241results about How to "Promote reconstruction" patented technology

The invention discloses a super-pixel high-frequency enhanced lightweight image super-resolution reconstruction method, device and equipment and a medium. Performing shallow feature extraction based on a convolutional network on an input image, grouping adjacent pixels into superpixels through local clustering, and clustering the superpixels according to similarity to obtain a local area; an intra-pixel attention module and an inter-pixel attention module are introduced, and the two attention mechanisms cooperate with each other in local and global feature extraction. Meanwhile, high-frequency prior information is introduced by using a high-frequency enhancement branch to capture high-frequency details, and the quality of a reconstructed image is improved. According to the method, pixels with similar characteristics are aggregated to form superpixels, and local and global information interaction is realized through an in-pixel attention module and an inter-pixel attention module respectively. Meanwhile, high-frequency prior information is added into the network by using a high-frequency enhancement module, so that the feature extraction capability of the network is effectively improved. A large number of experiments on a standard data set verify that the method has a better reconstruction effect.

The invention discloses a high-precision three-dimensional reconstruction method for a complex scene, and the method comprises the steps: obtaining the position and orientation of a camera from a multi-view image through the pose estimation of the polarization camera, and obtaining a normal vector of a scene surface through an MLP network; according to the method, a scene surface normal vector is used for correcting an ambiguous polarization normal vector calculated by using a polarization image, so that a prior normal vector is obtained, and the prior normal vector is input into a pre-trained improved NeuS network to provide additional constraints, so that the geometric blurring problem of a non-texture region and a mirror reflection region is relieved. And compared with depth priori, the network based on the priori normal vector can generate a smooth geometric shape, and can also avoid the problem of depth scale blur among multiple views, thereby obtaining more accurate three-dimensional information. According to the method, high-precision reconstruction can also be carried out on an object with shielding and an object with a thin structure, and better geometric reconstruction can also be carried out on a scene with sudden depth change.

The invention provides an underwater multi-view three-dimensional reconstruction method. The underwater multi-view three-dimensional reconstruction method mainly comprises the following steps: S1, acquiring a high-quality multi-view underwater image through a physical perception image enhancement technology PUIE; s2, initializing a neural symbol distance function SDF, and implicitly representing the three-dimensional shape of an object by a neural network; s3, generating a two-dimensional foreground mask by using few-sample multi-view target segmentation, and obtaining three-dimensional geometric information in combination with monocular depth estimation and normal vector prediction to form mixed geometric prior; and S4, in combination with an image enhancement result and mixed geometric priori, through a joint loss function, training a neural network optimization SDF, and completing accurate three-dimensional reconstruction. According to the underwater three-dimensional reconstruction method, a more efficient and more accurate underwater three-dimensional reconstruction solution is provided by introducing mixed geometric prior and optimizing the reconstruction process of the neural symbol distance function SDF.

The invention relates to the technical field of remote sensing images, in particular to a geographic information surveying and mapping system based on remote sensing images, which comprises a first remote sensing image acquisition module for acquiring first-level geographic information; the ground feature marking module processes the first-level geographic information and identifies and marks ground feature features; the map drawing module calculates a danger degree index and an importance index and draws a danger distribution map and an importance map; the safe area site selection module selects an optimal safe area and establishes a rescue site; the unmanned aerial vehicle path planning module plans an optimal path according to node positions, node weights and inter-node distances; the second remote sensing image acquisition module acquires and processes a remote sensing image by using the unmanned aerial vehicle to obtain second-level geographic information; the cloud computing module obtains and processes the geographic information to obtain a geographic information surveying and mapping map; and the geographic information display module displays the geographic information surveying and mapping map. According to the invention, accurate geographic information is mapped for rescue and reconstruction of post-disaster mountainous areas through multi-source satellite remote sensing images and unmanned aerial vehicle remote sensing images.

The invention provides a staged release type rigid-flexible composite nerve conduit as well as a preparation method and application thereof. A polymer fiber membrane with a double-layer nanofiber membrane structure is used as a tube wall, multi-section hydrogel comprising short-section hydrogel containing anti-inflammatory substances and long-section hydrogel containing growth factors is used as an inner core, and bioactive substance particles containing the growth factors are deposited on the inner wall of the polymer fiber membrane. The staged release type rigid-flexible composite nerve conduit is obtained. The composite nerve conduit provided by the invention is a rigid-flexible composite nerve conduit capable of controllably delivering anti-inflammatory substances and growth factors in time and space to meet the requirements of each stage of injury, and is combined with multiple induction signals such as a topological structure, chemotactic induction and the like to regulate and control the microenvironment of each stage after spinal cord injury and promote subsequent functional recovery.

The invention discloses an image fusion method based on raindrop layer interference reduction and background detail information enhancement, and relates to the technical field of new-generation information. According to the fusion method disclosed by the invention, the enhanced feature map including the direction weight, the reconstructed infrared image feature map including the direction weight and enhanced thermal radiation information, and the raindrop layer feature map including the direction weight and having rich raindrop edge information and texture details can be obtained; subtracting the enhanced feature map from the raindrop layer feature map to obtain a background detail feature map with reduced raindrop layer interference and enhanced background detail information; and the cross-modal complementary feature fusion of the background detail feature map and the reconstructed infrared image feature map can be realized by using a cross-modal complementary feature fusion network to obtain a Y-channel fusion image. The obtained color fusion image contains richer edge information, background detail information and heat radiation information in more source images, and artifacts in the color fusion image are fewer.

The invention belongs to the field of tendon repair in biomedical materials, and provides a gradient mineralized collagenous fiber membrane and a preparation method thereof. And vertically placing the collagenous fiber membrane at the bottom of the container, and adding mineralizing liquid to prepare the gradient mineralized collagenous fiber membrane. The prepared high-orientation collagenous fiber membrane comprises a gradient mineralization layer and a non-mineralization layer, the gradient mineralization layer comprises an I-type collagen component and a HAp component in spatial gradient distribution, and the non-mineralization layer is composed of the I-type collagen component. The GMA-Col provided by the invention has an aligned layered fibrous structure, shows enhanced mechanical properties in the alignment direction, and is beneficial to spatial continuous change of mechanical rigidity. The differentiation surface morphology, the mineral content, the rigidity and the wettability of the local area can influence the adhesion, proliferation and differentiation of cells to a certain extent, and the mechanical property of the healing injury part and the recovery of the animal movement function are remarkably enhanced by implantation of the bone marrow bone marrow bone marrow bone marrow bone marrow bone marrow bone marrow bone marrow bone marrow.

The invention relates to the field of automatic driving scene reconstruction, discloses an automatic driving boundary-free scene implicit reconstruction method and system considering geometric information enhancement, and adopts an automatic driving geometric perception grid-based neural rendering system. The system reconstructs and renders an accurate driving environment from sparse sensor data using a perspective deformed hash grid and a signed distance function (SDF). The system comprises a Hash pool, 16 levels are included, 219 two-dimensional feature vectors are held in each level, the feature vectors are input into an MLP network through spatial interpolation, scene features and SDF are extracted, finally, high-quality scene reconstruction is achieved through optimization of RGB images, depth and normal, and RGB input and monocular depth and normal observation are used as supervision during joint optimization. According to the system, the scene reconstruction quality is improved, a boundless scene can be effectively processed, the performance is improved in a viewpoint sparse environment, and higher robustness and accuracy are shown when a complex scene in an actual driving environment is processed.

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 belongs to the technical field of deep learning and recommendation, and particularly relates to a recommendation method and system based on variational graph auto-encoder denoising, which are used for improving the performance of a recommendation system. The method comprises the following steps: constructing a user-project interaction graph and a knowledge graph; graph embedding initialization is carried out on project nodes in the knowledge graph, and embedding representation of the project nodes is generated; the method comprises the following steps of: enhancing a project of a knowledge graph by using a variational graph auto-encoder to obtain a mean vector and a variance vector of the project, then generating two potential representations of the project through two times of sampling, and reconstructing through a decoder to obtain two knowledge graphs; performing graph contrast learning in the interactive graph, optimizing representations of the user and the item; updating all to-be-learned parameters in the model according to the optimization target; and according to the final embedded representation of the user and the project obtained through optimization, calculating a predicted interaction probability of the user and the project and generating a recommended project.

The invention discloses a turbine blade surface field rapid prediction method based on a multi-channel self-attention network, and relates to the technical field of data prediction. The technical problems that in the prior art, an existing turbine blade surface field prediction technology is long in consumed time, high in calculation cost and insufficient in precision, and obvious temperature and pressure distribution nonuniformity exists are solved. According to the technical scheme, the turbine blade surface field rapid prediction model building method based on the multi-channel self-attention network comprises the steps that turbine blade surface field data are collected; collecting a preset multi-channel self-attention network architecture; performing pretreatment; performing feature extraction and modeling on the turbine blade surface field according to the processed multi-channel self-attention network architecture; and according to the feature extraction and modeling results, establishing a'mapping relation from boundary conditions to a temperature field and a pressure field on the surface of the turbine blade '. The method is suitable for predicting the temperature field and the pressure field on the surface of the turbine blade.

The invention relates to a structured light illumination confocal super-resolution measurement system and method, and relates to the technical field of optical measurement. The system comprises a digital micromirror device (DMD), a sample platform, an electric displacement platform, a microscopic imaging assembly, a complementary metal oxide semiconductor (CMOS) imaging assembly and computer equipment, and the DMD is used for adjusting the point light source array and the structured light field. According to the DMD-based confocal super-resolution measurement method, a structured light field and a signal-noise separation technology are combined, and high-precision and high-resolution three-dimensional surface reconstruction can be realized. By using the flexibility of the DMD and the advantages of the super-resolution technology, the spatial resolution and speed of confocal imaging are effectively improved, so that the measurement process is more accurate. In addition, low-rank decomposition and self-supervised deep learning methods are fused, the image reconstruction process is optimized, noise interference is suppressed, and the overall performance of the system is further improved.

The invention relates to a time-resolved spectral measurement method, device and equipment based on a multi-event time-to-digital converter, and a medium, and the method comprises the steps: carrying out the histogram statistics of photon signals of different time responses in each column of single-photon avalanche diodes in different laser pulse periods through the multi-event time-to-digital converter, the photon signal distribution of the photon signal of the specific wavelength corresponding to each column of single-photon avalanche diodes in different laser pulse periods is determined; the multi-event time-to-digital converter carries out delay combination on photon signal distribution in different laser pulse periods so as to determine a photon lifetime curve corresponding to the photon signal of the specific wavelength of each column of single-photon avalanche diodes. And integrating the photon lifetime curves corresponding to the photon signals of the specific wavelength of each column of single-photon avalanche diodes so as to construct the time-resolved spectrum of the sample to be detected. According to the invention, counting loss or time error caused by dead time can be reduced.

The invention discloses an intelligent rapid magnetic resonance imaging reconstruction and pathological positioning method. The method is specifically implemented according to the following steps: step 1, constructing a pathological focused nuclear magnetic resonance image reconstruction model; 2, constructing a pathological focused nuclear magnetic resonance image reconstruction network according to the pathological focused nuclear magnetic resonance image reconstruction model constructed in the step 1; step 3, training a pathological focused nuclear magnetic resonance image reconstruction network; and step 4, using the trained pathologically focused magnetic resonance image reconstruction network to carry out magnetic resonance imaging and pathology positioning. According to the method, dependence on an accurate pixel-level label can be reduced, the pathological part of the image can be positioned while the reconstruction effect of the nuclear magnetic resonance image is enhanced, a better reconstruction effect is achieved on the part, and a clinician is effectively assisted in diagnosis.

The invention belongs to the technical field of radar signal processing, and particularly relates to a few-sample through-the-wall radar high-resolution imaging method, which specifically comprises the following steps of: 1, acquiring through-the-wall radar images of different targets at different rotation angles and optical images serving as optical labels, and constructing a source domain data set and a target domain data set; 2, training a source domain high-resolution imaging network by using the source domain data set; 3, the trained source domain high-resolution imaging adversarial network is utilized to initialize a target domain network, the target domain data set is utilized to train the target domain network, a migrated target domain high-resolution imaging network is obtained, and the structure of the target domain high-resolution imaging network is the same as that of the source domain high-resolution imaging network; and 4, inputting the radar image into a generator of the target domain high-resolution imaging network to obtain a high-resolution imaging result containing shape and contour information.

The invention provides a DNA tetrahedron-based compound as well as a preparation method and application thereof, and belongs to the field of medicines. The invention designs a bioactive composite hydrogel scaffold modified by a functionalized DNA tetrahedron compound containing BMP-2, and the functionalized BMP-2 binding peptide is stably combined with a tetrahedron framework structure with a unique three-dimensional space structure and fine programmability in a chemical click mode. And further preparing with methacrylated hyaluronic acid to obtain the composite hydrogel stent. The functional DNA tetrahedron compound can efficiently capture BMP-2, the composite hydrogel scaffold can capture endogenous BMP-2 in a targeting manner in the early stage of bone defect and stimulate stem cell osteogenic differentiation so as to promote in-situ osteogenesis, a series of complications caused by delivery of exogenous bone morphogenetic protein-2 are avoided, the in-vivo stability is poor, and the in-vivo bone morphogenetic protein-2 can be used for preparing the bone morphogenetic engineering scaffold. The method has the advantages of high safety, high cost and the like, and has a good application prospect.

The invention provides a semantic communication method and system for an ultra-high-definition video, and relates to the technical field of semantic communication, and the method comprises the steps: obtaining semantic features based on a to-be-coded video P frame and a channel state through employing a video P frame semantic feature extractor; based on the semantic features, obtaining four-step semantic importance by using a quadtree-based spatial domain entropy model; based on the semantic features and the four-step semantic importance, utilizing a four-way semantic feature codec to obtain the received semantic features; and reconstructing a video P frame by using a semantic information restorer and a de-noising enhancement module based on the received semantic features, the received channel state and the multi-scale reference context features. According to the method, the semantic features of the video frames are extracted and recovered by using the module constructed by the CNN, and the semantic features of the video frames are processed by using the coding and decoding module constructed by the Swinin-Transform, so that the coding and decoding efficiency is improved, the understanding ability of the model to the video content is enhanced, the high-quality video content can be better reconstructed, and the size of the model parameter is controlled.

The invention discloses an implicit image degradation estimation method based on decoupling contrast learning, and belongs to the technical field of image blind super-resolution, and the method comprises the steps: sampling image blocks in different LR images with the same degradation information to construct a positive sample set; on the data level, it is guaranteed that task irrelevant information such as content and texture does not affect degradation feature learning, and meanwhile data amplification is achieved by continuously and circularly pairing sampling image blocks; in the training process, a feature enhancement strategy is introduced to increase the number of positive samples of the same degradation type, so that the sample diversity of each degradation type is further expanded in the training process to ensure that the network more fully learns degradation information. The implicit image degradation estimator trained through the method is applied to a blind image super-resolution network based on implicit degradation feature guidance, the degradation information modeling quality of an LR image can be effectively improved, and then the reconstruction effect from the LR image to an HR image is improved.

The invention discloses a single-view three-dimensional reconstruction method based on structural features and implicit features. The method specifically comprises the following steps: inputting an image to a hierarchical structure reconstruction network to obtain structural features; a sampling space point is preset, global features of an input image are extracted through a convolutional neural network, a feature map is saved, and rough features are obtained after the global features are processed through a tree structure graph convolutional network, 3D convolution and interpolation operation; local features are obtained through up-sampling feature maps, standard viewpoint conversion and local feature splicing and dimension reduction operation of different channels; processing the coordinates of the preset spatial points to obtain point features, splicing the point features with the structural features, the rough features and the local features, placing the spliced features into a three-branch decoder for decoding, and adding the decoded features to obtain an SDF value of a reconstruction target; and extracting a zero value surface by using a Marking Cube algorithm to obtain a reconstruction result. The method does not need to know camera parameters in advance. Compared with the existing method, the method provided by the invention can better reconstruct an object with a complex shape.

The invention relates to an image reconstruction model construction method and device, an image generation method and device and computer equipment. The method comprises the following steps: acquiring two training X-ray images, a noise-added CT image and a to-be-trained image reconstruction model; taking the current noise adding step identifier as guide data, taking the two training X-ray images and the noise adding CT image as condition data, and inputting the condition data into the to-be-trained image reconstruction model to obtain model prediction noise; calculating a model noise loss value corresponding to the difference between the representation model prediction noise and the image real noise of the noise-added CT image; the model noise loss value comprises skeleton label information for the skeleton region; and training the to-be-trained image reconstruction model according to the model noise loss value to obtain a trained image reconstruction model. By adopting the method, the image of the target object can be more effectively reconstructed, so that the three-dimensional skeleton space information of the target object can be obtained on the basis of low-radiation damage, and the definition and medical diagnosis value of the reconstructed image are further remarkably improved.

The invention discloses a non-vision-field imaging system and reconstruction method based on a TOF sensor and a laser, and the method comprises the steps: building a non-vision-field imaging scene which is composed of the TOF sensor, the laser, an intermediate surface, and an imaging target; collecting a non-vision field data set, wherein the non-vision field data set is composed of an unprocessed original depth degraded image and a corresponding depth clear image; according to the non-vision field data set, a three-dimensional reconstruction model of a variable-scale coding-decoding architecture based on a deep neural network is built for reconstructing a non-vision field target, and the preprocessed non-vision field data set is adopted to complete training of the three-dimensional reconstruction model; and inputting a to-be-reconstructed depth degradation image into the three-dimensional reconstruction model for three-dimensional reconstruction, and outputting a depth reconstruction image of the non-vision field scene. The method is good in reconstruction effect, high in applicability and low in cost, can reconstruct the contour of the imaging target under the condition that the depth degradation image is completely fuzzy, is less in shadow, and can be widely applied to the technical field of computers.

The invention discloses a single-view point cloud reconstruction method for tree branches in a real scene. The method comprises the following steps: acquiring a single RGB image of the tree branches in the real scene; performing single-view depth estimation based on loop prediction on the single RGB image of the tree branch to obtain a plurality of depth images; performing depth image-to-point cloud conversion on the plurality of depth images to obtain sparse point clouds; carrying out sparse point cloud densification on the sparse point cloud to obtain a dense point cloud; performing point cloud coordinate fine correction on the dense point cloud to obtain a reconstruction result; and reconstructing the tree branch point cloud in the real scene based on the reconstruction result. The invention further discloses a corresponding system, electronic equipment and a computer readable storage medium. The problem that the depth information of the shielded branches cannot be effectively estimated is solved through single-view depth estimation based on loop prediction; the tree morphological feature analysis-based point cloud denseness algorithm and the point cloud correction method based on point-by-point correction solve the problem that the preliminarily obtained tree branch point cloud is sparse and inaccurate.

The invention relates to a wide-spectrum image color reconstruction method. The method comprises the following specific steps: constructing a wide-spectrum image color reconstruction network which comprises a backbone network and a semantic sub-network; obtaining a wide-spectrum semantic segmentation data set, and pre-training a semantic sub-network; the wide-spectrum image is divided into two paths to be input into the backbone network and the semantic sub-network respectively, the middle output of the semantic sub-network is connected to the backbone network through the semantic guide cross attention module, and cross attention operation is carried out on the middle output and the middle features of the backbone network; fixing the weight of the semantic sub-network, only updating the parameters of the backbone network, and training the backbone network on the wide-spectrum image reconstruction data set until the network converges; the weight of the semantic sub-network is released, so that the parameters of the semantic sub-network and the parameters of the backbone network are updated together in training, and the semantic sub-network and the backbone network are jointly trained until convergence; and reconstructing and recovering the wide-spectrum image by using the trained semantic sub-network and the backbone network. According to the invention, the problem of color shift in wide-spectrum imaging can be effectively solved.

The invention discloses a capsule endoscope image high-definition display method. The method comprises the following steps: S1, performing JPEG compression on an original image P1 shot by a capsule endoscope to obtain an image P2; s2, the image P2 is transmitted to an external receiver through a wireless transmission module in the capsule endoscope to obtain an image P3, and no image processing operation is carried out from the image P2 to the image P3; and S3, in an external receiver, performing denoising, white balance, contrast improvement and color correction operation on the image P3 by using an OpenCV tool to obtain an image P4. And S4, using a local implicit image function super-division model based on deep learning to perform super-division of any magnification on the image P4 to obtain an image P5 finally displayed in the upper computer. According to the invention, image processing of the capsule endoscope is transferred to the external receiving device, so that a series of defects caused by image processing in the capsule endoscope are effectively avoided; moreover, an image with any resolution can be generated, image details can be reconstructed more finely, and the image quality is improved.

The embodiment of the present invention provides a model generation, 3D hairstyle generation method, device, electronic device and storage medium. The model generation method includes: obtaining a 2D sample head image and a 3D hairstyle image corresponding to the 2D sample head image; inputting the 2D sample head image as an input image into a backbone model to obtain a first image; inputting the first image into a generator of a generative adversarial network to obtain a second image generated by the generator, comparing the second image with the input image, training the backbone model and the generative adversarial network until the second image generated by the generator meets a preset condition, and then terminating the training; determining the backbone model at the end of the training as a 3D hair reconstruction model. Through this method, the output result of the backbone model is "refined" through the adversarial generative network during the model training process, so that the trained 3D hair reconstruction model can output a 3D hairstyle with good effect, closer to the input two-dimensional plane image, and more suitable.

The application provides a three-dimensional reconstruction method based on multi-dimensional information fusion and a convolutional neural network, and comprises the following steps: acquiring polarized sub-images of a measured object under four transmission polarization directions and including multiple detection spectral bands through a multispectral polarization camera; performing image registration and joint denoising on all images; calculating polarization parameters of the measured object, combining spectral information and polarization information to calculate an azimuth angle and a zenith angle of the measured object; and acquiring surface normal vector component three-dimensional data of the measured object through a surface normal reconstruction model constructed based on a convolutional neural network and with the aid of physical prior knowledge. The method provided by the application can effectively restore the texture details of the surface of the measured object, can realize fast three-dimensional information acquisition of a measured object with a surface simultaneously having a diffuse reflection and a specular reflection characteristic in a complex indoor or outdoor scene under illumination conditions, and is helpful to expand the application range of the polarization three-dimensional reconstruction technology.

The present invention discloses an aberration compensation method and device, the method comprising: step S1, compensating and superimposing a conjugate wavefront aberration phase on an initial input phase to obtain a pre-compensated phase; step S2, superimposing the pre-compensated phase through the wavefront aberration phase distortion of an optical element itself to obtain a compensated phase, and subjecting the compensated phase to Fourier transformation to obtain aberration-compensated Fourier surface intensity; the aberration-compensated Fourier surface intensity information has a low bit error rate and a good reconstruction effect; it is a numerical compensation method without the need to add additional optical components; thereby reducing the number of optical elements used in an optical system and reducing the complexity of the optical system, providing good technical support for aberration compensation in, for example, the field of holographic storage, and having good application prospects in, for example, the field of holographic data storage research; it can be applied to the fields of holographic storage, biomedical image processing, digital holographic microscopy, etc.

The local super-resolution reconstruction algorithm based on the transmission line insulator infrared image includes the following steps: Step 1, image recognition; Step 2, image segmentation; Step 3, super-resolution reconstruction; By using the improved adversarial network structure, unsupervised reconstruction is adopted, the RRDB network structure is adopted, when the network structure is deepened, the stability of training can be maintained, the calculation amount and the use of memory are reduced, and the reconstruction effect is improved, the SeNET attention mechanism is added, from the feature channel level, according to the importance, the useful features such as the outline and the details of the insulator are improved, and the features which are not useful for the program are suppressed. The resolution of the current transmission line insulator infrared image is low, and when the insulator detection is carried out, the workload is large and the problems such as false detection and missed detection occur.

The invention discloses a hyperspectral image super-resolution reconstruction method based on differential features and cross-domain features, and mainly solves the problems that complex features in a spectral domain and a spatial domain cannot be fully extracted and the noise suppression effect is poor in the prior art. The scheme comprises the following steps: 1) sampling and normalizing an original image to generate a training data set; 2) respectively constructing a cross-domain feature extraction layer, a spectral differential feature extraction layer and a spatial differential feature extraction layer, and constructing a feature extraction sub-network based on differential features and cross-domain features by using the three feature extraction layers; 3) constructing a reconstruction network by using the shallow feature extraction layer and the plurality of feature extraction sub-networks, and training the reconstruction network by using the training data set; and 4) using the trained network to realize super-resolution reconstruction of a real hyperspectral image. According to the method, the cross-domain features of the spectral domain and the spatial domain of the hyperspectral image can be fully extracted and utilized, and the problem of detail distortion of the reconstructed image is effectively improved.

The present application belongs to the technical field of traditional Chinese medicine composition, and relates to a traditional Chinese medicine transdermal external preparation for treating vulvar hypopigmentation disease. The preparation is a paste prepared by mixing traditional Chinese medicine powder and horse oil. The traditional Chinese medicine powder is composed of the following raw materials by weight: Xuchangqing 10-12, Pugongying 10-15, Difusiduo 12-15, Tuzifuding 12-15, Baiji 10-15, Huzhang 9-12, Quansie 6-10, Diyu 10-12, Xuezhi 10-12, Danggui 6-10, Rushang 6-10, Miedu 6-10, Bingpian 1-3. The traditional Chinese medicine transdermal external preparation has definite and significant effects on vulvar hypopigmentation disease, especially in relieving intractable pruritus, promoting pigment recovery and improving skin sclerosis / atrophy. The traditional Chinese medicine transdermal external preparation has high safety, few adverse reactions and low recurrence rate.