134results about How to "Improve reconstruction quality" patented technology

The invention relates to the technical field of forest fire recognition and early warning, in particular to a forest fire early warning method and system based on a super-resolution neural operator, and the method specifically comprises the following steps: extracting an initial feature map # imgabs1 # of a to-be-recognized low-resolution forest fire image # imgabs0 #; constructing a super-resolution feature capture network, three HAFM hierarchical attention fusion modules, an MSMO multi-scale mapping operator module, a super-resolution feature fusion module and a super-resolution image reconstruction module, inputting the initial feature map # imgabs2 # into the super-resolution feature capture network, and finally outputting a super-resolution image # imgabs3 #; and constructing a forest fire recognition module, inputting the super-resolution image # imgabs4 # into a ResNet-18 neural network model to obtain a recognition feature # imgabs5 #, finally performing full connection layer processing on the recognition feature # imgabs6 # to obtain a recognition result of the forest fire image, and judging whether to perform early warning or not according to the recognition result. According to the invention, the problem that a low-resolution fire image cannot be accurately identified and timely early warned in existing forest fire identification and detection can be solved.

The invention discloses a pig single-view three-dimensional reconstruction method based on deep learning, and the method is called Magfup. In order to solve the problems that single-view three-dimensional reconstruction viewpoint learning is unstable and training is difficult due to lack of real three-dimensional data, an implicit-explicit combined three-dimensional representation method is adopted, high-dimensional data features subjected to super-resolution optimization are combined, a high-density pig three-dimensional grid can be reconstructed only through a single image, and the problem that training is difficult is solved. And data acquisition cost and equipment requirements are reduced. According to the method, the three-dimensional grid surface is optimized through the Laplacian smoothing method, and the reconstruction quality is improved. According to the invention, the model can learn the general shape and posture characteristics of the pig, predicts the three-dimensional posture, shape and texture information of the specific pig, and more accurately and comprehensively reflects the behavior characteristics of the pig. Therefore, the method can adapt to complex and changeable environments and pig behaviors in a pig farm, and a more comprehensive and accurate data basis is provided for pig behavior analysis.

The invention relates to the technical field of audio coding and decoding, and discloses an audio coding and decoding method and device based on a neural network, equipment and a storage medium. The method comprises the following steps of: firstly, gradually quantizing residual information of an audio signal by introducing an improved residual vector quantization method, and more finely retaining audio characteristics in a compression process; then, a loss function fusing reconstruction loss, perception loss, residual vector quantization loss and commitment loss is adopted, so that the reconstruction quality of the audio signal under different bit rates is remarkably improved, and efficient audio compression can be realized while high tone quality is ensured; and finally, an entropy coding module is introduced after quantization, so that the code rate of coding is further reduced, and the bandwidth requirement of audio real-time transmission is remarkably reduced.

The invention provides a fast magnetic resonance reconstruction method and system based on a multistage wavelet convolution network, and particularly relates to the technical field of magnetic resonance imaging and image reconstruction, and the scheme comprises the steps: collecting multi-channel under-sampling K space data; inverse Fourier transform is carried out on the multi-channel under-sampling K space data to obtain an under-sampling magnetic resonance image; and inputting the undersampled magnetic resonance image into a trained multi-stage wavelet convolutional network for reconstruction to obtain a reconstructed magnetic resonance image, the multi-stage wavelet convolutional network being constructed based on multi-stage wavelet transform and a convolutional neural network. According to the scheme, the magnetic resonance K space data is collected in an undersampling mode, the scanning time can be shortened, the multi-stage wavelet convolution network can effectively reserve high-frequency details of the image in the reconstruction process, and therefore the reconstruction quality and efficiency of the multi-channel magnetic resonance image can be remarkably improved on the whole.

The invention provides a satellite video super-resolution reconstruction method and system oriented to multiple degradation processes, and the whole process of the method comprises the steps: generating corresponding low-resolution video data through a satellite video random degradation module, and carrying out the feature extraction of the low-resolution data through a feature extraction module, the method comprises the following steps of: extracting features, transmitting each frame feature of a whole video sequence by adopting a high-order grid bidirectional propagation scheme according to the extracted features, realizing feature alignment of video data by using a feature alignment module, and finally performing pixel recombination on the aligned features, and adding the aligned features with low-resolution features after up-sampling to obtain a reconstructed high-resolution image. Three satellite video data experiments show that the high fidelity of the satellite video can be effectively recovered, the reconstruction quality of the remote sensing image is remarkably improved, and meanwhile, the method has a certain denoising capability.

The invention discloses an object three-dimensional reconstruction method and device based on deep learning, and relates to the technical field of three-dimensional reconstruction, and the reconstruction method comprises the steps: collecting a target object video of a target object through a camera imaging device, determining a target image set based on the target object video, and obtaining a target image set based on the target image set; the method comprises the steps of obtaining a target image set, determining target camera parameters and an initial camera pose, correcting the initial camera pose to obtain a target camera pose, inputting the target image set, the target camera parameters and the target camera pose to a preset neural radiation field model, outputting target radiation field information, and constructing an object three-dimensional model of a target object based on the target radiation field information. According to the invention, the technical problem of low accuracy of three-dimensional reconstruction of an object in the prior art is solved.

The invention relates to a transparent object reconstruction and new view angle synthesis method based on 3DGS. The transparent object reconstruction and new view angle synthesis method comprises the following steps: S1, reconstructing dense three-dimensional point cloud and camera pose information of a data set according to an image sequence; s2, reconstructing a three-dimensional scene by using the three-dimensional point cloud and the camera pose information and using a 3DGS method, and generating a three-dimensional Gaussian kernel to represent an input scene; s3, introducing a Sobe l edge detector, extracting edges of transparent objects in the rendered image and the real image generated by the three-dimensional Gaussian kernel, and calculating edge loss functions of a rendered edge image and a real edge image; s4, jointly optimizing the three-dimensional scene reconstruction model by combining the edge loss function and the original 3DGS loss function; and S5, performing overall optimization on the scene and outputting a final three-dimensional reconstruction result. The objective of the invention is to improve the reconstruction quality of the 3DGS model so as to improve the three-dimensional reconstruction effect of the transparent object.

The invention discloses a credible surface defect detection method and equipment based on a Bayesian condition diffusion model. The method comprises the following steps: constructing a training data set and a test data set; constructing and training an industrial product surface defect detection model based on uncertainty perception of a conditional diffusion model, inputting a test picture into the model for detection, and respectively obtaining a defect prediction image and an uncertainty prediction image of an input result; sorting the original input image, the defect prediction result and the uncertainty prediction result to obtain a conditional guidance image during second iteration; and inputting the obtained condition guide image and the to-be-detected image into the condition diffusion model to obtain a final defect prediction image and uncertainty measurement. According to the method, uncertainty measurement is carried out on a result generated by the model based on Laplace approximation, a detection result is optimized by utilizing the uncertainty of an output result, and then iteration is carried out to improve the credibility of a final detection result.

The invention provides a diffusion-driven channel adaptive point cloud semantic communication method, which belongs to the technical field of wireless communication, and comprises the following steps: obtaining a training point cloud data set, constructing a point cloud feature extraction network based on a hypergraph convolutional neural network as a semantic encoder, and constructing a channel adaptive enhancement module as a channel decoder, a channel adaptive recovery module is constructed at a receiving end as a channel decoder, a diffusion reconstruction network is constructed as a semantic decoder, end-to-end joint training is performed on the point cloud feature extraction network, the channel adaptive enhancement module, the channel adaptive recovery module and the diffusion reconstruction network, the diffusion reconstruction network predicts original point cloud distribution, and the point cloud feature extraction network and the channel adaptive enhancement module are subjected to end-to-end joint training. And a loss function is constructed based on the chamfering distance between the predicted point cloud distribution and the original point cloud. In the communication process, the reconstructed semantic features are input into the diffusion reconstruction network to reconstruct an original point cloud structure. The high-order semantic features of the point cloud can be effectively extracted so as to improve the adaptive capacity of the method to the incomplete point cloud.

The invention discloses a raster scanning correction method and system for an electromagnetic MEMS micromirror, and belongs to the technical field of raster scanning correction, and the method comprises the steps: collecting the real-time three-axis attitude information of an electromagnetic MEMS micromirror product in a three-dimensional space, and the fast and slow axis piezoresistive signals of the micromirror; carrying out PID control by taking the angle difference value as an error signal, outputting a driving voltage of a fast axis and a slow axis of the MEMS micro-mirror after the scanning view field correction, and further mapping to obtain a correction angle of micro-mirror scanning; and taking the correction angle as the input of PID control, and correcting the rotation angle of the micromirror by minimizing the tracking error between the rotation angle of the micromirror and the correction angle. Three-axis attitude information of an electromagnetic MEMS micro-mirror product is collected, and an angle difference value between the three-axis attitude information and standard three-axis attitude information is introduced into PID control to adjust a fast-slow axis driving signal, so that the rotation angle of the micro-mirror is corrected, and correction and compensation of grating distortion in grating scanning are realized. And the accuracy of the reconstruction quality of the three-dimensional point cloud is ensured.

The invention discloses an earth surface exposed geologic body reality sense three-dimensional reconstruction method based on a neural radiation field. The method comprises the steps that an earth surface geologic body image data set is acquired; performing feature matching on the image data set by using a feature matching strategy based on a nearest neighbor algorithm and a LightGlue neural network to generate feature point matching information and a feature description file; sparse three-dimensional point cloud data and camera pose information are obtained based on incremental SfM; and obtaining depth information of an image data set based on sparse three-dimensional point cloud data and camera pose information, and establishing a geologic body realistic three-dimensional reconstruction model by using a neural radiation field optimization method fusing the depth information and Gaussian position coding. According to the method, the feature matching precision and the camera pose calculation accuracy are improved, the texture detail expression ability of a geological structure can be improved, the reconstruction quality of a surface geologic body model is enhanced, and geological information support can be provided for the fields of geological research and engineering application.

The invention relates to a multi-scale agricultural remote sensing image super-resolution reconstruction method based on adaptive attention. The method comprises the following steps: S1, acquiring shallow layer features of a low-resolution image; s2, performing adaptive attention processing on the shallow layer features to obtain adaptive attention multi-scale features; s3, performing aggregation processing on the self-adaptive attention multi-scale features to obtain global aggregation features, accumulating the global aggregation features and the self-adaptive attention multi-scale features, and performing deep feature extraction to obtain current deep features; s4, repeating the steps S2-S3 for the current deep feature until a target number of times, and obtaining a final deep feature; and S5, accumulating the final deep features and shallow features, and performing up-sampling processing to obtain a super-resolution agricultural remote sensing image. According to the method, the quality of the low-resolution agricultural remote sensing image can be improved, and high-resolution image reconstruction is realized, so that the requirements of the fields of agricultural monitoring, precision agricultural management and the like on the high-resolution remote sensing image are met.

The invention discloses a rapid magnetic resonance diffusion kurtosis parameter imaging method based on a model, and relates to the technical field of magnetic resonance imaging. The method comprises the following steps: performing signal acquisition on a measured target; establishing a DKI mathematical model; calculating sensitivity information S of the multi-channel coil; performing initial reconstruction on the down-sampling k space data of each b value and each diffusion coding direction to obtain an initial complex image; performing DKI tensor estimation according to the amplitude information of the initial complex image to obtain an initial DKI tensor; extracting phase information P of each b value and each diffusion coding direction; constructing a target function with regularization constraint; solving a DKI tensor enabling the objective function to be minimum; calculating a DKI quantization parameter; and calculating a DKI image according to the DKI tensor obtained by optimization solution and the DKI mathematical model. According to the method, the DKI tensor can be directly reconstructed from the downsampling k space data, two steps of image reconstruction and tensor fitting are not needed, error transmission is avoided, and the DKI parameter imaging quality is further improved.

The invention discloses a limited angle projection phase contrast CT image reconstruction method and device based on deep learning. The method comprises the following steps: acquiring limited angle projection data of X-ray phase contrast CT scanning through an equal angle increment strategy; performing flat field correction and dark field correction preprocessing on the limited angle projection data to obtain limited angle projection data before phase recovery; preprocessing the finite angle projection data through a phase recovery strategy to obtain finite angle projection data after phase recovery; cT reconstruction is carried out on the limited angle projection data before and after phase recovery through a filtering back projection strategy, and CT images of limited angle projection before and after phase recovery are obtained; a phase contrast CT intelligent image reconstruction network model is constructed and trained; and performing reconstruction processing on the CT image of the limited angle projection before and after phase recovery through the trained phase contrast CT intelligent image reconstruction network model to obtain a reconstructed phase contrast CT image. According to the method, phase contrast CT image reconstruction can be rapidly and efficiently carried out, finite angle reconstruction artifacts can be effectively inhibited, and the image quality is improved.

The invention discloses a hyperspectral image reconstruction method and system, and relates to the technical field of hyperspectral image reconstruction, and the method comprises the steps: building and training a hyperspectral image reconstruction network based on local correlation and global continuity modeling, the network comprises a plurality of spectrum reconstruction units, each spectrum reconstruction unit comprises a plurality of spectrum relation modeling modules, and each spectrum relation modeling module comprises a plurality of spectrum relation modeling modules; each spectral relationship modeling module comprises an inter-spectral relationship modeling module and an intra-spectral spatial modeling module which are respectively used for extracting inter-spectral relationship characteristics and intra-spectral spatial characteristics of the spectral characteristics, and the inter-spectral relationship characteristics are fusion characteristics fusing inter-spectral local correlation characteristics and inter-spectral global continuity characteristics; and inputting the RGB image into the trained hyperspectral image reconstruction network to obtain a reconstructed hyperspectral image. According to the method, the inter-spectrum local correlation features and the inter-spectrum global continuity features are efficiently extracted, and high-quality hyperspectral reconstruction is realized through block-level adaptive feature fusion.

The invention discloses an SAR intelligent refocusing method based on deep learning. The method mainly solves the problems that in traditional SAR image refocusing, phase error modeling is complex, the image resolution is low, and details are lost. According to the implementation scheme, the method comprises the following steps: acquiring a data set of an SAR pure image and a corresponding defocused image, and dividing the data set into a training set, a verification set and a test set; constructing a deep learning network comprising an encoder and a decoder; using the training set to carry out forward and back propagation iteration to train the network, and evaluating and updating model parameters on the verification set to obtain a trained network model; and inputting a defocused image in a test set into the trained network model, extracting image features through an encoder, and reconstructing a high-resolution image through a decoder to obtain a restored image result. According to the method, the deep features of the SAR image can be effectively extracted, gradient disappearance is relieved, features of different levels are effectively fused, the reconstruction quality and resolution of the SAR image are improved, the detail performance of the restored image is enhanced, and the method can be used for radar imaging and evaluation.

The invention discloses an image deblurring method based on multi-scale feature weighting, and the method comprises the steps: collecting fuzzy ship sign picture data of a real scene to obtain a preliminary data set, and estimating a basic fuzzy kernel and a mixing coefficient from the preliminary data set through employing a fuzzy estimation network; obtaining a group of clear ship license pictures, converting the clear ship license pictures into fuzzy ship license pictures by using a basic fuzzy kernel and a mixing coefficient, and gathering each clear-fuzzy ship license image pair to obtain a clear fuzzy data set; constructing a deblurring model which is based on the multi-scale feature extraction network and is fused with a feature weighted fusion module; training a deblurring model by using the clear fuzzy data set, and optimizing the deblurring model through a loss function based on a frequency domain; and inputting a blurred image to be processed into the trained deblurring model to obtain a clear image. According to the method, detail features can be better restored, and the deblurring effect and the overall definition and accuracy of the generated image are improved.

The invention belongs to the technical field of medical imaging, and particularly relates to an MRI (Magnetic Resonance Imaging) condition reconstruction method based on coil calibration image control, which comprises the following steps of: acquiring a coil calibration image and undersampled MRI image data; inputting the coil calibration image into a condition control module to estimate a coil sensitivity distribution map; taking the coil calibration image as condition input, and reconstructing undersampled MRI image data in combination with a diffusion model; wherein the diffusion model comprises a forward diffusion process and a reverse diffusion process, image reconstruction is realized by gradually adding and removing noise, and detail reduction and definition of a reconstructed image are remarkably improved by combining the forward and reverse noise removal processes of the diffusion model with a condition control mechanism of a coil calibration image; the condition control module dynamically adjusts the influence of the coil calibration image on the reconstructed image, thereby effectively reducing the noise and artifacts in the image, and enabling the reconstructed image to be clearer and more accurate.

The application provides a generative image compression method based on vector quantization, and belongs to the technical field of image and video compression. The method comprises the following steps: obtaining a continuous latent representation of an input image through an analysis transformation module; then performing vector quantization processing to obtain discrete indexes of the continuous latent representation and corresponding quantized features; constructing a continuous index probability distribution; predicting a conditional probability distribution of the discrete indexes through a conditional autoregressive entropy model; calculating a coding rate based on the continuous index probability distribution and the conditional probability distribution; reconstructing an image based on the quantized features and constructing a distortion loss; constructing a rate-distortion loss function and jointly training an image compression model comprising the analysis transformation module, the vector quantization module, the conditional autoregressive entropy model and a synthesis transformation module to obtain a trained image compression model; and compressing the input image by using the trained image compression model. The application can realize rate-distortion joint optimization and collaborative improvement of compression efficiency and reconstruction quality.

The invention provides a three-dimensional image reconstruction method and device, and belongs to the technical field of image processing, and the method comprises the steps: obtaining first point cloud data and second point cloud data; wherein the sources of the first point cloud data and the second point cloud data are different; determining a target registration demand based on the first point cloud data and the second point cloud data; a target registration strategy is determined from a plurality of point cloud registration strategies based on the target registration requirement, and the point cloud registration precisions of the plurality of point cloud registration strategies are different; registering the first point cloud data and the second point cloud data based on a target registration strategy to obtain point cloud registration data; and performing rendering processing on the point cloud registration data to obtain a three-dimensional image model. According to the three-dimensional image reconstruction method and device provided by the invention, the efficiency and accuracy of three-dimensional image reconstruction can be improved.

The present application relates to the technical field of computer graphics, computer vision and three-dimensional reconstruction, and particularly relates to an immersive scene reconstruction rendering method and system based on adaptive 3D Gaussian sputtering, which acquires scene initial point cloud data and camera parameters, constructs a 3D Gaussian point cloud model containing multiple attributes and generates sky point cloud; based on the mechanism of pulse neurons, the opacity of Gaussian points is adaptively controlled, and according to the gradient information and geometric attributes of Gaussian points, splitting, copying or pruning operations are performed to optimize the distribution of point cloud; through a deep learning network, the camera pose is corrected, large-scale scenes are divided into blocks for parallel training, and a bilateral grid post-processing is combined with multiple loss functions to optimize rendering, and the sky point cloud is subjected to exclusive constraint, so that the technical defects of the prior art are solved, and the immersive scene application requirements in the fields of VR / AR, digital twin and the like are met.

The invention discloses a face super-resolution reconstruction detection method, device and equipment, and relates to the field of computer vision, and the key points of the technical scheme are as follows: detecting whether a low-resolution image has a to-be-confirmed face region or not; when it is detected that the low-resolution image has a to-be-confirmed face region, inputting the image of the to-be-confirmed face region into a face super-resolution reconstruction network for super-resolution reconstruction so as to confirm the face region in the image; wherein an enhanced generative adversarial network constructed by a generator and a discriminator serves as the face super-division reconstruction network, a DB residual dense block of the generator is replaced by a plurality of stacked Swin Transform block structures, and a reconstruction loss function of the discriminator is introduced to be used for judging whether the reconstruction loss function is a cross entropy loss function of a face region or not. The problem that the quality of the generated image is affected due to the fact that certain noise and artifacts still exist in the process of reconstructing the image resolution by an ESRGAN provided in the prior art is solved.

The invention discloses a space debris detection method applying a convolutional neural network, and relates to the field of image processing, and the method comprises the steps: obtaining image data of a to-be-detected space; a convolutional neural network is used as a framework, and a lightweight cross-scale feature fusion module and a content awareness feature recombination module are fused to construct a fragment detection model; and inputting the image data of the to-be-detected space into the debris detection model to obtain a space debris detection result. According to the invention, the detection problem in a complex observation environment can be solved, and the space debris detection accuracy is improved.

The invention discloses a construction method of a video reconstruction system of a joint information source channel, the video reconstruction system, equipment and a medium. At a transmitting end, the method comprises the following steps: performing multi-frame joint semantic coding on a video sequence, extracting a potential representation simultaneously containing spatial information, time information and high-level semantic information, and directly mapping the potential representation to a wireless channel for transmission; and at a receiving end, a diffusion generation model is constructed based on the denoising network and the diffusion model, and semantic features are introduced to carry out condition guidance on the diffusion denoising process, so that generative reconstruction of the potential representation damaged by noise is realized. According to the method, the diffusion generation model is integrated into a deep joint source channel coding framework, so that the video reconstruction quality and semantic consistency are remarkably improved in a low signal-to-noise ratio and complex channel environment, the cliff effect is effectively weakened, and higher robustness and adaptive ability are achieved.

The invention discloses a terahertz laminated imaging system based on a quantum cascade laser and a physical driving neural network. The terahertz laminated imaging system comprises the quantum cascade laser, a TPX lens, a two-dimensional translation stage, a terahertz array detector and a physical driving neural network structure, a sample is fixed on the two-dimensional translation table, and the two-dimensional translation table can move on an XY plane; the quantum cascade laser emits terahertz waves, the terahertz waves penetrate through the TPX lens to form an illumination probe, the illumination probe irradiates a sample, areas, irradiated by the probe, of the sample at adjacent translation positions are overlapped, and the terahertz array detector collects light intensity information of the sample at each translation position; and the physical driving neural network structure reconstructs amplitude and phase information of the sample by using the light intensity information. The terahertz laminated imaging system provided by the invention is portable, and meanwhile, a physical driving neural network structure without training is adopted, so that the reconstruction quality can be improved, and the terahertz laminated imaging system can be conveniently migrated to a real space domain laminated imaging technology of other wavebands.

The application discloses an audio coding model construction method, device and equipment. The method includes the following steps: constructing a network structure of an audio coding model adopting a dynamic frame frequency mechanism; learning network parameters of the model from audio training data; dividing a multi-level residual vector quantizer related to acoustic characteristics in the model into multiple quantizer groups; obtaining audio training data of different frequency bands corresponding to different quantizer groups; and adjusting network parameters of the quantizer groups according to the audio training data of the frequency bands corresponding to the quantizer groups. By using this processing method, different frequency band-specific quantization optimization strategies are adopted for acoustic characteristics of different frequency bands in the case of dynamic frame rate, dynamic frame rate and frequency band reconstruction are coordinated, and low-frequency energy of an audio signal is prevented from covering high-frequency details when the dynamic frame rate is low; therefore, the full-band reconstruction quality is effectively ensured to be uniform, and the audio reconstruction quality is improved.

The present invention discloses an image super-resolution method based on auxiliary edges. The method includes an edge reconstruction subnetwork, an image reconstruction subnetwork, and an edge fusion module based on a pixel attention mechanism. The main steps are: (1) selecting a public image data set and downsampling to obtain a low-resolution image; (2) using an edge extraction algorithm to calculate the edge corresponding to the high-resolution image; (3) the image reconstruction subnetwork extracts features from the low-resolution image and reconstructs an initial high-resolution image; (4) the edge reconstruction subnetwork uses the low-level features of the image reconstruction subnetwork as input to reconstruct the initial edge image; (5) the edge fusion module uses the initial high-resolution image and the edge image as input, fuses the edge information, optimizes the initial high-resolution image, and obtains the final high-resolution image. The present invention can efficiently utilize edge information, suppress blur at the edge, and achieve high-quality super-resolution reconstruction.

This invention relates to a hyperspectral snapshot compressed imaging reconstruction method with fused conditional diffusion, belonging to the field of deep learning technology. The method includes the following steps: S1: acquiring a two-dimensional compressed image of the target scene; S2: preprocessing the hyperspectral image training set to generate sample data for training; S3: inputting the preprocessed training samples into a hierarchical conditional diffusion image reconstruction network for training; S4: after the network training is completed, inputting test samples into the network to reconstruct the hyperspectral image and obtain the result. This method, while reconstructing the detailed structure of hyperspectral images, possesses stronger denoising capabilities and has significant advantages over other methods in suppressing artifacts.

The invention discloses a super-resolution reconstruction method for enhancing self-attention based on a space channel. The super-resolution reconstruction method is specifically implemented according to the following steps: step 1, constructing a super-resolution reconstruction model for enhancing self-attention based on the space channel; 2, training the model constructed in the step 1; and step 3, testing the model trained in the step 2. According to the method, the ubiquitous problems of huge network model parameter quantity, high calculation complexity, insufficient channel information utilization, non-ideal reconstruction effect and the like in the existing reconstruction method are solved, texture details of the reconstructed image are richer and more vivid, the reconstruction quality is remarkably improved, and the method has wide prospects and important application values in practical application.

The application relates to a method and device for reconstructing an angiography image and an angiography imaging system, wherein the method for reconstructing the angiography image comprises the following steps: acquiring first projection data collected by a target imaging device at a first acquisition angle, and acquiring second projection data collected by an X-ray imaging device at a second acquisition angle; the X-ray imaging device is independent of the target imaging device, and the first acquisition angle is different from the second acquisition angle; reconstructing a four-dimensional angiography image according to the first projection data collected at multiple first acquisition angles and the second projection data collected at multiple second acquisition angles, and obtaining the four-dimensional angiography image. The method can obtain projection data at different angles without changing the collection track of the target imaging device, can reduce the interference of the blood vessel overlapping area, can consider the time resolution, and can improve the reconstruction quality of the 4D DSA reconstruction image.