49 results about "Projection reconstruction" patented technology

The invention relates to the technical field of image data processing, and discloses a few-sample scene-oriented industrial product surface defect image analysis method, which comprises the following steps of: obtaining surface gray level image data of a product to be analyzed, calculating a structure tensor matrix and generating an anisotropy degree graph; searching similar blocks in a preset search neighborhood, and constructing a local texture data matrix; performing singular value decomposition on the local texture data matrix to extract a main subspace; constructing a projection operator and utilizing the projection operator to carry out orthogonal projection reconstruction on the local image block to generate a reconstructed background image block; according to the method, through an orthogonal subspace projection mechanism, good product textures and defect signals are separated, random noise is removed, meanwhile, high-frequency structural features are completely reserved, and the method is high in robustness, high in robustness and high in robustness. And the defect detection precision of a complex texture surface in a few-sample scene is improved.

The invention relates to a remote sensing image super-resolution reconstruction method based on cross-scale Mama, and belongs to the technical field of remote sensing image super-resolution reconstruction. The method comprises the following steps: introducing a CSRSSG cross-scale residual state space group and a CARR channel attention back projection reconstruction module to improve an original MambaIR model, and obtaining a cross-scale Mamba super-resolution reconstruction model; extracting shallow layer features of the remote sensing image to be reconstructed based on a cross-scale Mama super-resolution reconstruction model; based on a cross-scale Mama super-resolution reconstruction model, deep layer features are extracted from the shallow layer features; and adding the shallow features and the deep features element by element to obtain residual connection features, and inputting the residual connection features and the remote sensing image to be reconstructed into a cross-scale Mamba super-resolution reconstruction model for reconstruction to obtain a super-resolution reconstructed remote sensing image. The method can effectively enhance the extraction and expression capability of the model on associated information among different scale features in the remote sensing image, and improves the super-resolution reconstruction quality of the remote sensing image.

The invention discloses a multi-modal data enhancement method and system based on feature space alignment. The method comprises the following steps: collecting modal original data streams of multiple unmanned aerial vehicles; reversely deducing an internal generation rule from a data final state, and constructing a sparse coding feature representation library through causal pilot signal extraction and singular trajectory analysis; constructing an adaptive weight map according to the feature representation library, and identifying a space alignment path through dynamic calibration processing to generate an alignment imbalance index; performing cross information entropy analysis to establish a collaborative enhancement chain, and identifying a collaborative enhancement mode based on the information coupling degree to generate a complementary enhancement vector; performing multi-dimensional projection reconstruction on the complementary enhancement vector, and determining feature mapping probability distribution through diffusion time inversion to obtain a multi-dimensional projection feature map; carrying out nonlinear enhancement processing to generate an enhancement expansion mode, and identifying an enhancement key node to construct an enhancement regulation and control sequence; and outputting hierarchical enhancement data based on the regulation and control sequence and the projection feature map, and realizing accurate space alignment and collaborative enhancement of multi-sensor data.

The invention provides a generalization segmentation method and system in the medical image field, and belongs to the technical field of medical image processing. According to the method, after multi-center medical image data is preprocessed, a network architecture based on U-Net and ResNet is constructed for feature extraction and segmentation prediction. The image features are converted into frequency domain representation through a frequency domain decomposition module, and low-frequency amplitude and phase information are separated. And constructing a style representation space by using a frequency domain style projection reconstruction module, projecting an unknown domain style to a source domain style library, and disturbing and expanding feature distribution. Through a multi-stage comparison decoupling module, in combination with class prototype comparison learning and pixel-level semantic comparison learning, decoupling of domain-independent and domain-related features is realized. And finally, combining the segmentation loss with the contrast learning loss optimization model to generate a final segmentation result. By the adoption of the method, the problem of medical image multi-center data field deviation is solved through frequency domain style alignment and comparison decoupling.

The invention discloses a coastal tidal flat elevation measurement method based on an orthographic reconstruction technology, and the method comprises the following steps: laying image control points in a target monitoring area of a muddy tidal flat, and then measuring the elevation information of each image control point; an unmanned aerial vehicle is adopted to collect aerial photos of the area according to the planned flight route; an RTK base station is erected in the area to collect static POS data; obtaining static POS data and airborne POS data to calculate flight POS data; flight POS data and aerial photos are obtained, modeling is carried out in combination with elevation information of an image control point, and point cloud data and an orthoimage of the area are generated; and identifying a ponding area from the orthoimage, removing the ponding area from the point cloud data, and establishing a digital elevation model of the area to obtain tidal flat elevation information of the area. Therefore, the problem that large-range tidal flat elevation measurement cannot be carried out due to the limitation of the tidal falling time of the muddy tidal flat in the traditional artificial beach running is solved.

The invention discloses a method and a system for generating a long video with a controllable camera track based on a video diffusion model. Comprising a camera track and initial frame preparation stage, a point cloud construction and multi-view image generation stage, a scale factor alignment optimization stage, a camera motion prior injection and noise initialization stage, a diffusion inversion generation stage and a sliding window time consistency fusion stage. Three-dimensional camera track modeling, projection reconstruction and diffusion processes are combined, a video content generation process is explicitly guided to be aligned with a track path set by a user, and long video generation which is reasonable in structure, natural in vision and continuous in time is achieved.

The invention provides a multi-user VR video streaming media rate-based adaptive adjustment method and system, and relates to the technical field of VR, comprising the following steps: constructing a double-layer architecture of an edge layer and a user layer, carrying out video space-time segmentation and multi-quality coding on the edge layer, and realizing video adaptive transmission based on user motion information; a forward and reverse weighted user experience evaluation model is adopted to carry out optimal allocation on system resources; a quality balanced user subset is constructed in combination with time dimension equilibrium constraint, and resource dynamic optimization is carried out; the multi-dimensional resource verification model is used to realize video quality grade optimization distribution; and generating a panoramic frame through integrity verification, time sequence synchronization and spherical projection reconstruction. According to the invention, the service quality balance of multi-user VR video transmission is improved, the system resource overhead is reduced, and the user watching experience is enhanced.

The invention discloses a precipitation forecast correction method combining EOF projection and a U-Net network, and the method comprises the steps: extracting a main spatial mode of a precipitation abnormal field based on historical observation data, and carrying out the projection reconstruction of a mode forecast abnormal field on this basis, and obtaining a spatial structure with physical significance; an observation climate state is introduced to replace a mode climate state, so that systematic deviation of the mode is effectively eliminated; for residual terms which are not explained in the reconstruction process, U-Net is adopted for modeling and prediction so as to capture complex nonlinear error components in the mode; and finally, superposing a linear reconstruction result and a residual term of deep learning prediction to form a final rainfall forecast correction result. According to the method, a correction framework combining physical driving and data driving is constructed, and on the basis of keeping the physical interpretability of a traditional EOF method, a deep learning model is introduced to model a residual term, so that dual capture and correction of linear errors and nonlinear errors in mode forecasting are realized.

The invention discloses a three-dimensional full-wave field measurement method based on a single-point laser Doppler vibration meter, and the method achieves the multi-angle and automatic scanning of the surface of a structure through the installation of the single-point laser Doppler vibration meter at the tail end of a multi-degree-of-freedom mechanical arm and the combination of a preset scanning path control program. Under signal excitation, the measurement system obtains vibration response signals in different incident directions, and reconstructs a complete vibration component of the structure in a three-dimensional space in combination with a space vector projection reconstruction algorithm. Compared with a traditional measurement scheme in which three beams of laser are needed and accurate alignment is carried out, the system hardware structure is greatly simplified, the complexity and the operation difficulty of the measurement system are reduced, and the flexibility, the precision and the adaptability of three-dimensional wave field measurement are improved.

The invention discloses a cable safety monitoring and early warning system comprising the following modules: a disturbance injection module used for injecting a double-frequency disturbance electric signal; the temperature acquisition module is used for acquiring temperature change data of each monitoring point of the cable; the residual construction module is used for constructing a two-dimensional disturbance thermal residual spectrum; the thermal evolution analysis module is used for performing wavelet packet multi-scale decomposition on the disturbance thermal residual spectrum and generating a two-dimensional thermal evolution image through an improved back projection reconstruction algorithm; the first early warning calculation module is used for acquiring a thermal response function and calculating a time partial derivative to obtain a thermal evolution trend index as a first early warning factor; the vibration acquisition module is used for acquiring a reflection waveform data set; the second early warning calculation module is used for calculating an equal-amplitude decay time symmetry rate index as a second early warning factor; and the early warning generation module is used for generating a final early warning scheme according to a preset judgment rule. According to the invention, on the basis of dual-frequency disturbance excitation and an improved back projection reconstruction algorithm, intelligent early warning of cable monitoring is realized.

Disclosed in the present invention are a weighted analytic filtered back projection reconstruction method and system for asymmetric cone angle artifacts. The method comprises the following steps: dividing a reconstruction area into a plurality of weight regions on the basis of relative positions of a ray source ring and a detector ring; acquiring the projection data volume of voxel points in each weight area irradiated by X-rays; according to the projection data volume of the voxel points in each weight area irradiated by the X-rays, assigning a different initial weight to each weight area; performing smooth transition on the initial weight of each weight area by means of a transition weight to form a final weight assigned to each weight area; and according to different final weights of the weight regions, performing final weighted analytic reconstruction on projection data p (α, β, γ) to acquire a back projection image.

The present application relates to a kind of remote sensing image super-resolution reconstruction method based on cross-scale Mamba, belong to remote sensing image super-resolution reconstruction technical field.The method includes: introducing CSRSSG cross-scale residual state space group and CABR channel attention reverse projection reconstruction module are improved to original MambaIR model, obtain cross-scale Mamba super-resolution reconstruction model;Based on cross-scale Mamba super-resolution reconstruction model, extract the shallow feature of the remote sensing image to be reconstructed;Based on cross-scale Mamba super-resolution reconstruction model, extract deep feature from shallow feature;Element-by-element addition is carried out to shallow feature and deep feature, and residual connection feature is obtained, and residual connection feature and the remote sensing image to be reconstructed are input into cross-scale Mamba super-resolution reconstruction model and are reconstructed, and super-resolution reconstruction remote sensing image is obtained.The present application can effectively enhance the extraction and expression ability of model to the associated information between different scale features in remote sensing image, improve remote sensing image super-resolution reconstruction quality.

The present invention provides a method for evaluating printing effects in volumetric bioprinting projection, which includes: taking transverse slice samples, performing Radon transform on each transverse slice sample to obtain projection data, then using a to-be-determined window function to perform filtered back-projection transform on the projection data obtained from each transverse slice sample, obtaining a back-projection reconstruction image, normalizing and pixelating it to obtain a simulated printed transverse slice, performing an operation on the simulated printed transverse slice and the transverse slice sample at the corresponding height, obtaining 254 similarity evaluation values corresponding to 254 simulated printed transverse slices at each height, respectively calculating the average value of the M similarity evaluation values corresponding to M simulated printed transverse slices under the same critical curing brightness value, and taking the optimal average value as the printing evaluation index of this to-be-determined window function. Comparing the printing evaluation indexes of different to-be-determined window functions, and taking the window function corresponding to the optimal printing evaluation index as the optimal window function.

The present invention discloses a three-dimensional measurement method for high-reflection surface structured light based on main and auxiliary dual-view multi-gray-level projection. Based on the light-reflecting characteristics of the high-reflection surface, a three-dimensional reconstruction system for structured light with main and auxiliary dual-views is established. By additionally introducing an auxiliary reconstruction view, it is possible to avoid image overexposure caused by specular reflection during the single-projection reconstruction process. For the regions where the image is overexposed in the main reconstruction view, image reconstruction in the auxiliary view is used for compensation, enabling more points to be reconstructed in a single view, improving the measurement speed and efficiency. At the same time, the method of sharing the same projector also saves hardware costs well. Combining with the multi-gray-level projection method, by gradually reducing the projection intensity, points that are overexposed in both the main and auxiliary dual-views are reconstructed, thereby reconstructing a complete high-reflection surface. Meanwhile, a method for discriminating overexposed points in dual-views is proposed to determine whether the point is overexposed in both the main and auxiliary views.

The invention relates to the technical field of multi-source servo intelligent calibration, and particularly discloses a servo control system autonomous intelligent calibration method based on multi-source information fusion, and the method comprises the steps: obtaining the equipment attribute information of all servo equipment and multi-source sensor data synchronized through a global time protocol in response to a system starting or calibration instruction; and according to the physical space distribution data and the transmission connection relation data in the equipment attribute information, constructing an equipment incidence matrix representing the coupling strength between the equipment. According to the method, various cross-device coupling conflict characteristics including transmission, electromagnetism and loads can be intelligently recognized, external interference noise is accurately stripped from sensor data through dynamic weight adjustment and a coupling projection reconstruction mechanism, it is ensured that a control system only carries out calibration for the real intrinsic error of the device, and the reliability of the system is improved. Therefore, false compensation and system instability caused by coupling interference are avoided.

The invention provides a poria cocos producing area identification method based on poria cocos Raman spectrum principal component analysis. The method comprises the following steps: firstly, acquiring Raman spectrums of poria cocos from different producing areas by using a Raman spectrometer, carrying out pretreatment such as averaging, base line removal and feature screening on the original spectrums, and constructing a spectrum data set and a test set of the poria cocos from different producing areas; then, principal component analysis is carried out on the spectral data sets of the poria cocos from different producing areas, and principal components with the spectral cumulative contribution rate larger than 90% are reserved to be used for constructing a feature vector matrix; and finally, respectively performing projection reconstruction on a test spectrum on the constructed feature vector matrix to obtain recovery spectrums of the test spectrum in different producing areas, comparing Euclidean distances between the recovery spectrums in different producing areas and the test spectrum, and determining that the test spectrum belongs to the producing area corresponding to the recovery spectrum with the minimum Euclidean distance. The method is rapid, simple, convenient and high in accuracy, does not need a large number of samples for training, and has great application potential in the aspect of identifying the origin of poria cocos.

The application discloses a kind of based on deep learning cascade enhancement multi-bandwidth photoacoustic imaging system and method, the system is by pulsed laser, two half annular array probes, multichannel data acquisition card and central computer composition, pulsed laser is used to excite the light-absorbing body in target area, generates photoacoustic signal;Two half annular array probes respectively have high-frequency probe with center frequency 5.5MHz and 2.5MHz low-frequency probe, high-frequency probe is used to obtain high-resolution image, low-frequency probe is used to detect larger light-absorbing body and low signal-to-noise ratio structural features;Multichannel data acquisition card is used to carry out digitization processing to photoacoustic signal received by two half annular array probes, and the data after processing is transmitted to central computer, reconstructs photoacoustic image based on filter back-projection reconstruction algorithm.The multi-bandwidth photoacoustic imaging system of the application shows very high image reconstruction capability, provides more accurate solution for biomedical imaging.

A sparse projection CBCT reconstruction method, device and equipment and readable storage medium, which comprises the following steps: obtaining X-ray projections of a target patient at multiple angles; obtaining a whole tissue mask prediction projection for reconstruction and a hard tissue mask prediction projection for reconstruction according to a preset tissue projection prediction model and the X-ray projections; supervising and training a preset neural field network initial model according to the X-ray projections, the whole tissue mask prediction projection for reconstruction and the hard tissue mask prediction projection for reconstruction to obtain a neural field network model corresponding to the target patient; obtaining whole tissue gray scale data of each coordinate point in a three-dimensional image to be reconstructed according to the neural field network model and coordinate information of each coordinate point in the three-dimensional image to be reconstructed, and forming the three-dimensional image to be reconstructed. Different tissues in CBCT are modeled by a multi-unit network, the interpretability and controllability of the neural field network model are improved, and the method has strong practical significance.

The application discloses a kind of multi-projection light field image synthesis method and system, it is related to image synthesis technical field, the method includes: obtaining the coordinates of projector array, the bottom of holographic screen and actual camera array;Determine multiple display restoration light;The display restoration light of each projector is carried out image synthesis, obtains multiple target images, and the holographic screen receives the projection of projector on target image and reconstructs three-dimensional light field;The display restoration light of a projector is carried out image synthesis process is: according to the column pixel of all display restoration light of projector corresponding target image determination column pixel synthesis process is: determining the ideal camera of display restoration light coordinates;According to the ideal camera coordinates and the coordinates of actual camera array determine the actual coordinates of two actual cameras;From according to display restoration light, active range and actual coordinates determine multiple actual imaging pixels.The application improves the accuracy of projection image synthesis and projection reconstruction light field display effect.

The present invention discloses a fast pipeline computed tomography (CT) method based on deep learning. The steps are as follows: Place multiple objects on a pipeline CT inspection table, perform synchronous CT scanning based on sparse sampling to obtain a pipeline CT sparse projection sequence; Interpolate the pipeline CT sparse projection sequence into a complete projection sequence with missing information; Use deep learning technology to process the projection sequence with missing information, supplement the projection information, and obtain a projection sequence with complete information and dimensions; Perform segmentation processing on the complete projection sequence to obtain the projection sequence corresponding to each object; Use the filtered back-projection reconstruction algorithm to reconstruct the projection sequence of each object separately to obtain the final tomographic reconstruction image. The present invention greatly shortens the imaging time; At the same time, it ensures the reconstruction quality of the fast pipeline CT imaging technology, making it meet the requirements of high-quality and high-efficiency industrial non-destructive testing.

A method includes obtaining k-space data acquired by an MRI scanner from a single channel body coil utilizing a multi-shot EP-DWI pulse sequence and sampling the k-space data for a plurality of shots so that for each shot both a central k-space is fully sampled to form a central calibration region and an outer k-space is partially sampled by a factor equal to a number of shots. The method includes reconstructing an initial fully sampled k-space estimate for each shot utilizing both partial Fourier constant sampling and projection on convex sets reconstruction, wherein the plurality of shots is treated as a plurality of channels for filling in missing k-space for a respective shot. The method includes utilizing a low-rank regularization algorithm in an iterative manner to generate a reconstructed image for each shot, wherein the initial fully sampled k-space estimate for each shot is utilized as an initial guess.

The invention discloses an airport clearance area scene twin modeling and dynamic target safety prediction method, belongs to the technical field of digital twin and computer modeling, and solves the problem of high difficulty in automatic modeling of a large airport scene. The method comprises the following steps: acquiring point cloud data of a static target in an airport clearance area and preprocessing the point cloud data; performing curved surface reconstruction by adopting a point cloud projection reconstruction method to obtain rapid three-dimensional modeling of the static target, including the three-dimensional coordinate of the static target and the semantic relationship of the static target; performing dynamic target recognition on the obtained video of the airport clearance area in real time by using an improved YOLOv8 network to obtain classified dynamic targets and a semantic relationship between the dynamic targets; performing real-time positioning on the identified dynamic target based on the improved BP network to obtain a three-dimensional coordinate of the dynamic target; and fusing the three-dimensional coordinates of the static target and the dynamic target in an un-ity engine to realize twin modeling of the clearance area, and dynamically updating in real time. The method is used for airport clearance area scene twin modeling.

This application relates to the field of computer science and discloses an artificial intelligence task scheduling system and method for edge computing. The system includes modules for semantic manifold kernel extraction, heterogeneous resource potential field construction, gradient tensor projection reconstruction, and streaming gradient optimization scheduling. The method utilizes a potential energy function to map heterogeneous network resources into a scalar potential field and calculates negative gradient vectors; it generates a semantic manifold kernel mask based on the semantic entropy density of feature channels, and combines it with an environmental pressure tensor mapped by the negative gradient magnitude to project and reconstruct the model weights to generate a dynamic inference model; it synthesizes a scheduling vector using the negative gradient vector and Lyapunov drift correction to determine the next-hop target node. This invention achieves adaptive model compression and network load balancing while preserving key semantic features through potential field gradient collaborative optimization of the model structure and transmission path, solving the problem of balancing transmission efficiency and inference accuracy in edge computing scenarios.

The present invention relates to the technical field of rubber performance evaluation, and in particular to a method for analyzing and evaluating performance data of rubber materials. The method comprises the following steps: acquiring X-ray scanning data of a rubber sample; performing phase contrast imaging on the X-ray scanning data of the rubber sample to obtain projection data of the rubber sample; performing back-projection reconstruction on the projection data of the rubber sample to obtain three-dimensional structural data of the rubber material; subjecting the rubber sample to multi-parameter environmental simulation loading to obtain environmental response data of the rubber material; dynamically collecting the environmental response data of the rubber material to obtain deformation process data of the rubber material; synchronously recording and analyzing the deformation process data of the rubber material to obtain a stress-strain characteristic diagram of the rubber material; and performing polarization spectral excitation on the rubber sample based on the stress-strain characteristic diagram of the rubber material to obtain fluorescence data of the rubber molecular chain. This invention improves the reliability and accuracy of rubber material performance evaluation.

The application discloses a kind of self-adapting direction and step of multi-energy spectrum CT iterative reconstruction method with scattering correction, comprising: step 1, setting multi-energy spectrum CT projection reconstruction model with scattering correction;Step 2, using iterative algorithm to solve out the increment of base material distribution projection value angle by angle, update the current base material image to new base material image;Wherein, through self-adapting direction and step to quickly obtain convergent base material projection image.The scheme provided by the application is used to remove scattering, and the result of base material decomposition is more accurate, and the reconstruction result artifact is less, which lays a foundation for quantitative analysis.

The present invention belongs to the field of biomedical imaging, and specifically relates to a multi-color magnetic particle imaging method and system based on magnetic field free line projection decoding. The method aims to solve the problem that the existing technology has no method for using magnetic field free lines to perform relaxation-based multi-color imaging, resulting in low imaging efficiency and quality. The method of the present invention comprises: placing N types of magnetic nanoparticles at the center of the imaging field of view for scanning, and using time-frequency analysis to obtain the central spectrum line of the time-frequency spectrum; scanning the imaging object, collecting the voltage response signal and performing time-frequency analysis to obtain the time-frequency spectrum; constructing and solving a system of linear equations to obtain various unknown quantities; discretely rotating the magnetic field free lines by P angles to obtain the content distribution of the N types of magnetic nanoparticles at each angle; constructing sinusoidal diagrams of the N types of magnetic nanoparticles, performing back-projection reconstruction to obtain a multi-color image, and superimposing them to obtain a reconstructed image. The present invention realizes relaxation-based multi-color imaging using magnetic field free lines, improving imaging efficiency and quality.

The present application relates to a kind of step edge high-precision three-dimensional reconstruction method, using the parallel single-pixel imaging principle based on projection reconstruction, in combination with edge detection algorithm and adaptive bidirectional projection strategy solve the light ray aliasing problem of step edge, to realize the high-precision acquisition of step edge three-dimensional stereo matching point.First, the sequence of phase-shifted fringe pattern of bidirectional periodic continuation is generated, is projected to measurement scene by projector in turn, and the coded image of scene is obtained by camera synchronous acquisition.Then, the lower frequency image in fine positioning fringe image is extracted to calculate absolute phase map, carries out edge extraction, generates edge mask image.Secondly, the one-dimensional projection function calculation of bidirectional is carried out to the edge pixel in mask image, further calculate the intersection of projection line to obtain multiple intersection points of bidirectional projection line as candidate matching point, and two direct matching points from upper and lower surfaces of step edge are screened out by epipolar distance constraint.Finally, using calibration parameter and matching point, three-dimensional reconstruction point cloud of step edge is obtained by solving stereo matching algorithm.The application effectively solves the light ray aliasing problem of step edge caused by camera visual angle obstruction in triangulation camera-projector system, reconstructs the upper and lower surfaces of step edge completely, and improves the three-dimensional measurement precision of step edge.Based on the optimized bidirectional projection reconstruction parallel single-pixel imaging, while ensuring the measurement accuracy, the measurement efficiency is improved, which is conducive to the application and popularization of the method in actual industrial measurement field.

The invention discloses a remote sensing image semantic segmentation method and system guided by linear positioning attention, and the method comprises the following steps: carrying out the direction-sensitive feature projection reconstruction of an input feature map through a linear positioning attention mechanism, and adjusting the direction distribution of a feature vector through a nonlinear power positioning function, the low-rank limitation is broken through in combination with depth separable convolution, and direction enhancement features are generated; a deformable edge attention mechanism is used, a geometric self-adaptive edge attention graph is generated based on a deformable multilayer perceptron, edge features are enhanced through serial secondary attention modulation, and edge enhancement features are obtained; a symmetric coding and decoding network is constructed, an encoder uses ResNet18 to extract multi-scale features, a decoder uses a multistage linear positioning ViT block to perform step-by-step up-sampling and fuse with jump connection features, shallow jump connection introduces edge enhancement features, and finally a pixel-level semantic segmentation map is output through a segmentation head.

The solar cell panel invisible defect detection device is characterized in that a solar cell panel to be detected generates photoluminescence under the irradiation of a photoluminescence excitation light source, and reflected light under the irradiation of a natural light source provides spectral information of the surface of the solar cell panel; ambient light interference is corrected through an optimization algorithm of a data processing system; the calculation tomography spectrometer obtains multi-angle spectral projection data through an interferogram, and provides basic data for generation of a spectral image cube; through combination of Fourier transform and a computational tomography projection reconstruction algorithm, a spectral image cube is generated by utilizing spectral data of a solar cell panel under the conditions of photoluminescence and natural reflection light, and invisible defects are detected. The interference problem in the sunlight environment is effectively solved by utilizing the computational tomography spectral imaging technology, and the method is suitable for online detection of invisible defects of the outdoor operation solar cell panel.

The invention relates to a two-dimensional projection potential reconstruction method and device based on a local orbit. The method comprises the following steps: acquiring high-resolution image data and diffraction pattern data of a sample under different imaging conditions, and inputting the high-resolution image data and the diffraction pattern data into a pre-constructed two-dimensional projection potential reconstruction model for multi-slice simulation imaging calculation and image calculation processing to obtain two-dimensional projection reconstruction information of the sample. The diffraction pattern is more sensitive to the tilting angle, so that the tilting angle deviation of the sample can be corrected through an algorithm from the diffraction pattern, and high-frequency information which does not exist in the microscopic image can be obtained through the diffraction pattern. By using the local orbit, the solution space of the projection potential is constrained by using the prior information, namely the locality of the atomic electrostatic potential, so that the defects that the existing projection potential reconstruction technology depends on the periodicity of a known sample and is poor in convergence are overcome, and meanwhile, the signal-to-noise ratio is improved.