132results about How to "Rebuild fast" patented technology

The invention discloses a mura defect detection method based on sample learning and human visual characteristics, which belongs to the TFT-LCD display defect detection field. According to the invention, the method comprises the following steps: firstly, utilizing the Gaussian filter smoothing and Hough transform rectangle to preprocess the TFT-LCD display image, removing a large amount of noise and segmenting the image areas to be detected; then, using the PCA algorithm to conduct learning to a large amount of defect-free samples; automatically extracting the differential characteristics between the background and the target and re-constructing a background image; and then, thresholding the differential characteristics between a testing image and the background; through the reconstructing of the background and the threshold calculating, jointly creating a model. According to the invention, based on the training sample learning, a relationship model between the background structure information and the threshold value is established; and a self-adaptive segmentation algorithm based on human visual characteristics is proposed. The main purpose of the invention is to detect different mura defects in a TFT-LCD, to raise the qualification rate and to increase accuracy for the detection of mura defects.

The invention provides a remote sensing image super resolution reconstruction method and a system based on a depth convolution network. The method comprises steps: a to-be-processed remote sensing image is converted to YCbCr space from RGB space, and brightness space and chromaticity space are separated; a multilayer depth convolution network is built, a super resolution reconstruction model is built based on the multilayer depth convolution network, the super resolution reconstruction model is used for reconstructing the brightness space, and brightness space after reconstruction is obtained; with the brightness space after reconstruction as a guide graph, the chromaticity space is guided for joint bilateral filtering, and chromaticity space after reconstruction is obtained; the brightness space after reconstruction and the chromaticity space after reconstruction are integrated, the to-be-processed remote sensing image after integration is returned to the RGB space from the YCbCr space, a super resolution image is obtained, and the super resolution image has a higher resolution than the to-be-processed remote sensing image. The above method and the system, in a condition of not relying on a multi-temporal remote sensing image sequence in the same scene, realize super resolution reconstruction in view of the remote sensing image, and the image resolution is enhanced.

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.

In a K-space SENSitivity Encoding (KSENSE) magnetic resonance parallel imaging method the sensitivity distribution of MR reception coils; is calculated and based on the sensitivity of the coils, signals from the respective coil merging channels are merged. The merged data are used to perform k-space data fitting and optimal fitting parameters are found. The fitting parameters are used to remove artifacts in the reconstructed image. The KSENSE method, compared to SENSE, mSENSE and GRAPPA, has the advantages of the image reconstructed by KSENSE having an optimized signal-to-noise ratio (SNR) that is superior to that of an image reconstructed by GRAPPA under the same conditions and approximates that when mSENSE is used, and an image reconstructed by KSENSE has relatively low residual artifacts and an artifact intensity, as a whole, that is superior to that of an image reconstructed by SENSE or the like under the same conditions and equivalent to that when GRAPPA is used, and, compared to GRAPPA that also performs operations in k-space, KSENSE has a higher reconstruction speed.

The invention discloses an infrared temperature measurement imaging device and a detection method thereof. The device comprises an infrared temperature measurement imaging module, a three-dimensional imaging module, a data processing module and a correction processing module. The infrared temperature measurement imaging module is used for acquiring two-dimensional temperature image data of a detected object. The three-dimensional imaging module is used for acquiring position data of a detected object surface. The data processing module is used for acquiring three-dimensional temperature data of the detected object surface according to the position data of the detected object surface, the two-dimensional temperature image data of the detected object and other parameters. The correction processing module is used for correcting the three-dimensional temperature data of the detected object surface according to a position relation of the detected object and the infrared temperature measurement imaging module and acquiring corrected temperature data. The device can acquire three-dimensional temperature distribution of the detected object. Through correcting the temperature of the detected object surface, the accurate temperature of the detected object surface can be acquired.

The invention relates to a CT imaging method which is based on multiple scanning and used for CT imaging, namely, super-visual field CT imaging, when the scanning visual field of CT equipment can not cover an object to be detected. The method comprises the following steps: a projection data set is obtained by multiple scanning and collection and the CT image of the object to be scanned is obtained by that a scanning reconstruction algorithm is operated to process the projection data set. The invention is characterized in that when the object to be detected is scanned, the area covered by each scanning is allowed to have overlapped parts and the scanning reconstruction algorithm used for processing the projection data set does not need to rearrange the data. The invention has the main advantages that the size of the object to be detected is not limited by the CT scanning visual field, the scanning mode is flexible; as the reconstruction algorithm does not need to rearrange the data, interpolation calculation needed by the rearrangement and the degrading of the reconstructed image caused by the rearrangement are avoided, therefore, the reconstruction speed is fast and the resolution of the reconstructed image is high.

The invention discloses a laser and image data fused three-dimensional reconstruction method. The method comprises the steps of obtaining three-dimensional point cloud data of a laser radar and RGB image data of a camera in a to-be-reconstructed three-dimensional scene; mapping the three-dimensional point cloud data of the laser radar to a two-dimensional image space of a camera, and constructinga sparse depth map; converting the sparse depth map into a dense depth map, and obtaining RGB information and depth information of each frame of image; constructing a crystal grid of a to-be-reconstructed three-dimensional scene, mapping the depth information of each frame of image into the crystal grid, and determining the surface of a reconstructed three-dimensional model; and mapping the RGB information of each frame of image to the surface of the reconstructed three-dimensional model to form texture information, and obtaining the reconstructed three-dimensional model. The invention furtherdiscloses a laser and image data fused three-dimensional reconstruction system. Data generated by passive three-dimensional reconstruction and data generated by active three-dimensional reconstruction are fused and intercommunicated, and real-time reconstruction of a three-dimensional scene is achieved.

The invention discloses a method for SPEED rapid magnetic resonance imaging based on wavelet domain sparse representation. The method for SPEED rapid magnetic resonance imaging based on wavelet domain sparse representation comprises two steps of regular under-sampled data collection and SPEED image reconstruction based on wavelet domain sparse representation. The regular under-sampled data collection comprises two steps of k-space regular under-sampling and k-space central domain complete sampling. The SPEED image reconstruction based on wavelet domain sparse representation comprises seven steps of zero filling Fourier reconstruction, discrete wavelet transformation, wavelet domain double-layer sparse ghost model construction, overlapped ghost separation based on a least square error method, discrete wavelet reverse transformation, rectification summation of a plurality of ghost images and image reconstruction. The discrete wavelet transformation is used for SPEED data sparse representation, the sparseness of the image is improved through discrete wavelet transformation, and further the quality of the SPEED reconstruction image is improved.

The invention discloses a magnetic resonance image reconstruction method based on an adaptive orthogonal basis, and the method comprises the following steps: (1) obtaining original k-space data, carrying out the Fourier inversion of the sampled k-space data, and obtaining an original reconstruction image; (2) building a reconstruction model based on compression sensing under the constraint of the orthogonal basis; (3) partitioning the original reconstruction image, randomly extracting some image blocks for the learning of the orthogonal basis, and obtaining a group of adaptive orthogonal basis; (4) employing a hard threshold method to achieve the sparse representation of all image blocks based on the adaptive orthogonal basis; (5) employing a least square method to updating the reconstruction image, and obtaining a current reconstruction image; (6) judging whether the current reconstruction image meets the condition of convergence or not: if the current reconstruction image meets the condition of convergence, the current reconstruction image serves as a final reconstruction image; or else, going to step (7); (7) reducing the value of a regularization parameter, the current reconstruction image serves as the original reconstruction image, and returning to step (3). The method is high in reconstruction speed, and is good in image quality.

The invention discloses a passive single pixel telescope imaging system, and belongs to an optical imaging field. The passive single pixel telescope imaging system comprises an imaging lens, a spatial light modulator, a light beam convergent lens and a data acquisition card, which are orderly arranged on a light path; the spatial light modulator is arranged at an imaging position of the imaging lens; the light beam convergent lens is located on a light path of an output beam of the spatial light modulator; the data acquisition card is provided with a photovoltaic conversion chip; the data acquisition card and the spatial light modulator are connected with a processor through a data line. The imaging system is simple in structure and low in environmental requirement; besides, the acquisition time can be effectively reduced and the recovery of target images under a low sampling rate can be realized. The invention further discloses a passive single pixel telescope imaging method. The imaging method is simple in recovery algorithm and fast in image reconstruction speed, and can shoot and reconstruct the images of large sizes, so that the imaging method has a higher application value in the macro fields like military, remote sensing satellite, astronomy and engineering detection, etc.

The invention relates to an unmanned aerial vehicle navigation map construction system based on an image three-dimensional reconstruction technology. The unmanned aerial vehicle navigation map construction system comprises an unmanned aerial vehicle, a data acquisition assembly and a three-dimensional navigation map construction system, wherein the three-dimensional navigation map construction system comprises an image set input system, a feature point extraction system, a sparse three-dimensional point cloud reconstruction system, a dense three-dimensional point cloud reconstruction system, apoint cloud model optimization system and a three-dimensional navigation map reconstruction system. A scene image set is input into a three-dimensional navigation map construction system, feature point detection is carried out on all images, and a sparse point cloud model of a scene and a dense point cloud model of the scene are reconstructed, and then miscellaneous points of the models are removed, and surface reconstruction is carried out, and optimization is carried out, and a three-dimensional navigation map of the scene is reconstructed. The invention further discloses an unmanned aerialvehicle navigation map construction method based on the image three-dimensional reconstruction technology. According to the unmanned aerial vehicle navigation map construction method, the three-dimensional navigation map is constructed by utilizing the system, and the reconstructed three-dimensional navigation map is close to a real scene, and the precision is relatively high.

The invention provides an analytic method for eliminating a metal artifact of a CT image. With the method, the iterative operation can be avoided. The method comprises: a metal image is segmented from an image reconstructed based on projection data on the multi-energy-spectrum-ray condition; a projection image is solved; an approximate value of an energy spectrum function is searched; an expression of an artifact function is obtained and an artifact image is obtained; the artifact image is subtracted from an original image to obtain a corrected image as shown in an attached drawing. The method is characterized in that the band shaped artifact can be eliminated and the structural feature of original information can be kept; and no priori knowledge of the energy spectrum and original projection data are needed according to the correction method and the reconstruction speed is fast.

The invention relates to a method for realizing three-dimensional back projection by a graphics processor, comprising the following steps: storing projection data collected by a CT (Computed Tomography) processor in a host memory; meanwhile, calculating the Hermite coefficient of each pixel point and the sampling value of a spiral weight in a CT image to be reconstructed, i.e. a target image; copying the projection data in the host memory, the Hermite coefficient of each pixel point and the sampling value of the spiral weight into the display cache of the graphics processor; carrying out texture binding to data in the display cache; carrying out three-dimensional back projection in GPU (Graphics Processing Unit) to obtain a CT reconstruction image; and copying the obtained CT reconstruction image into the host memory. By applying the method disclosed by the invention, the three-dimensional back projection is realized in the GPU, and figures are chosen by virtue of a GPU three-dimensional array storage and three-dimensional texture binding technology based on a float4 data type as the basic element of a two-dimensional parameter array, thereby avoiding the problem that the figures are slowly chosen because parameter arrays are excessive so as to cause that GPU cache utilization is low so that operation efficiency is further improved and a satisfied result is obtained.

The invention discloses a magnetic resonance imaging method and device. According to the method, after a first deep neural network is used for reconstructing a first image, a first part of k-spatial data and a second part of k-spatial data extracted from complete k-spatial data are subjected to subtraction operation to obtain residual k-spatial data; then the residual k-spatial data is subjected to image reconstruction to obtain a residual image; and finally, the first image and the residual image are added to obtain a magnetic resonance image. In this way, the first part of the really collected k-spatial data is utilized in the processing carried out after the first image is reconstructed, the processing process of data fidelity terms is carried out outside the DNN reconstruction, and theDNN does not need to consider the processing of the data fidelity terms, so that the complexity and network parameters are lowered and reduced, and the image reconstruction speed is high. In addition, the processing process of the data fidelity terms is completed through sparse constraint reestablishing, without multi-time iteration, and the reconstruction speed can be improved on the premise that the image reconstruction stability is improved.

The invention provides an air-space-reservoir-bank integrated emergency survey data fusion and integrated display system and method. The air-space-reservoir-bank integrated emergency survey data fusion and integrated display system comprises a data acquisition and transmission module, a data extraction module, a data management module, a data registration module, a data splicing module, a data analysis and processing module, a data fusion module and a three-dimensional scene reconstruction module, wherein the data extraction module is connected with the data acquisition and transmission module; the data management module is connected with the data extraction module; the data registration module is connected with the data management module; the data splicing module is connected with the data management module; the data analysis and processing module is connected with the data management module; the data fusion module is connected with the data analysis and processing module; and the three-dimensional scene reconstruction module is connected with the data analysis and processing module. According to the air-space-reservoir-bank integrated emergency survey data fusion and integrated display system, the range and the condition of the affected area can be rapidly and accurately surveyed in detail, and the survey result is obtained.

The invention relates to a large diameter pipeline wall outside CT local scanning imaging method, and belongs to the technical field of CT scanning imaging. The method comprises the following steps: a radiation source and a detector are arranged in a circular track surrounding the center of a pipeline to be detected, and the detector is arranged in an offset manner; the radiation source and the detector circularly move along the circular track to scan in order to obtain the projection data of the annular region outside the pipeline to be detected; and a TVM-POCS reconstruction algorithm and a region scale fitting segmentation technology are combined to reconstruct the image of the annular region outside the pipeline according to the projection data. The large diameter pipeline wall outside CT local scanning imaging method has the advantages of simple and easy scanning mode, short scanning time, low radiation dose, satisfactory solving of the cross cutting problem of the projection data, great reduction of the reconstruction artifact, satisfactory solving of the problem of reconstructed image gray scale unevenness caused by ray beam hardening, and good quality of the finally displayed local reconstruction image of the annular region outside the pipeline.

The invention discloses a 4D-CBCT imaging method based on motion compensation, and belongs to the field of computed tomography. The method comprises the following steps: firstly, acquiring high-quality 4D-CBCT data of a patient, and dividing the high-quality 4D-CBCT data into samples and label data; then, requiring a motion compensation learning convolutional neural network of 4D-CBCT data to be constructed, wherein the network is used for establishing mapping between different phase images; secondly, training the network by taking the sample and the label data as inputs to obtain an optimal network parameter weight; and finally, reconstructing 4D-CBCT projection data under clinical scanning with the assistance of the network, and acquiring a high-quality reconstructed image. According tothe method, reconstruction blurring caused by respiratory movement and noise and artifacts caused by data acquisition angle loss can be reduced to a great extent, the scanning period can be shortened.The radiation damage to a subject can be reduced, the quality requirements of clinical analysis and diagnosis can be met, and the tracking efficiency of lung tumors can be improved.