15results about How to "Improving Imaging Efficiency" patented technology

The invention belongs to the technical field of pulsed eddy current nondestructive testing, and particularly discloses a ferromagnetic component pulsed eddy current detection imaging method and system, and the detection imaging method comprises the following steps: collecting a pulsed eddy current signal of a ferromagnetic component, carrying out logarithmic processing on the pulsed eddy current signal, and then carrying out secondary filtering processing; carrying out dimension reduction processing on the signal after the secondary filtering processing; calculating an amplitude standard deviation of the signal after dimension reduction processing of each time dimension, and selecting an effective imaging interval based on the amplitude standard deviation; performing Min-Max standardization processing on the data in the effective imaging interval; and calculating a pixel value based on a processing result obtained by Min-Max standardization, and carrying out homogenization processing on the pixel value to obtain a pulsed eddy current image. According to the invention, rapid imaging can be realized aiming at the pulsed eddy current detection signal of the ferromagnetic component, so that defects can be identified and preliminarily positioned.

The utility model provides an integrated mobile robot which comprises a mobile base which is used for moving to any position or pausing at any position according to a received control instruction and determining an orientation angle; the mechanical arm is used for upwards moving the target box body on the material taking position to a material placing position; the camera supporting rod is arranged on a rotating seat of the mechanical arm to rotate along with the rotating seat, or the camera supporting rod is arranged on the movable base, the camera supporting rod is at least provided with a first visual perception module, the first visual perception module comprises a multispectral camera and a laser radar, and the multispectral camera and the laser radar are arranged on the camera supporting rod; the image acquisition module is used for cooperatively realizing image information acquisition of the target box body and the box stack body formed by the target box body. The visual perception module can rotate along with the mechanical arm and can also rotate through the camera supporting rod, the imaging efficiency of the target box body can be improved, and then the grabbing efficiency of the mechanical arm on the target box body is improved.

The invention provides a space target rapid three-dimensional imaging method and system based on active and passive combination, and can be applied to the field of imaging detection. The method comprises steps that based on a target observation line-of-sight direction, an imaging device is utilized to carry out image acquisition on a target object, a vertical image and a horizontal image are acquired, the vertical image is acquired based on an optical camera and a passive optical technology, and a plane where the vertical image is located is vertical to the target observation line-of-sight direction; determining first relative motion information of the target object and the optical camera; determining second relative motion information of the target object and the synthetic aperture laser radar; performing size calibration on the vertical image and the horizontal image according to the first relative motion information and the second relative motion information to obtain a target vertical image and a target horizontal image; and based on the association information between the target vertical image and the target horizontal image, carrying out orthogonal fusion on the target vertical image and the target horizontal image to obtain a target three-dimensional image.

The application discloses a kind of acoustic-electric tomography image imaging algorithm based on two-point gradient iterative method, and the innovation is to improve the calculation efficiency by two-point gradient iterative method, and the accuracy of image reconstruction is improved by the selection of regularization term.The core of the application is to use the characteristics of acoustic-electric hybrid imaging, that is, when the boundary detection current is applied, the power density data is obtained by using ultrasonic disturbance, so as to obtain the internal data of biological tissue.At the same time, according to the different characteristics of different biological tissue conductivity, different regularization terms are introduced, two-point gradient iterative method is established, and high-resolution reconstruction of biological tissue conductivity is realized.Compared with the traditional acoustic-electric imaging method, the application reduces the calculation and storage cost of multiple observation currents, improves the imaging efficiency.And the iteration process is modified by using a special regularization term, so that the boundaries or geometric characteristics of different tissues can be accurately determined, and the imaging accuracy is improved.

This utility model relates to the field of medical device technology, and in particular to an intraoral scanning mounting structure and an intraoral scanning device. The intraoral scanning mounting structure includes: a housing with a mounting position, on which a protrusion is provided; an electro-optical component is mounted on the mounting position, and a groove is formed on the circumferential edge of the electro-optical component for the protrusion to extend into, thereby positioning the electro-optical component; an elastic component includes a first elastic member and a second elastic member, which are independently provided. The first elastic member is clamped between the mounting position and a first wall of the electro-optical component, and the second elastic member is clamped between the mounting position and a second wall of the electro-optical component, thus satisfying the installation and fixing requirements while allowing the electro-optical component to move within the mounting position due to thermal expansion and contraction; in the circumferential direction of the electro-optical component, a groove is provided between the portion of the first wall that abuts against the first elastic member and the portion of the second wall that abuts against the second elastic member, thereby ensuring the installation accuracy requirements of the electro-optical component.

The present application relates to the technical field of pipeline defect detection, in particular to a frequency-wavenumber domain imaging method suitable for high-temperature pipeline defect detection, which mainly solves the technical problem that the existing FK domain imaging method is difficult to be applied to high-temperature pipeline defect detection imaging. The frequency-wavenumber domain imaging method suitable for high-temperature pipeline defect detection comprises the following steps: signal acquisition; data preprocessing and gated direct wave signal interception; construction of time scale scaling factor; acquisition of frequency-dependent amplitude compensation factor; phase velocity extraction; gated defect echo signal interception, frequency domain transformation, frequency-dependent amplitude compensation and wavenumber domain transformation; Stolt mapping reconstruction; two-dimensional inverse Fourier transformation; normalization and image display. The imaging method effectively improves the imaging efficiency and significantly improves the imaging position error and focusing state.

The invention relates to the technical field of millimeter wave near-field imaging, in particular to a sparse MIMO array near-field imaging method based on distance quantization coding acceleration, and the method comprises the steps: obtaining original echo data; the pre-calculated pulse peak index and the phase compensation item are subjected to compression coding and fixed-point quantization and are pre-stored in a high-speed storage resource; performing oversampling and pulse compression on the echo data; according to the current pixel point and the antenna pair serial number, peak position data and a phase compensation item are extracted from the pre-calculation result for phase compensation, and a phase compensation result is obtained; and traversing all antenna pairs to obtain a reconstruction value of the current pixel point, and traversing all pixel points in the target imaging scene to obtain a final imaging result. Therefore, the problem that time consumption of non-linear calculation in a related BP method is large is solved, operation codes related to non-linear variables can be effectively quantized and pre-stored in high-speed storage resources, and the method is suitable for hardware acceleration.

The application relates to a deep learning microwave imaging method fusing virtual antenna data, comprising the following steps: a two-dimensional electromagnetic scattering model is established, and original scattering field observation data are collected under a sparse antenna configuration; the original scattering field observation data are processed by adopting a Fourier zero padding (FDZP) method, virtual scattering field data based on FDZP expansion are obtained, and a data set is further constructed; an FDZP-UNet network is constructed; the data set is input into the FDZP-UNet network, target dielectric constant prediction is output, and a high-resolution reconstructed image is further obtained. The deep learning microwave imaging method fusing virtual antenna data provided by the application comprehensively considers three performance indexes of imaging fidelity, system hardware cost and reconstruction efficiency, realizes virtual aperture expansion at a physical level through the Fourier domain zero padding method, effectively makes up for the physical information loss caused by sparse sampling, and greatly reduces the dependence on a large-scale physical antenna array.

An image decoding method according to this document comprises a step of deriving, for transform coefficients, modified transform coefficients on the basis of inverse secondary transform, wherein: the step of deriving the modified transform coefficients may comprise a step of deriving a transform kernel to be applied to the inverse secondary transform; on the basis of both the horizontal and vertical lengths of a target block being greater than or equal to 8 and the horizontal or vertical length being 8, the transform kernel may be set to a 64 × 32 matrix; and on the basis of both the horizontal and vertical lengths of the target block being 8, a 64 × 16 matrix sampled from the 64 × 32 matrix may be applied to the inverse secondary transform of the target block.

This invention discloses a multi-channel light-slice microscopic imaging device. The image acquisition module includes multiple image detectors. A laser array emits multiple excitation beams of different wavelengths, which are then combined into a single incident beam by an optical path module. This incident beam passes through a scanning galvanometer and an imaging module before illuminating the sample on a stage mounted on a three-dimensional displacement platform. The imaging objective in the imaging module collects fluorescence signals from the sample, which are then split by the beam-splitting module to form multiple fluorescence signals of different wavelengths and projected onto the multiple image detectors. Each image detector forms an image imaging channel. Multiple image imaging channels receive multiple fluorescence signals of different wavelengths and transmit them to the corresponding image detectors. The three-dimensional displacement platform enables three-dimensional displacement movement of the sample slice. By employing a strategy of continuous motion imaging of the sample slice and simultaneous detection and acquisition of fluorescence signals by multiple image detectors, the time interval between sample preparation and imaging, as well as the pauses during imaging, can be reduced.

The application discloses a kind of single-pixel imaging method and device based on array light source, including obtaining the illumination light field of array light beam and the back light intensity detection value of the reflection back after array light beam irradiation on target object;Based on back light intensity detection value and illumination light field, reconstruct target image as rough target image;Based on rough target image and back light intensity detection value, reconstruct target image using depth neural network without training, output final target image.The present application realizes high efficiency imaging and long distance target detection based on array light source, while removing image noise, artifact and periodicity by introducing depth neural network, further improves the imaging quality and imaging efficiency of single-pixel imaging.

The invention relates to a low-field magnetic resonance diffusion imaging reconstruction method. The method comprises the following steps: acquiring K space data of low-field magnetic resonance; mapping the acquired K space data to a standard Cartesian coordinate system by adopting a grid interpolation method of a Gaussian kernel, and weighting each radial sampling point through a Gaussian kernel function to ensure that the data is smoothly distributed on a standard grid; image reconstruction is carried out based on image prior constraints by using the interpolated K space data, and the image prior constraints comprise a low-rank structure and sparse prior information of the image; parameters of low-rank and sparse components are optimized step by step by using a mixed kernel function, and constraint is carried out by combining a low-rank structure and sparse prior information of an image, so that the image reconstruction effect is improved. The invention also relates to a low-field magnetic resonance diffusion imaging reconstruction system. According to the invention, the image quality and the anti-noise performance can be obviously improved.

The present application relates to underwater observation and visual image processing technology, aiming to provide a kind of in-situ high-definition imaging method under turbid water environment based on hybrid feature fusion. Including: the construction CycleGAN network architecture's bidirectional cyclic antagonism system model, the model includes double generator, double discriminator and composite loss function module;By introducing physical turbidity distribution into spatial attention calculation, the hybrid fusion of water turbidity information and depth semantic features is realized;The model is trained, and the network parameter weight is updated by back propagation;The underwater turbid image to be processed is input into the trained model, and the high-definition clear underwater imaging result is output.The present application breaks through the technical bottleneck of blurred imaging, large noise and serious detail loss in low visibility water environment, and can simultaneously solve the problems of color distortion, uneven brightness and detail blur, improve detection accuracy, practical efficiency and reliability, effectively improve the operation perception ability of underwater robot.

The invention discloses a CT image reconstruction method and system based on maximum residual error expansion oblique projection, and belongs to the technical field of medical image processing. Splitting the to-be-reconstructed CT image matrix according to columns to form a plurality of linear equations; setting the initial values of the auxiliary vector zk and the solution vector xk as z0 and x0; in the first iteration, a working column and a working row are selected according to a maximum residual index, and z0 and x0 are iterated through an orthogonal projection method; then working columns / rows continue to be selected according to the maximum residual error index, new column / row projection direction vectors are calculated through the working columns / rows of two adjacent steps, the auxiliary vector zk is iterated through the new column projection direction vectors and the appropriate step length, and if the new row projection direction vectors are not zero vectors, the new row projection direction vectors are not zero vectors; if so, iterating the solution vector xk through a new row projection direction vector and a proper step length, otherwise, iterating the solution vector xk through orthogonal projection; outputting a final solution vector xk + 1 when a termination condition is met; and combining all the final solution vectors according to the column xk + 1 to obtain a reconstructed CT image. The CT image reconstruction precision and the imaging efficiency are improved.

The application discloses a spectral super-resolution method based on a variational autoencoder, and comprises the following steps: inputting a target RGB image into a first convolutional layer of a spectral super-resolution network to obtain a plurality of feature maps containing shallow layer features corresponding to the target RGB image; inputting the feature maps into an attention mechanism module of the spectral super-resolution network to perform feature mapping on the feature maps; inputting the feature maps subjected to the feature mapping into a second convolutional layer of the spectral super-resolution network to reconstruct a target hyperspectral image corresponding to the target RGB image; and training the spectral super-resolution network according to a first hyperspectral image and a first RGB image extracted by the variational autoencoder, wherein the first hyperspectral image is reconstructed by the untrained spectral super-resolution network on the basis of a training RGB image. The target RGB image is acquired by a common camera, so that the image acquisition difficulty is low, and the hyperspectral image is reconstructed by the spectral super-resolution network, so that the imaging time is short, and the method can be widely applied to the field of spectral super-resolution.