speckle noise, and at the same time can better help doctors to analyze the condition.

The invention relates to a desert seismic exploration random noise eliminating method based on noise modeling analysis, and belongs to the technical field of physical geography. The desert seismic exploration random noise eliminating method comprises the following steps: building a noise-containing synthetic record; building desert seismic exploration random noise model; taking a natural noise function, a near-field human noise function, and a far-field human noise function as excitation functions of the wave function; solving a wave equation with a green function to obtain a noise field of the natural noise, a noise field of a near-field human noise and a noise field of a far-field human noise; overlapping the noise field of the natural noise, the noise field of the near-field human noiseand the noise field of the far-field human noise to obtain a desert area random noise model; and determining a proper filtering method for filtering and denoising by using the random noise model. Through adoption of the desert seismic exploration random noise eliminating method, a shearlet variable threshold denoising method finally determined based on the noise modeling is suitable for eliminating the desert seismic exploration noise; the signal-to-noise ratio of the seismic exploration data is improved; the effective signal can be recovered accurately; and the interpretation of subsequent seismic data is effectively facilitated.

The invention belongs to the field of medical imaging technologies and optical image information processing, and provides a speckle noise filtering method for an optical coherence sectional image. The speckle noise filtering method for the optical coherence sectional image aims to improve visual quality of the image, reduces influences of speckle noises to the image and can carry out filtering noise reduction treatment on the optical coherence sectional image. According to the technical scheme, the speckle noise filtering method for the optical coherence sectional image comprises the following steps: step 1, inputting an optical coherence sectional image f; step 2, carrying out logarithm taking operation on the coherence sectional image; step 3, carrying out shearlet transform on the image to obtain shearlet coefficients after logarithm is taken; step 4, carrying out hard threshold operation on the obtained shearlet coefficients Sj, l, x and y; step 5, carrying out shearlet inverse transform on retained parts after hard threshold operation is carried out to achieve the purpose of filtering; and step 6, outputting the filtered image. The speckle noise filtering method for the optical coherence sectional image is mainly used for processing optical image information.

The invention discloses a feature-level medical image fusion method based on 3D (three dimension) shearlet transform, belonging to the technical field of medical image processing and application. The feature-level medical image fusion method mainly comprises the following steps: 1, performing 3D-D-CSST (three dimensional-discrete-compact shearlet transform) or 3D-DT-CSST (three dimensional dual-tree compact shearlet transform) on two images to obtain transformation coefficient images Ca and Cb; 2, performing image fusion on transformation coefficients to obtain a fusion coefficient Cf; and 3, performing DWT or DTCWT inverse transformation, performing backward shear transformation on the transformed image to obtain a fusion image Vf. According to the feature-level medical image fusion method, the problems that the quality of a fused image is relatively low and information which is partially important and is not remarkable is easily ignored are solved.

The invention, which belongs to the image processing field, puts forward an image segmentation method based on non-down-sampling shearlet conversion and a vector C-V model. According to the method, multi-level decomposition is carried out on a given image by using non-down-sampling shear waves to obtain low-frequency and high-frequency components at different directions; the components are sent to a vector C-V model; a series of evolving curves distributed uniformly are initialized on the overall image; and an obtained energy functional is minimized to obtain a segmentation result. With the method, the segmentation effect is obvious; and objective evaluation quality and the visual effect are good.