139 results about "Imaging problem" patented technology

The present invention can provide solutions to many common imaging problems, such as, for example, unevenly distributed illumination, shadows, white balance adjustment, colored ambient light and high dynamic range imaging. Imaging systems and methods can be provided through a computer (e.g., laptop or desktop) such that the system or method can take advantage of the computer's processing power to provide functionality that goes beyond typical camera. Such an imaging system may include an imaging device, a camera, a light source and a user interface.

The present invention includes a method for parallel magnetic resonance imaging termed Superresolution Sensitivity Encoding (SURE-SENSE) and its application to functional and spectroscopic magnetic resonance imaging. SURE-SENSE acceleration is performed by acquiring only the central region of k-space instead of increasing the sampling distance over the complete k-space matrix and reconstruction is explicitly based on intra-voxel coil sensitivity variation. SURE-SENSE image reconstruction is formulated as a superresolution imaging problem where a collection of low resolution images acquired with multiple receiver coils are combined into a single image with higher spatial resolution using coil sensitivity maps acquired with high spatial resolution. The effective acceleration of conventional gradient encoding is given by the gain in spatial resolution Since SURE-SENSE is an ill-posed inverse problem, Tikhonov regularization is employed to control noise amplification. Unlike standard SENSE, SURE-SENSE allows acceleration along all encoding directions.

A moving target detection imaging method of a dual-channel frequency modulation continuous wave SAR system comprises the steps of recording echo data by a radar system, performing residual video phase removing processing on the echo data, performing Doppler frequency shift compensation on the data, performing phase deviation compensation on two-channel conversion data, and performing subtraction processing on the two-channel data; performing migration correction and direction dechirping processing on an image with clutter cancellation to realize the rough imaging of a target, and detecting the moving target and extracting one by one; performing inverse transformation on the extracted targets to an original data domain, estimating motion parameters to construct a precise direction dechirping function of the moving target, performing residual video phase removing processing, precise direction dechirping processing and distance migration correction, constructing a fuzzy function for compensation, and then performing Keystone transformation to finish the precise image of the moving target. The moving target detection imaging method solves the imaging problem of the fast moving target when motion parameters are unknown under the continuous wave system, so the signal-to-noise rate and the target detection probability are greatly improved.

The invention relates to the active optics phase conjugate method and the imaging device, the optical switch. It constructs the mode separation/integration convertor by the optical waveguide array which is set together at one end, the optics field couples with each other; it is set separately in the other end. It leads the optical wave into the separating optical waveguide by the separation/integration convertor, then to achieve the active optics phase conjugate by adjusting the phase and the swing. It can solves the imaging problem in many limit condition such as big size, high quality, super quick focal variation, long distance and so on. So it can be used in the field of the computer-human conversation, the robot optics, the integrate circuit photoetching, information storage, the military affairs, the energy source, the biology and the light communication network.

The invention belongs to the field of exploration earth physics and specifically relates to an elastic vector reverse time migration imaging method. According to the invention, multi-component seismic data is used as input, an elastic wave propagation operator is utilized to carry out wave field prolongation, an elastic vector seismic wave field is constructed; and a decoupling prolongation equation method is utilized to obtain longitudinal and transverse wave fields of decoupling; vector points of the longitudinal and transverse wave fields with seismic source illumination compensation are utilized to construct elastic wave cross-correlation imaging conditions, and a multi-component seismic data elastic vector reverse time migration imaging result is obtained; and low-frequency noise suppression is carried out on the imaging result, and a final elastic vector reverse time migration imaging result is obtained. The elastic vector reverse time migration imaging method solves the scalar imaging problem of the longitudinal and transverse wave fields, and the imaging precision of elastic reverse time migration under a complex construction is improved.

The invention discloses a method for imaging an actual aperture foresight on basis of incomplete data deconvolution, which mainly solves the problem of imaging the right ahead scene of a flightpath. The method has the processing procedures that: 1) sequence overlap scanning is carried out in a ground monitoring area for obtaining the radar return data; 2) linear interpolation is utilized to expand the radar return data for obtaining the approximate complete deconvolution data; 3) the frequency-domain response of the approximate complete deconvolution data is calculated; 4) a frequency-domain Gaussian lowpass filter is defined; 5) the frequency-domain response of the approximate data is multiplied by the frequency-domain Gaussian lowpass filter, and the frequency-domain response of the processed return data is obtained; 6) the frequency-domain response of an antenna pattern is calculated; 7) a space-invariable filtering algorithm is adopted for calculating a transmission function; 8) the frequency-domain response of a deconvolution post restored image is calculated; and 9) a restored image is gained. The method has the advantage of improving the imaging quality and the detection speed, and can be used for an airborne radar monitoring system in the data processing field for imaging the ground scene of the flightpath.

The invention discloses a real beam scanning radar front-view super-resolution imaging method, which belongs to a method for realizing scanning radar front-view super-resolution imaging by using the total variation functional of the scattering coefficient of an imaging region as de-convolution of priori information. A scanning radar azimuth echo signal is modeled into a result obtained by superimposing noise onto the convolution of a sampling sequence of antenna beam along the azimuth and a sampling sequence of a target reflectance distribution function along the azimuth, and a problem of scanning radar front-view super-resolution imaging is converted into a de-convolution problem; then, the total variation functional of the scattering coefficient of an imaging region is integrated into priori information of the de-convolution problem, and the de-convolution problem is converted into a constrained optimization problem; and finally, a global optimal solution to the constrained optimization problem is obtained through a cross iteration method, and scanning radar front-view super-resolution imaging is realized.

The invention discloses an SAR (synthetic aperture radar) tomography super-resolution imaging method. The SAR tomography super-resolution imaging method includes registering obtained two-dimensional imaging results of various SAR tomography baselines, creating height-direction signals pixel by pixel according to the sequence of the baselines, realizing frequency modulation correction and constructing a redundancy matrix; and modeling a height-direction imaging problem into a sparse signal reconstruction problem according to the characteristic of sparsity of height-direction scattering coefficients, computing a sparse solution of the spares signal reconstruction problem by means of iteration by the aid of the constraint condition of the minimum weighted norm, and realizing height-dimensional imaging of an object. The method is applied to SAR tomography height-dimensional imaging, and problems that the quantity of two-dimensional SAR images in the same area and (/or) trajectory distribution is uneven, and only a few parts of trajectory intervals (baselines) meet the Nyquist sampling theory are solved. In addition, by the aid of the method, energy of the object is more concentrated, namely, the resolution is improved, and the problem that the quantity of baselines is small is solved.

The invention belongs to the technical field of radar signal processing, and specifically relates to a surface target imaging method based on a Terahertz aperture coding imaging system. The method comprises the following main steps: based on wavelet sparse base structure of a wavelet transformation, under the Terahertz aperture coding imaging system, grid division is performed on a target plane, and the number of grids is determined; a wavelet mother function is selected, a maximum decomposition level is determined through utilization of matlab, and, according to the wavelet mother function, the number of the grids and the maximum decomposition level, a wavelet sparse base matrix is solved through adoption of the matlab; and surface target imaging is performed based on a sparse reconstruction algorithm, a reference signal matrix is multiplied by the wavelet sparse base matrix to form a dictionary matrix, a new mathematical model aiming at a surface target is constructed, effective solution is performed through the sparse reconstruction algorithm, and a surface target imaging result can be obtained through a wavelet inverse transformation. The surface target imaging problem under the Terahertz frequency band aperture coding system is solved, consistency of the imaging model is high, an imaging effect is good, and the imaging speed is fast.

The invention discloses a mirror-like object surface optical imaging device and an imaging method thereof. The device comprises a parallel light source, a grating, a camera, a computer and a light source controller for controlling the parallel light source. The camera is connected to the computer. The parallel light source and the grating cooperate to generate a non-uniform light source with light and dark stripes. According to refraction and reflection principles of light, by moving the non-uniform light source, the light and dark stripes can scan the total surface of a scanned object, and the camera is employed to acquire a same position on the object surface continuously in a movement period so as to obtain series of pictures, and subjecting the series of pictures to morphological dilation treatment, and then synthesizing a final target pictures. The device and the method provided by the invention solve the unable imaging problem due to too small mirror-like object surface defects and high reflections, and mirror-like object surface micro-defects and a background can form significant contrast, the noise is small, the gray value difference is great, and defect segmentation can be easy.

The invention relates to an electrical tomography regularization reconstruction method based on an adjacent point variation sum. The electrical tomography problem is regarded as a linear ill-posed problem, A is a sensitivity matrix, b is a relative boundary measured value vector, x is an imaging gray value vector corresponding to field domain substance electrical characteristic distribution and is called a solution vector, and the method is characterized in that the sum of absolute values of electrical characteristic quantity difference of all adjacent pixel points is used as a regularization function, and a Newton-Raphson iterative method is utilized to solve x, and calculation is completed to obtain a reconstructed image.

The invention discloses a multi-sensor multi-target location method based on imaging strategies. According to the method, a sensor network is modeled to form a two-dimensional sparse array, the multi-target location problem based on the sensor network is converted into an imaging problem, and projection strategies like the BP algorithm are adopted to solve complex data association problems; the peak value of a three-dimensional image is searched for through the greedy algorithm, false targets are removed and multiple targets are located through the culling algorithm. Compared with a traditional multi-target location method, the method has the advantages that phase-coherent accumulation is carried out on multiple echoes of the targets from the point of imaging, and therefore the signal-noise ratio of the targets can be further improved, and the multiple targets can be conveniently and accurately calculated; multi-target location is conveniently achieved in an imaging space, and therefore the probability of multi-target location is increased.

The invention discloses a method for imaging an actual aperture foresight on basis of subspace projection, which mainly solves the high resolution imaging problem of a right ahead object of a flightpath. The method has the detection processes that: 1) radar beams are sent out at equal intervals for carrying out sequence overlap scanning on a ground monitoring area to obtain the radar return data; 2) a modular arithmetic is utilized to extract the intensity vector of the return data; 3) the autocorrelation matrix of the intensity vector of the return data is gained; 4) eigen decomposition is carried out on the autocorrelation matrix; 5) the corresponding eigenvector of a small eigenvalue is used for forming a noise subspace; 6) a pattern search vector is defined; 7) the pattern search vector is projected into the noise subspace; 8) the peak values of a spatial spectrum function are calculated; and 9) the number of the signal is determined according to the number of the peak values of the spatial spectrum function for realizing foresight imaging. The method has the advantage of improving the resolution precision of an adjacent target in a main lobe, and can be used for an airborne radar monitoring system in the imaging field for realizing the detection and the recognition for a target on a track line.

The invention relates to a frequency domain imaging method of a ground penetrating radar, which is a frequency domain imaging algorithm based on linear frequency modulation waves. The method comprises the steps of performing difference frequency treatment on a target echo signal received by the radar and a reference signal, obtaining an echo wave after the difference frequency; performing Fourier transform on a two-dimensional scanning direction of the echo wave; and then performing phase compensation on data by taking an imaging central point as a reference point; then, performing secondary slot interpolation operation; decoupling three-dimensional imaging data by the slot interpolation; and finally, performing three-dimensional Fourier transform on data after slot interpolation, thereby accomplishing reconstruction of images. According to the frequency domain imaging method of the ground penetrating radar, the problem in three-dimensional coherent imaging under near field condition is solved, and compared with a conventional pulsed incoherent imaging technology, and images with better resolution ratio can be obtained.

The invention provides an image analysis method based on self-adaptive regularization. The image analysis method mainly comprises a self-adaptive regularization method, the application of the self-adaptive regularization to imaging problems and overall energy optimization, and comprises the following processes: the self-adaptive regularization method is used for optimizing compound energy functions generated in image analysis problems, the data fidelity and the regularization degree are automatically balanced according to current data fitting in the process of iterative optimization, a Huber loss function is also required to be used in the data fidelity and regularization items, an efficient convex optimization algorithm based on an alternate direction multiplier algorithm is provided, and by the use of an equivalence relation between the Huber function and a single norm approximation operator, the self-adaptive regularization method is verified in a synthetic or real image through image segmentation, dynamic prediction, image de-noising and other problems; results prove that compared with a conventional image analysis model, a self-adaptive Huber-Huber model applying the method has relatively better robustness and higher efficiency.

The invention discloses a combined retinal vessel segmentation system and method based on the fractal dimension number and Gauss filtering. The system comprises an image pre-processing unit used for improving retina image contrast and improving image quality to reduce interference of imaging problems on the subsequent processing process, a local detection unit used for extracting detailed portions of the retina image after pre-processing to improve fine vessel extraction accuracy, a global detection unit used for extracting a main portion of the retina image after pre-processing to highlight the global information of the retina vessel, a retina image fusion unit used for fusing the detailed portions of the retina image extracted by the local detection unit and the main portion of the retina image extracted by the global detection unit and improving system segmentation accuracy, and a retina image post-processing unit. The system is advantaged in that the fractal dimension number and Gauss filtering are combined, the system is suitable for segmentation of vessels of retina eyeground images, and segmentation accuracy is substantially improved.

The invention belongs to the field of radar imaging, and relates to an SA-ISAR imaging method for a target with a micro-motion component based on low-rank and sparse joint constraint. The method comprises the following steps: S1, carrying out modeling of a target one-dimensional range profile sequence after translation compensation of target with a micro-motion component; S2, carrying out modelingon a sparse aperture ISAR imaging problem of the target with a micro-motion component; and S3, solving the sparse aperture ISAR imaging problem of the target with the micro-motion component by adopting linear ADMM. The method has the following beneficial effects: sparse aperture ISAR imaging of a target with a micro-motion component can be realized; under sparse aperture conditions, the one-dimensional distance image sequence of the target main body part and the micro-motion part can be effectively separated; an m-D effect caused by the target micro-motion component is eliminated; sidelobe and grating lobe interference caused by the sparse aperture is eliminated, and thus the target main body part ISAR image with a good focusing effect is further obtained; and the method has an importantengineering application value for radar imaging of a target with a micro-motion component, micro-motion parameter estimation, feature extraction and target recognition under the condition of data limitation.

The invention belongs to the technical field of image processing, relating to a method for extracting and describing DAISY-based features with mirror face turning invariance. The method comprises the following steps: (1) carrying out Gaussian kernel convolution processing on an input image; (2) continually carrying out Gauss difference processing on the image to detect the extreme points thereof; (3) screening feature points; (4) precisely positioning the positions of feature points; (5) determining the direction parameter of each feature point; (6) summing gradient module values on both sides respectively using the main direction as a dividing line; and (7) organizing the pixel units in a Gaussian weight window to carry out encoding and normalized operations, thereby generating description data of the image. The invention enhances the robustness of the feature extraction and description method to mirror imaging problem, and expands application fields of computer vision.

The invention relates to the technical field of form generation, and discloses an HS code matching method and system based on intelligent analysis and recognition, an HS code display method and systembased on intelligent analysis and recognition and a storage medium, and the method comprises the steps: obtaining a to-be-judged object; correcting an imaging problem; detecting a text in the to-be-judged object; recognizing the text content; extracting required fields and/or elements from the text recognition result to generate to-be-judged object description information; according to the obtained description information of the to-be-judged object and pre-trained graph data, judging the category of the to-be-judged object, and performing entity linking with the graph data; training the pre-trained atlas data in combination with a semantic library to generate a model according to provided HS coded document data, and carrying out continuous learning and optimization by external data feedback and an AI algorithm. The intelligent knowledge search engine capable of meeting the customs declaration pre-classification service field is built to meet the requirement for quickly obtaining knowledge, and can accurately correspond to various columns in combination with character recognition.

The invention discloses a distributed radar imaging method for resource optimum allocation. The method is applied to the field of radar imaging detection. For the high resolution imaging problem generated when resources of a transmitter and a receiver are limited, according to the method provided by the invention, through analysis of a distributed radar wave number domain spatial spectrum rule, adistributed radar imaging geometric model and a spatial spectrum signal model are exported; through analysis of singular value distribution of a measurement matrix, influences of signal transmitting bandwidths and the number of radar receiving channels on spatial spectrum are determined, so the optimum signal transmitting bandwidth and the optimum radar transmitting and receiving number are obtained; and system echo and system observation matrixes are regenerated based on the optimum signal transmitting bandwidth and the optimum radar transmitting and receiving number, and distributed radar imaging is realized through utilization of a truncated singular value decomposition algorithm. According to the method provided by the invention, through reduction of the radar transmitting and receiving number, the system signal transmitting bandwidth is improved, and distributed radar imaging resolution is improved.

The invention belongs to the technical field of radar association imaging and discloses a radar association imaging method based on generalized total variation regularization. The method comprises steps that an associated imaging model is established; the constraint of generalized total variation regularization is added, and an optimal imaging problem based on second-order TGV regular terms is established; a corresponding Lagrangian function is constructed to obtain an optimization model, and to-be-solved sub-problems are obtained through utilizing the alternating direction multiplier method and solved iteratively. The method is advantaged in that the model is solved through dividing variables and applying the multiplier alternating direction method, so a higher-quality echo reconstructionimage can be obtained, advantages of the proposed method are verified in the simulation test process, the higher resolution can be achieved, moreover, that a relatively small error can be ensured when the noise is relatively large can be seen from the mean square error of imaging, obviously, the method is further applicable to dealing with other radar image recovery problems.

The invention discloses a human visual characteristic-based adaptive image enhancement method. The problems of medium/low-grayscale and medium/low-resolution imaging are solved. The method comprises the following steps of 1, calculating statistical indexes of an image; 2, performing grayscale value normalization complementary-transformation; 3, calculating an overall grayscale regulation coefficient alpha and an edge enhancement coefficient beta of the image; 4, calculating a mean image; 5, calculating an intermediate image; 6, performing asymmetrical inverse transformation on the intermediate image obtained in the step 5 by taking e as a power base; 7, performing grayscale normalization inverse transformation on an asymmetrically inversely transformed image obtained in the step 6 to obtain an enhanced image to implement the adaptive image enhancement method.

Various systems and methods of the present invention provide amorphous computing systems and methods for use thereof. In some cases, the amorphous computing systems include one or more amorphous hardware elements that are programmed under control of a computer processor such as, for example, an AMD™ or INTEL™ based personal computer. Portions of an algorithm can then be computed by the individual amorphous hardware elements that can be, as one example, an FPGA or some subset of hardware gates available on an FPGA. Methods can include a variety of computations including Wave Equations and Kirchhoff algorithms. Thus, in particular cases, the amorphous computing systems and methods for using such are applied to seismic imaging problems, as well as other problems.

The invention discloses an image reconstruction method for precisely recognizing a stroke intracranial lesion area and is used for reconstructing an electrical conductivity distribution of hemorrhagicstroke by electrical impedance tomography (EIT). According to the method, the EIT problem is regarded as a linear ill-posed problem and a target function is determined; and the process of the reconstruction method comprises the following steps: acquiring object field information, and solving a relative boundary measurement value vector and a sensitivity matrix; setting an initialization parameter; transforming a data fidelity item by using a weighting matrix; solving the target function in an iteration manner; judging whether iteration ends or not; and performing imaging according to an obtained gray value. The image reconstruction method effectively overcomes an edge excessive smoothness effect and inhibits stair step artifacts, improves the stability and applicability of an algorithm and the spatial resolution of a reconstructed image, and expands the application range of a total variation regularization algorithm in the medical detection field.