17results about How to "Improve reconstruction effect" patented technology

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 blueberry tree branch segmentation method and system, and the method comprises the steps: carrying out the multi-scale feature extraction of a to-be-segmented blueberry tree image, and obtaining the feature representation containing low-layer details, middle-layer semantics and high-layer context; information complementation and semantic alignment of deep and shallow layer features are realized through a bidirectional cross-level information interaction module, and fusion features are optimized by using a space and channel attention mechanism to obtain enhanced features; and performing up-sampling and dynamic reconstruction on the enhanced features by adopting a frequency domain dynamic convolution module, refining branch edges and texture details, and outputting a high-resolution pixel-level segmentation result. The method effectively solves the problems of fuzzy boundary, fracture adhesion and background interference in the segmentation of the slender branches and trunks of the blueberries, and has good precision and generalization ability.

The method belongs to the technical field of panoramic monitoring of ultra-high voltage converter station, and aims to solve the problem that panoramic monitoring image data is directly uploaded to the cloud, occupying a large amount of cloud resources. By adopting multi-scale convolution blocks in the deep multi-scale residual network model to construct low-order and high-order features of images of various scales, the incomplete phenomenon of image detail extraction is avoided, and the residual learning mechanism is adopted to retain low-order rough features, thereby improving the reconstruction ability of the image. Topology optimization constructs the topology structure of the heterogeneous network, and the framework combining deep reinforcement learning and Monte Carlo tree search is used to construct the network according to the pre-defined topology rules. The search result of the Monte Carlo tree strengthens the learning of the deep convolutional neural network, so as to obtain more accurate prediction in the next iteration. After the data is processed in the edge side, it is transmitted to the cloud storage, saving the cloud storage space and transmission bandwidth.

This invention relates to a super-resolution reconstruction method for UAV images of kiwifruit sunburn. Compared with existing technologies, it solves the problems of poor model generalization due to the lack of accurate modeling of the actual physical degradation of UAVs, and the difficulty of feature extraction networks in simultaneously considering global semantics and local high-frequency lesion details, resulting in blurred reconstruction. The invention includes the following steps: acquisition and preprocessing of UAV images of kiwifruit sunburn; generation of multi-scale paired datasets; construction of a super-resolution reconstruction model for kiwifruit sunburn images; training of the super-resolution reconstruction model for kiwifruit sunburn images; acquisition and preprocessing of UAV images; and super-resolution reconstruction of UAV images. This invention significantly eliminates boundary artifacts in image processing by constructing a multi-scale attention and spatial-frequency fusion mechanism, accurately restoring the high-frequency edge texture details of small lesions at different scales, and reducing data acquisition time.

This invention discloses a method for reconstructing the surface environment of exoplanets based on a neural radiation field. The method includes: acquiring images of the exoplanet's surface within a visible area using a navigation camera on an exoplanet rover; calculating the camera's intrinsic and extrinsic parameters during imaging based on the common viewpoint matching relationship of the images; constructing a neural radiation field model using the images and the camera's intrinsic and extrinsic parameters as input to reconstruct the exoplanet's surface environment structure; and using the intrinsic and extrinsic parameters corresponding to the new perspective image to be synthesized as input to the neural radiation field model to synthesize a new perspective image or virtual reality image of the exoplanet's surface environment. This method overcomes the disadvantages of exoplanet surface images, such as single and repetitive texture features, sparse and dispersed viewpoints, and low overlap areas, achieving a reconstructed exoplanet surface environment with excellent visual effects and correct geometric structure.

A method and device for training a behavior recognition model, an electronic device, and a storage medium are disclosed, relating to the technical field of model training, and including: obtaining a sample image of a driver, and determining a training data set according to the sample image; inputting the training data set into the behavior recognition model for recognition, obtaining a sample recognition result, and determining a recognition confidence according to the sample recognition result and an actual behavior result; if the recognition confidence is less than or equal to a confidence threshold, determining a target loss function value according to the sample recognition result and the actual behavior result, updating the behavior recognition model based on the target loss function value, and training the updated behavior recognition model. The present application can adaptively generate a reorganization kernel according to semantic content, thereby realizing more fine and semantically consistent feature reconstruction, and further ensuring the training accuracy of the behavior recognition model, and further improving the behavior recognition accuracy of the in-vehicle personnel by the trained behavior recognition model.

The application discloses an ECG signal acquisition method and device based on an rPPG signal, equipment and a medium, relates to the technical field of computer software, and comprises the following steps: obtaining a PPG signal based on an rPPG signal, and generating rPPG conditional coding through a PPG encoder. The low-frequency, high-frequency and spatial domain ECG tokens of the mask state are used as the current mask token, the rPPG conditional coding is used as the constraint, and the mask reconstruction is completed through a bidirectional Transformer. The mask is gradually removed according to the confidence, and the reconstruction is circularly performed until all the masks are removed to obtain target tokens. The low-frequency and high-frequency feature tokens in the target tokens are subjected to corresponding decoding and inverse short-time Fourier transform to generate a frequency domain ECG waveform; and the spatial domain feature tokens are subjected to an ECG decoder to obtain a spatial domain ECG waveform. The target ECG signal is obtained by fusing the two waveforms, and a high-precision and high-fidelity ECG signal can be obtained based on the rPPG signal.

The invention relates to the technical field of medical image processing, in particular to a CT image reconstruction method based on a progressive texture perception diffusion model, and the method comprises the steps: inputting a to-be-reconstructed low-dose CT image or a sparse view angle image and projection image data into a trained progressive texture perception diffusion model, and outputting a reconstructed CT image; the progressive texture perception diffusion model carries out preliminary reconstruction on a to-be-reconstructed low-dose CT image or a sparse view angle image to obtain a low-frequency structure of the image; based on the low-frequency structure, high-frequency details are generated through conditional Schrodinger bridge diffusion network iteration, multi-scale high-frequency features in the high-frequency details are extracted, and a reconstructed CT image is generated based on the low-frequency structure of the image and the multi-scale high-frequency features. The invention provides a CT (Computed Tomography) reconstruction method based on coarse-fine segmentation, so that artifacts are removed and more tiny tissue textures are reserved on the premise of reserving global structures such as key bones and blood vessels.

Embodiments of the present application provide a cryo-EM image reconstruction method and device, electronic equipment and storage medium, relating to the technical field of image reconstruction, the method comprising: acquiring two-dimensional projection image samples collected by cryo-EM, and sampling prior latent variables from a preset prior distribution; for each two-dimensional projection image sample, sampling from the current variational posterior distribution by stochastic gradient Langevin dynamics to obtain a plurality of negative latent variable samples corresponding to the two-dimensional projection image sample; optimizing the discriminator network based on the prior latent variables and the negative latent variable samples; based on the negative latent variable samples, jointly updating the parameters of the encoder network and the decoder network to optimize the three-dimensional structure reconstruction quality of the cryo-EM. Through the method provided in the embodiments of the present application, the conformational diversity, posterior fitting accuracy and final reconstruction quality of the cryo-EM three-dimensional structure reconstruction are significantly improved without relying on other additional constraints.

The application discloses a kind of based on genetic algorithm's cylindrical surface hydrophone array optimization and sound field reconstruction method, belong to underwater sound field measurement and reconstruction technical field, this method includes: by simulation and equivalent source method, calculate the relative reconstruction error based on real sound pressure and equivalent sound pressure on cylindrical surface;Randomly initialize genetic algorithm population and repair the minimum spacing of constrained hydrophone;In the main loop of genetic algorithm, with the multi-objective fitness function including reconstruction error, space coverage, uniformity index and spacing penalty drives optimization, the population is iteratively updated by selection, crossover, mutation and elite reservation, and then the optimal hydrophone array layout is output;Based on the optimal hydrophone array layout, sound pressure is collected and L1 regularization sparse equivalent source method is used to complete sound field reconstruction.The application deeply couples array layout and reconstruction algorithm, minimizes reconstruction error under fixed number of hydrophones, considers array physical realizability, and significantly improves underwater sound field reconstruction accuracy.

The application provides a self-adaptive and privacy-protected source channel joint coding online segmentation learning method, comprising the following steps: based on a preset codec network architecture, a segmentation point set containing multiple candidate segmentation points is constructed, each segmentation point divides the encoder into a sending end and a receiving end; based on a context multi-arm tiger machine algorithm, the optimal segmentation point at the current moment is selected from the segmentation point set according to the obtained communication system state information at the current moment; a segmentation reasoning process is performed according to the optimal segmentation point, the sending end obtains intermediate features by processing the input data through the encoder network layer before the optimal segmentation point, and transmits the intermediate features to the receiving end, the receiving end decodes the intermediate features to reconstruct output data; in response to detecting that the data source distribution drifts or the channel condition change amount exceeds a preset threshold, online adaptive segmentation training is triggered. The application realizes the triple balance of reconstruction accuracy, communication overhead and privacy protection.

This invention discloses a spectral reconstruction method for a channel-type polarization spectrometer based on adaptive sparse bases, comprising the following steps: 1. Input data: Obtain the measured spectrum through a CSP modulation system; 2. Obtain the preliminary reconstructed spectrum: Input a sparse basis matrix composed of a traditional Legendre polynomial and a DCT basis, and obtain an approximate spectral solution using compressed channel polarization technology; 3. Form an adaptive sparse basis matrix and iteratively solve for the reconstructed spectrum: By removing "useless atoms" from the original sparse basis and adding "sub-subdivision atoms" to the remaining effective atomic functions, an adaptive sparse basis matrix is ​​formed. The adaptive sparse basis matrix is ​​then iteratively solved to obtain a better reconstructed spectrum. This invention improves the reconstruction effect of traditional compressed channel polarization methods for high-frequency spectral signals and those with multiple absorption peaks, further enhancing the reconstruction accuracy of high-frequency complex spectra.

This invention discloses a super-resolution reconstruction method for the carbon skeleton of a fuel cell catalyst layer using FIB-SEM. The invention employs a high-precision 3D digital structure generation method to construct high-resolution supervision labels required for super-resolution training. Based on the same digital structure, a corresponding low-resolution SEM image is generated through Monte Carlo electron scattering simulation, thus establishing a one-to-one correspondence training sample set between "low-resolution imaging representation and high-resolution real structure". Since the high-resolution supervision labels are directly derived from the digital structure generation process, smaller unit scales can be used to express the nanopores and skeleton details of the catalyst layer during the structure construction stage. The low-resolution input is obtained from physically consistent SEM imaging simulation, reflecting the resolution loss and signal degradation in the real imaging process. This bypasses the difficulty in obtaining real high- and low-resolution paired samples and the training data limitations imposed by the approximately 3 nm lateral resolution of real FIB-SEM, effectively improving the recognizability and reconstruction capability of the catalyst layer microstructure features.

The application discloses a multi-view three-dimensional reconstruction method and device, and the method comprises the following steps: extracting picture features of three scales of an input image by using a two-dimensional UNet network; optimizing a feature sequence by using a self-attention mechanism; performing two-dimensional to three-dimensional conversion by using homography transformation, and constructing an initial cost volume; inputting the initial cost volume into a three-dimensional UNet network to perform softmax and soft-argmin operations to obtain an initial depth map D k，i Wherein k represents three stages, and the depth map generated in the previous stage is taken as an additional input of homography transformation in the next stage; the depth information entropy of the initial probability volume is taken as a weight to fuse the initial cost volume; and the fused cost volume is subjected to the softmax and soft-argmin operations of the three-dimensional UNet network to generate a depth map D0. The application improves the integrity and accuracy of multi-view three-dimensional reconstruction of an object.

The invention relates to a three-dimensional scene reconstruction method based on aerial photography image data and laser point cloud, and the method comprises the steps: carrying out the data preprocessing of the aerial photography image data and the laser point cloud data, and obtaining corrected image data and classified point cloud data; performing feature extraction and registration processing on the corrected image data and the classified point cloud data to obtain registration image data and registration point cloud data; fusing the registration image data and the registration point cloud data to generate feature fusion data; performing point cloud triangulation processing and texture mapping, and reconstructing a three-dimensional scene surface; and carrying out geometric optimization and detail enhancement on the surface of the three-dimensional scene to obtain a three-dimensional scene model. According to the method, data preprocessing can be carried out on the aerial photography image data and the laser point cloud data, then feature extraction, registration processing, data fusion, triangulation processing, texture mapping, geometric optimization and detail enhancement are carried out, the aerial photography image data and the laser point cloud data can be fully and effectively utilized, and the three-dimensional scene reconstruction effect is improved.

The method for super-resolution reconstruction of panoramic monitoring image of protection system of extra-high voltage converter station belongs to the technical field of power equipment detection, and solves the problems of unclearness and low resolution of the existing panoramic monitoring image, which cannot meet the demand of panoramic monitoring of inspectors; by adopting multi-scale convolution blocks to construct low-order and high-order features of images of various scales in the deep multi-scale residual network model, the phenomenon of incomplete extraction of image details is avoided, the residual learning mechanism is adopted in the network model to retain low-order rough features, reduce the training difficulty, promote the reuse of features, and thus improve the reconstruction capability of the image; the reconstructed image has better structural similarity and peak signal-to-noise ratio performance; the standard data set and the panoramic monitoring image data set of the extra-high voltage converter station are used in sequence for image super-resolution reconstruction and target recognition experiment, and the experimental results show that the high-resolution image reconstructed by the method can meet the demand of panoramic monitoring of inspectors.

The embodiment of the application provides a kind of double hand-hinged object pose estimation method and related equipment, belong to computer vision technical field.The method includes: extracting the feature of double hand and object from input image and encoding as joint feature;From global feature, the left hand and right hand position query representing double hand area are predicted and extracted;Interact with double hand position query, generate double hand relative position query representing the relative position relationship of double hand, and combine it with the hand parameter query and object parameter query obtained by mapping into joint query;Using attention masking mechanism, make joint query and joint feature interact, match the most relevant feature information for each query;Finally, the parameters of both hands, the parameters of the object and the hinge angle correction amount are added to obtain the final hinge angle, and the three-dimensional grid of the double hands and the hinged object is output.The application significantly improves the accuracy and rationality of pose estimation by explicitly modeling the correlation between the relative position of the hands and the hinged state of the object.