285results about How to "Improve rebuild speed" patented technology

The invention provides a contact network three-dimensional reconstruction method based on SIFT and LBP point cloud registration. The method comprises the first step of obtaining initial three-dimensional point cloud data of the environment where parts of a contact network to be reconstructed are located through motion-sensing peripheral Kinect for Windows, and conducting denoising, simplifying, partitioning clustering, fusing and other preprocessing operations on the initial three-dimensional point cloud data to obtain single-view-angle point cloud data of the parts of the contact network to be reconstructed, the second step of extracting key points through an SIFT algorithm, constructing description vectors of the key points by means of LBP features of uniform patterns and determining the corresponding relations between the key points in different point clouds according to the distances between the vectors, the third step of completing point cloud registration through a rough registration method and an ICP fine registration method and obtaining the complete three-dimensional point cloud data of the parts of the contact network to be reconstructed, and the fourth step of completing three-dimensional reconstruction through the Poisson surface reconstruction method and obtaining a three-dimensional model. According to the method, the key factor is point cloud registration which is the key step influencing the three-dimensional reconstruction speed; the description vectors of the key points are constructed by means of the LBP features of the uniform patterns, so that vector dimensions are reduced, the matching speed of the corresponding relations is increased, registration is accelerated, and the three-dimensional reconstruction speed is increased.

The invention discloses a two-dimensional image recognition and object reconstruction method based on a three-dimensional model database. The method includes the following steps: a three-dimensional model of an object used for a two-dimensional projection is constructed, the three-dimensional model database is established and an correspondingly correlative attribute database is built up; the two-dimensional projection matching degrees of a two-dimensional image of the object to be recognized and the three-dimensional model of the relative object are compared; as for the two-dimensional projections being in accordance with the matching degree requirement, the object in the correlative attribute database is recognized as the object in the two-dimensional image; and the three-dimensional model in the three-dimensional model database which is correlative to the object in the attribute database is used for reconstructing the object. Due to the adoption of the comparison of the two-dimensional image of the object with the two-dimensional projection of the relative three-dimensional model, the aim of recognizing the object in the two-dimensional image of the object is realized and the two-dimensional images of the object which are photographed in a plurality of angles and from a plurality of directions are recognized; moreover, the aim of reconstructing the object in the two-dimensional image is achieved by using the correlative three-dimensional model; in addition, a paralleling calculation method is adopted to accelerate the speed of the reorganization and the reconstruction.

The invention provides a computer tomography (CT) parallel reconstructing system and an imaging method thereof. The CT parallel reconstructing system comprises a front end sampler, a central node and a plurality of sub-nodes connected with the central node, wherein each sub-node is provided with an image processor. The imaging method of the CT parallel reconstructing system comprises the following steps: (1) a reconstructed image is divided into a plurality of block regions; (2) original projection data of each angle, which is sampled by the front end sampler, is received by the central node; (3) calculation tasks are allotted to the sub-nodes by the central node, and the reconstruction values of one or a plurality of block regions are calculated by the sub-nodes to which the reconstruction calculation tasks are allotted; (4) the reconstruction calculation tasks are completed by the sub-nodes; and (5) a complete reconstruction image is combined by the central node. By adopting the method of dividing the reconstructed image into subregions, the invention fully excavates the parallel characteristics of CT scan and reconstruction, and performs parallel reconstruction of GPU while data collection, and the reconstruction time is lowered from the minute grade of the prior art to millisecond grade, thereby accurately displaying the reconstruction of the sectional image of a detected object in real time.

The invention relates to a disk array data reconstruction method which pertains to a computer data storage method and solves the problem that the existing disk array data reconstruction method takes too much time and affects the reading and writing performances and reliability of a storage system. A disk array of the invention is provided with a main control module, reading and writing processing modules and a reconstruction module and comprises the steps of initialization, diary pot diagram update, reconstruction based on diaries and finishing. The method instructs data reconstruction process by the real-time monitoring of disk space using status of the disk array; when space that is not accessed is reconstructed, the only requirement is to write 0 in all corresponding data blocks newly added into a disk, thus greatly reducing physical disk visiting time brought by reconstruction, increasing reconstruction speed and reducing the response time of user access; the reconstruction method does not change the reconstruction procedure or the distribution manner of disk array data, can conveniently optimize various traditional disk array data reconstruction methods and is applicable to the construction of storage systems with high performance, high usability and high reliability.

The invention discloses a CT image reconstruction method. The method comprises: an effective data boundary of an image is identified, the minimum value and the maximum value of the obtained effective data boundary point line (row) numbers are obtained, data weighting is carried out on the image, a fan-shaped beam is corrected to be a corresponding parallel beam, artifacts in the image are eliminated by convolution filtration, and back projection reconstruction is carried out by utilizing the minimum value and the maximum value of the line (row) numbers. The invention also discloses a CT image reconstruction device comprising a boundary identification module, a data weighting module, a filtration module and a reconstruction module. The CT image reconstruction method and the device improve the reconstruction speed under the premise of ensuring the unchanged quality of the reconstructed image by identifying the effective data boundary before the CT image reconstruction and only reconstructing effective data in the boundary after the CT image reconstruction.

The invention discloses a method for realizing rebuilding of disc redundant array (RAID), a disc redundant array (RAID) for realizing the rebuilding and a controller thereof. When the RAID is under a degraded state caused by invalidation of member discs, even if the member discs are invalidated, if only invalidated address areas of the invalidated member discs are not overlaid, the method can continuously use data in the invalidated address areas of the invalidated member discs and respectively restore all the data in the each member disc into corresponding hot spare discs. Therefore, error tolerability in the process of rebuilding the RAID is not limited by RAID redundancy rate. Furthermore, the data in the invalidated address areas of the each invalidated member disc still can be directly copied, if the invalidated address areas of the invalidated member discs have I/O write operation, I/O write data can be directly copied to the corresponding spare discs, so as to reduce calculated amount of a RAID algorithm and speed up rebuilding speed of the RAID.

Embodiments of the present disclosure relate to a method and apparatus for rebuilding redundant array of independent disks (RAID). The method comprises dividing a RAID group including a disk into a plurality of stripes in response to detecting a failure of the disk, and each stripe includes a disk slice across all disks of the RAID group. The method further comprises mapping the plurality of stripes to other RAID groups respectively and rebuilding data on the plurality of stripes in other RAID groups. Embodiments of the present disclosure rebuild in parallel the stripes of the failed RAID group in other RAID groups, thereby effectively increasing the rebuilding speed for RAID and ensuring reliability of the RAID group.

The invention relates to the field of curved planar reconstruction, in particular to a curved planar reconstruction method for cardiac scattered-point cloud data. The curved planar reconstruction method for cardiac scattered-point cloud data includes the steps of firstly, defining the data representation form of acquired point cloud data, establishing octree topological relation, and defining a space function; secondly, establishing a vector field, selecting local adjacent planes, and determining vertex normal vectors according to an average of planar normal vectors of the local adjacent planes; thirdly, uniformizing the vertex normal vectors; and fourthly, solving a Poisson equation, extracting a contour surface by MC (marching cubes) algorithm according to an indicator function and gradient thereof, and reconstructing a cardiac three-dimensional surface model. By the curved planar reconstruction method for cardiac scattered-point cloud data, cardiac spatial position relation can be displayed visually and accurately, optional display, zooming, rotation and cutting in three dimensions can be achieved, cardiac diseases can be detected, computer diagnostic analysis for diseases such as arrhythmia and surgical accurate simulation are achieved, and safety and reliability in disease diagnosis are improved.

The invention discloses an OFDM sparse channel estimation method based on adaptive compressed sensing. The method comprises the following steps of step (1), modeling an OFDM channel estimation problemas a compressed sensing signal reconstruction problem; and step (2), solving the compressed sensing signal reconstruction problem by utilizing an adaptive compressed sensing channel estimation algorithm, and estimating an OFDM sparse channel. The method has the advantages that the OFDM sparse channel can be quickly estimated under a condition that the OFDM system channel sparsity is unknown; andcompared with an OFDM sparse channel estimation method based on an existing sparsity adaptive algorithm, the OFDM sparse channel estimation method based on the algorithm has lower channel estimation mean square error, lower system error bit rate and shorter estimation time.

The invention provides a dynamic magnetic resonance image reconstruction method and device of adaptive parameter learning, and the method comprises the steps of carrying out the reconstruction of theregularization terms in a CS-MRI model, and includes using DCT in the spatial domain and using TV in the temporal domain to remove redundancy from dynamic magnetic resonance images, and using a convolutional neural network to carry out the adaptive learning on a large number of parameters in CS-MRI, and establishing a magnetic resonance image reconstruction model; reconstructing the sample image through the established magnetic resonance reconstruction model to obtain a reconstructed image; calculating a difference value between the full-sampling image and the reconstructed image; and according to the difference value, updating parameters in the model, including DCT, a TV filtering operator, a regularization parameter and the like, by using a back propagation algorithm in the network. According to the magnetic resonance image reconstruction model established by the method, the height under-sampling image can be efficiently reconstructed to obtain the image with very high reconstructionprecision and reconstruction speed, so that the time of magnetic resonance scanning can be effectively shortened under the condition that the spatial resolution is not lost.

The invention discloses a multi-channel magnetic resonance image reconstruction method based on deep learning, and relates to a multi-channel magnetic resonance image reconstruction method. The methodcomprises the following steps: acquiring multi-channel magnetic resonance images, and forming a training set by a sensitivity mapping graph, an under-sampled zero-filled multi-channel magnetic resonance image and a full-sampled synthetic image; establishing a multi-channel deep learning network model for magnetic resonance image reconstruction; constructing a loss function of the network; training multi-channel magnetic resonance image reconstruction network model parameters; reconstructing the target under-sampling multi-channel magnetic resonance image; obtaining an end-to-end mapping function from the under-sampled multi-channel image to the complete magnetic resonance image corresponding to the network model and a network model loss function by adopting a residual connection mode forthe iteration block; training magnetic resonance image reconstruction network model parameters of a residual connection mode; and reconstructing the target under-sampling multi-channel magnetic resonance image by using the residual connected network model. The method has the advantages of high reconstruction speed and good reconstruction effect.

The invention discloses a quantitative optical molecular tomographic device and a quantitative reconstruction method. The molecular tomographic device comprises a bioluminenscent tomographic data acquisition platform, a Micro CT (Computerized Tomography) system, a quantitative calibration system and a quantitative reconstruction computer, wherein the bioluminenscent tomographic data acquisition platform is used for capturing the distribution condition of bioluminescent light sources emerging on the body surface of a small animal, and the Micro CT system is used for acquiring the anatomical structure information of the small animal body. The quantitative reconstruction method comprises a quantitative method of a receiving light source based on a field of view and a reconstruction method based on a finite element subdivision network. By utilizing the quantitative optical molecular tomographic device and the quantitative reconstruction method, three-dimensional light source distribution and the quantitative information of a cell number in a small animal body can be inverted through the two-dimensional light source distribution and the quantitative energy information on the surface of the small animal body.

The invention relates to an ultrasonic signal reestablishing method based on sparse data, and belongs to the technical field of compressive sensing. Firstly, a Gabor atom library serves as a sparse transformation domain of ultrasonic signals, nonlinear transformation is carried out on the ultrasonic signals by using a measurement matrix to obtain measurement values, and original signals are reestablished through an orthogonal matching pursuit algorithm according to the measurement values and a sensing matrix. According to the ultrasonic signal reestablishing method based on the sparse data, an artificial fish school algorithm is introduced to optimize the orthogonal matching pursuit algorithm, the times of inner product calculation can be reduced, and calculation speed can be improved. The ultrasonic signal reestablishing method based on the sparse data has the advantages of being high in reestablishing accuracy and computing speed, and can be applied to fields such as data compression, transmission and storage and high resolution imaging.

The invention discloses a method for rapid calculation of a pressure difference of a bifurcated vessel and an FFR based on CFD simulation. The method comprises the following steps of receiving geometrical parameters and a geometrical model of a second of the bifurcated vessel, wherein the blood vessel comprises a near end point and a far end point and the geometrical parameters include a first geometrical parameter, a second geometrical parameter and a third geometrical parameter; receiving entrance blood flow volume and blood flow pressure at the near end point of the bifurcated vessel section; calculating a reference lumen diameter at a first position of the blood vessel based on the first geometrical parameter, the second geometrical parameter, the third geometrical parameter and the position data of the first position; utilizing computational fluid dynamics (CFD) simulation to obtain pressure distribution on the geometrical model based on the geometrical model and the entrance blood flow volume and the blood flow pressure at the near end point; and utilizing a pressure correction formula to correct the pressure distribution based on the first geometrical parameter and the third geometrical parameter, and computing to obtain a pressure difference value deltaP between first blood flow pressure at the near end point of the blood vessel and second blood flow pressure at the first position.

The invention discloses a method for quickly implementing ultrasonic imaging on basis of compressive sensing and mainly solves the problem that the time of computing iteration time step length by a quick iterative shrinkage/thresholding algorithm under a compressive sensing frame is slow. The method includes the steps of 1, according to a set resolution, discretizing a detection area; 2, subjecting the discretized detection area to wide-band pulse scanning to obtain an echo vector and an observation matrix; 3, establishing an ultrasonic imaging mathematic model according to the echo vector and the observation matrix; 4, determining the iteration step length by acquiring a 2-norm of a sum of module values of each row of elements in the observation matrix; 5, plugging the iteration step length into the quick iterative shrinkage/thresholding algorithm to solve scattering intensity of a reconstructed observation scenario; and 6, arranging module values of the scattering intensity into a two-dimensional matrix so as to obtain a reconstructed ultrasonic image. Compared with the traditional quick iterative shrinkage/thresholding algorithm, the method has the advantages that the time of computing the iteration step length is greatly shortened, timeliness of the ultrasonic image is improved, and the method is applicable to the field of ultrasonic real-time processing.

In process of recreating a RAID, according to the size of preset storage space unit, sequentially copying the data in each storage space unit in the failure member disks to the new member disks; if the copy is successful, then copying the data in next storage space unit into the new member disks; if the copy is failure, then reading out the data in other normal member disks to make calculation to get the data in the failure storage space unit, and then writing the data to the new member disk, and then continuing the copy of the next storage space unit.

The invention provides a data reconstruction method for disk array, which belongs to computer data storage methods and solves the problems that disk array data reconstruction consumes excessive time and affects the reading-writing performance and reliability of systems. The method consists of a reconstruction stage and a recovery stage in sequence, wherein an agent disk array is arranged outside a disk array during reconstruction; a user control interface, a reconstruction module, a space management module, a hot data identification module, a request redirection module, a data recovering module, a redirection data log table and a non-hot data chain list are arranged on the disk array during reconstruction; the reconstruction stage comprises a reconstruction step and a reconstruction-stage request processing step which are concurrent; and the recovery stage comprises a data recovering step and a recovery-stage request processing step which are concurrent. The method has the advantages of accelerating reconstruction speed and reducing the access response time of users, does not change reconstruction process or the data distribution mode of the disk array, and is suitable for constructing the storage systems with high performance, availability and reliability.

The invention discloses a composite degraded image high-quality reconstruction method based on a conditional generative adversarial network. The method is based on a conditional generative adversarialnetwork to perform high-quality reconstruction on a composite degraded image in outdoor visual systems such as unmanned aerial vehicle aerial photography, video monitoring and intelligent transportation, and comprises an overall process, establishment of a composite degraded image sample library, establishment and training of a network model, and high-quality reconstruction of the composite degraded image. Unified high-quality reconstruction is carried out on composite degraded images obtained by outdoor vision systems such as unmanned aerial vehicle aerial photography, video monitoring and intelligent transportation through a conditional generative adversarial network. According to the invention, a scheme for establishing a corresponding clear-composite degraded image sample library is provided.; a conditional generative adversarial network is adopted to establish a composite degraded image high-quality reconstruction method, and unified reconstruction of composite degraded images with haze, blurring, a compression effect and the like can be completed. And a light network is adopted, so that the image reconstruction speed is increased, and the application of the method in practice is facilitated.

A computed tomography (CT) imaging apparatus (40) includes a CT imaging scanner (42) that acquires projection data. Data acquisition electronics (90) communicate with the CT scanner (42), and an acquisition computer (60₁) receives the projection data via the data acquisition electronics (90). A communication network (70) communicates with the acquisition computer (60₁) and with at least one additional computer (60₂, 60₃, 60₄). The at least one additional computer (60₂, 60₃, 60₄) cooperates with the data acquisition computer (60₁) and the communication network (70) to define a computational pipeline (104) that reconstructs selected projection data into a selected image representation.

The invention discloses an image super-resolution reconstruction method based on a multi-column convolution neural network. Firstly, a multi-column convolution neural network model is designed according to the depth learning algorithm, and includes feature extraction and image reconstruction. Then, the original image is cut into small blocks, and these high-resolution blocks are downsampled to getlow-resolution blocks, and these low-resolution and high-resolution pairs of blocks are used to build the training set. Finally, the model is trained by using the stochastic gradient descent algorithm to get a model that reconstructs the low-resolution image to the high-resolution image, and the input low-resolution image is reconstructed to the corresponding high-resolution image. The method ofthe invention is tested on five universal image databases of Set5, Set14, BSDS100, Urban100 and Manga109, and has high robustness and accuracy.

The invention provides an image processing method, an image processing device, a terminal device and a readable storage medium. The method comprises the steps of obtaining a to-be-processed image to be subjected to super-resolution reconstruction; carrying out image segmentation on the to-be-processed image to obtain a first image containing a target object and a second image containing other image areas, and setting the pixel values of other pixel points in the first image to be zero except the target object; inputting the first image into a first neural network model, obtaining a corrected first image after super-resolution reconstruction, and performing super-resolution reconstruction on the second image by using a preset algorithm to obtain a corrected second image, the high-frequencyinformation energy contained in the corrected first image being greater than a first threshold, and the duration of processing the second image by using the preset algorithm being less than a second threshold; and fusing the corrected first image and the corrected second image to obtain a processed image. According to the application, the super-resolution reconstruction effect and the consumed time of the super-resolution reconstruction can be considered at the same time.