43results 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 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 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 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 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 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 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 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 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 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.

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 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.

The invention discloses a spatial-temporal feature enhancement-based dynamic point cloud geometric compression method and related equipment, and the method comprises the steps: carrying out the multi-layer down-sampling of a reference point cloud and an original point cloud, and obtaining L layers of reference compression point clouds and original compression point clouds; entropy coding and decoding are carried out on the L layers of original compressed point clouds to obtain L layers of reconstructed point clouds; l = L; performing up-sampling on the lth-layer reconstruction point cloud to obtain (l-1)-layer extension point cloud, performing geometric coding and decoding on the (l-1)-layer extension point cloud and the (l-1)-layer original compression point cloud to obtain (l-1)-layer coarse reconstruction point cloud; performing time domain modeling based on the (l-1)-layer coarse reconstruction point cloud and the reference compression point cloud to obtain (l-1)-layer fusion alignment features; obtaining (l-1) layers of reconstruction point clouds based on the (l-1) layers of fusion alignment features and the original compression point clouds; performing progressive decrease operation on l, and performing cyclic reconstruction until l is equal to 1; and performing up-sampling according to the first-layer reconstructed point cloud to obtain a final reconstructed point cloud. The method can adapt to complex point cloud compression requirements and can be widely applied to the technical field of point cloud data processing.

The invention relates to a three-dimensional reconstruction method and system for a scene in a three-dimensional Gaussian splash chamber based on plane prior guidance, and belongs to the technical field of three-dimensional reconstruction and computer graphics. The method aims at solving the problems that in indoor scene reconstruction, especially in a weak texture area, geometric reconstruction of an existing method is not accurate, and artifacts are likely to be generated. The method comprises the following steps: firstly, acquiring a multi-view image and camera parameters thereof, predicting depth and normal priori, and extracting a plane region through multi-granularity segmentation and geometric consistency check; a dense point cloud is generated by using depth prior back projection, and a three-dimensional Gaussian splash model is initialized based on a down-sampling point cloud. And finally, under a micro-renderable framework, optimizing the model by combining the planar region geometric constraint, the non-planar region geometric constraint and the global geometric constraint, and completing reconstruction. According to the method, the reconstruction precision of the weak texture plane region is effectively improved, the detail recovery capability of the complex structure region is enhanced, and a reconstruction result with visual high fidelity and geometric accuracy is realized.

This application provides a method, system, electronic device, and medium for high dynamic range (HMR) reconstruction based on a single image. The method includes: processing a high RMR image used for training into a low RMR image to be processed and an optimized low RMR image; using the low RMR image to be processed as input to a partially convolutional U-shaped network in a two-stage deep learning model to train the partially convolutional U-shaped network, thereby obtaining an intermediate low RMR image; using the intermediate low RMR image to train a classical convolutional U-shaped network in the two-stage deep learning model, thereby obtaining a reconstructed HMR image; and inputting a single low RMR image into the trained two-stage deep learning model to obtain the reconstructed HMR image. This application enables HMR reconstruction based on a single image through a two-stage deep learning model, improving the reconstruction quality of HMR images based on a single image.

The invention discloses a frequency domain enhanced single-pixel imaging method and a frequency domain enhanced single-pixel imaging system, which are used for acquiring a single-pixel measurement value corresponding to a target object and performing image reconstruction based on the single-pixel measurement value to obtain a target imaging result. The method comprises the following steps that a single-pixel imaging network is constructed, the single-pixel imaging network comprises an input layer, a plurality of coding layers, a plurality of decoding layers and an output layer which are connected in sequence, the number of the coding layers is the same as that of the decoding layers, and each coding layer comprises a basic convolution layer, a Swindow-Transform module and a frequency domain MLP module which are connected in sequence; based on the single-pixel measurement value, performing iterative training on the single-pixel imaging network according to a double-domain self-supervision method, and obtaining a first reconstructed image and a second reconstructed image in each iteration; and taking the optimal first reconstructed image as a target imaging result. The method can give consideration to both imaging quality and reconstruction speed.

The application provides a 3D Gaussian sputtering method based on deep feature fusion, comprising: extracting multi-view features and monocular depth features through a multi-view Transformer network and a pre-trained monocular depth estimation model respectively; dynamically fusing the two types of features through a content-guided attention module; using a 2D U-Net network to perform depth regression on the fused features to obtain a robust depth distribution; projecting the depth distribution back to a 3D space to obtain Gaussian centers, and then using a 2D U-Net network to predict other Gaussian parameters; and rendering a high-quality three-dimensional model according to all Gaussian parameters. The 3D Gaussian sputtering method combines the complementary advantages of multi-view feature matching and monocular depth prior, significantly improves the reconstruction quality of complex scenes, and solves the problem of low scene reconstruction quality caused by inaccurate matching or missing matching information of traditional feature matching methods in complex scenes.

The application discloses a remote sensing image blind super-resolution method and system based on unsupervised learning and belongs to the technical field of remote sensing image reconstruction. The application is applied to a remote sensing image super-resolution scene in which a degradation model is unknown. First, spatial reconstruction information is transferred from a natural image field to a remote sensing image field by using transfer learning. Then, the spatial-spectral reconstruction information is optimized based on unsupervised learning. By using the transfer learning, the application does not need to be trained on remote sensing images with limited training data, and the advantages of data-driven are explicitly used. By using the unsupervised learning, the application can be generalized to remote sensing image super-resolution reconstruction tasks in various degradation scenes, the spectral reconstruction information is fully learned, and the blind super-resolution precision of the remote sensing image is improved.

The invention discloses a double-working-distance view field lens-free coding single exposure imaging device. The device comprises a coding plate, a beam splitter prism, a first image sensor, a second image sensor, a control system and a computer, the control system is respectively connected with the first image sensor, the second image sensor and the computer; the coding plate is placed in front of the beam splitter prism and is used for modulating optical information of a target sample; the beam splitter prism is used for dividing the modulated light of the target sample into two parts, one part is received by the first image sensor after being reflected, and the other part is received by the second image sensor after being transmitted. According to the method, information of a target sample can be reconstructed through an iterative algorithm on the basis that two coded images which aim at the same coding plate but have different working distance point spread functions are acquired through single exposure. The method is of great significance to high-quality lens-free coding dynamic imaging.

The application provides a self-supervised speech denoising method and device, and relates to the field of acoustic signal processing, and comprises the following steps: S1, obtaining a DCUnet network, constructing a speech denoising model by adding a TCM module in the DCUnet network; S2, obtaining a noise speech set, training the speech denoising model through the noise speech set, and obtaining a trained speech denoising model; S3, denoising speech through the trained speech denoising model. The application constructs a speech denoising model based on a deep complex domain DCUnet network, combines a complex domain overall processing strategy, improves the reconstruction quality and denoising performance of the speech signal, dynamically captures the change characteristics of the noise scene through the TCM module in the speech denoising model, enhances the adaptability of the model to dynamic signals in a complex environment, trains the speech denoising model by using an ONT strategy, uses noise speech as training data, does not need clear target speech data, reduces the training data collection cost, and improves the generalization ability of the model.

The present application relates to a genetic algorithm-based optical imaging system sensor optimization configuration method.The present application includes taking the uniformity coefficient of the sensor sensitivity distribution as the optimization target, taking the angle position of the sensor structure light-emitting and light-sensing elements as the optimization object, and obtaining the optimal structure of the optical tomography sensor applicable to the fan-shaped laser through the genetic algorithm.Then, the angles of the light-emitting elements and the light-sensing elements are set according to the optimal sensor structure parameters, so that the optimized sensor is obtained.Finally, the imaging optimization is realized through the imaging system.The present application is not dependent on any specific actual distribution, is applicable to the case without prior information, and has stronger universality.The genetic algorithm is used to realize the optimization of the sensor structure, and the calculation amount of designing an optimal configuration structure with the minimum uniformity coefficient is reduced.

The application relates to a real-world video super-resolution method, system, device and medium, which comprises the following steps: inputting an original video sequence; video embedding: extracting the spatial features of each frame from the original video sequence to obtain first features, and inputting the first features into a double-axis space-time attention mechanism module to obtain second features; wherein the double-axis space-time attention mechanism module comprises a vertical-time attention block and a horizontal-time attention block, the feature blocks generated after the first features are processed through rotation position coding are converted into token sequences, and the token sequences are rearranged and then respectively sent into the vertical-time attention block and the horizontal-time attention block to simulate spatial texture and motion characteristics; space-time reconstruction: time attention is adopted to integrate time information, and a video output with higher space-time quality is generated. Compared with the prior art, the application has the advantages of good video reconstruction quality, high robustness and low cost.

The present application belongs to the field of hyperspectral reconstruction in coded aperture snapshot spectral imaging system, and particularly relates to a hyperspectral image reconstruction method based on spatial-spectral dual-prior frequency domain enhancement, comprising: obtaining a hyperspectral image, a system measurement value of the hyperspectral image and an imaging mask according to a coded aperture snapshot spectral imaging system, inputting the system measurement value and the imaging mask into a trained hyperspectral image reconstruction model to obtain a reconstructed hyperspectral image; the hyperspectral image reconstruction model comprises an imaging mask preprocessing module, a spatial prior denoising reconstruction module and a convolution layer; the spatial prior denoising reconstruction module is adopted to recover spatial information from different angles by using spatial learning and frequency domain learning two branches, the frequency domain learning branch uses the imaging modulation mask as spatial prior to assist in recovering spatial detail features, and the hyperspectral reconstruction quality of the model is improved.

The application discloses a kind of object three-dimensional reconstruction method and device based on deep learning, it is related to three-dimensional reconstruction technical field, wherein, the reconstruction method includes: using camera imaging equipment to collect target object video of target object, and based on target object video, determine target image set, based on target image set, determine target camera parameter and initial camera pose, and correct initial camera pose, obtain target camera pose, input target image set, target camera parameter and target camera pose to preset neural radiance field model, output target radiance field information, based on target radiance field information, construct the object three-dimensional model of target object.The application solves the technical problem that the accuracy of three-dimensional reconstruction of object in related art is low.