169 results about "Resolution improvement" patented technology

The invention discloses a space-time spectrum combined super-resolution reconstruction method based on a giant remote sensing satellite group. The method comprises the following steps: acquiring time series, multi-view and multi-spectral data of the same area from a plurality of heterogeneous satellites, and performing radiometric calibration and atmospheric correction; sub-pixel-level alignment of the multi-source data is realized by adopting a joint registration model; extracting time change features by using three-dimensional convolution, extracting space structure and texture features by using two-dimensional convolution, and extracting and reducing the dimension of spectral features by using one-dimensional convolution; performing adaptive weighted fusion on time, space and spectral features through an attention mechanism to generate a joint feature tensor; and carrying out super-resolution reconstruction to obtain a target image with high spatial resolution, high time resolution and high spectral fidelity. The method gives consideration to both resolution improvement and spectrum authenticity, and is suitable for high-precision remote sensing application scenes such as fine urban mapping, agricultural monitoring, ecological environment assessment, disaster emergency and battlefield situation awareness, remote reconnaissance, target change detection and damage assessment.

The invention discloses a submarine topography super-resolution reconstruction method based on window displacement multi-source fusion, and aims to solve the problem of precision attenuation caused by absence of multi-beam truth value evaluation and resolution improvement of an existing neural network submarine topography reconstruction method. The method comprises the following steps of: obtaining gravity anomaly, gravity vertical gradient, vertical line deviation component, submarine topography background and ship survey water depth multi-source data of a target sea area; constructing a residual U-Net attention neural network model, and performing training by taking multi-source data extracted by a 11 * 11 window and position codes as input; moving the trained model sampling window in a staggered manner according to a target resolution step length; extracting data points in the window during movement and injecting position codes; and outputting a high-resolution water depth predicted value based on the position code and the window data. The reconstruction precision is improved by fusing physical quantities, the multi-resolution robustness is guaranteed by a window displacement mechanism, and the medium-frequency feature reconstruction capability is enhanced by a residual U-Net attention model.

The invention discloses a three-dimensional seismic high-resolution strengthening method based on an adaptive reflection coefficient. The method comprises the following steps: S10, acquiring a three-dimensional seismic data volume; s20, constructing a reflection coefficient prior probability model through multi-scale dictionary construction and statistical feature extraction; s30, adaptive matched filtering: performing time-varying filter and directional compensation on the original three-dimensional seismic data volume and the Q-field model to obtain a data volume subjected to Q compensation and reflection feature enhancement; s40, three-dimensional anisotropy enhancement: carrying out structure tensor field calculation and tensor driving diffusion on the data volume in the step S30 to obtain enhanced data; and S50, joint inversion optimization: performing joint inversion by using the reflection coefficient prior probability model and the enhanced data to obtain a final high-resolution seismic body. According to the three-dimensional seismic data processing method, a physical mechanism and data driving are fused, a three-dimensional processing technology considering vertical and transverse resolution improvement is adopted, and the contradiction between resolution improvement and geological authenticity maintenance in three-dimensional seismic data processing is solved.

The invention discloses a turntable-based dual confocal image scanning microscope and an imaging method, and relates to the technical field of optical elements, systems, instruments and imaging. The imaging light source assembly, the optical fiber transmission assembly, the light source collimation assembly, the structured light generation assembly, the turntable assembly, the microscopic amplification assembly and the camera detection assembly are arranged along a light path; the structured light generation module realizes structured light illumination on a sample surface by loading a specific mask sequence, super-resolution imaging is realized, pinholes in the turntable assembly are equidistantly arranged according to an Archimedes spiral, and the turntable assembly rotates at a high speed to realize multi-point parallel scanning and de-scanning; the detection module performs detection by using an area array detector, so that the resolution improvement of double diffraction limit is realized; according to the dynamic pinhole array-pixel redistribution method, super-resolution reconstruction is carried out based on an image scanning microscopic system principle, and Stokes shift, optical aberration and other non-ideal conditions can be effectively corrected.

The invention relates to a lightweight image super-resolution reconstruction method fusing spatial domain and frequency domain features, and the method comprises the steps: S1, transmitting a low-resolution image to a convolution layer, and extracting initial local structure information, S2, transmitting the initial local structure information to a spatial domain-frequency domain feature fusion module, and carrying out the fusion of the spatial domain and frequency domain features; s3, transmitting the low-resolution image to a deep convolutional neural network to obtain a shallow feature representation with a specific semantic inspiration effect, S4, transmitting the shallow feature representation to a cross-scale feature fusion module to obtain a cross-scale feature fusion result, and S5, carrying out cross-scale feature fusion on the cross-scale feature fusion result to obtain a cross-scale feature fusion result. S5, the spatial features and the deep semantic features are spliced in the channel dimension, feature fusion is achieved through a convolution layer, and then the features obtained after feature fusion are subjected to resolution improvement through an up-sampling module and then a reconstructed image is output; the method has the advantage that high-quality image reconstruction is realized while the lightweight class is ensured.

The invention relates to the technical field of resolution improvement, in particular to a video definition improvement method and system.According to the video definition improvement method and system, through window accumulation of adjacent frame gray difference and coordinate difference, minimum displacement is selected, mapping is updated, boundary neighborhood weighted correction is carried out, cross-frame dislocation is reduced, and motion area connection is guaranteed; capturing a three-frame gray scale sequence to calculate a difference value, writing a replacement value according to a monotone and amplitude condition, inputting the replacement value and an alignment frame difference into Fourier transform, decomposing amplitude and phase distribution in a residual field in a frequency domain, and performing amplitude and phase consistency elimination on an abnormal block during frame-by-frame superposition to obtain an abnormal block; row difference and column difference absolute values of neighborhood blocks in the residual iteration texture matrix are accumulated into energy, the energy and a center difference are combined with a threshold value to output a mark, and a texture region and an edge region are distinguished; the brightness of the low-frequency position is updated according to the weight mean value, the maximum difference and the direction difference are combined for the high-frequency position, the edge gradient is extracted through the convolutional neural network, a correction value is generated, edge steps and fuzziness are suppressed, and high-frequency details are continuously kept.

The invention provides a thoracic surgery pathological section image super-resolution reconstruction method, and relates to the technical field of image processing, and the method specifically comprises the steps: collecting a high-resolution thoracic surgery pathological section image, and generating a low-resolution image; constructing a local directional dot matrix tensor and a response adjustment factor based on the low-resolution image, calculating an angle response aggregation kernel tensor, and performing segmented mapping in combination with a nonlinear response reconstruction function to complete directional fusion normalization so as to obtain a directional aggregation enhanced image; a robust local reference value and local texture energy are calculated in a neighborhood range, a structural strength weight is generated in combination with a variance balance parameter and a contrast balance parameter, a difference amplification item is constructed, image fusion with direction aggregation is enhanced through a residual mode, and a structure guide enhanced image is obtained; carrying out weighted mean and variance calculation to generate a difference regulation factor, and combining convolution smoothing and nonlinear amplitude limiting processing to obtain a structure contrast mapping image; and finally, inputting the image into an image reconstruction module to realize resolution improvement.

The invention provides a target contour reconstruction and in-plane track measurement method and system based on vision. The method comprises the steps that a polygon target is fixedly installed on a target to be measured, a camera is fixed for imaging, and a camera coordinate system is established; performing coarse precision matching on the target through a cross-correlation algorithm to obtain an ROI (Region of Interest), and iteratively updating a matching position in a time sequence video frame; sub-pixel-level resolution improvement and binarization segmentation are carried out on the ROI, and a target edge contour is extracted; performing linear fitting on the edge point set based on a gradient space point set clustering algorithm, and reconstructing a target contour; and converting the pixel displacement into a physical displacement amount, and calculating an attitude angle in the target plane based on the angular point position. According to the invention, the dynamic contour of the target is reconstructed by using the camera, and the in-plane displacement and attitude high-precision measurement is carried out; while high precision and high environmental adaptability are ensured, miniaturization, low power consumption and high efficiency of measurement equipment are realized.

The invention discloses an infrared photothermal imaging system and method in structured light illumination fluorescence detection, and belongs to the field of super-resolution imaging. The system comprises a structured light generation module, a fluorescence excitation module, a fluorescence collection module and a mid-infrared light thermal module. The structured light generation module generates structured light with adjustable phase and direction; the fluorescence excitation module inputs the structured light to a to-be-analyzed sample and excites the sample to emit fluorescence; meanwhile, infrared pulse laser can be input into a sample by utilizing a mid-infrared photothermal module, and the fluorescence intensity is modulated by utilizing a photothermal effect; finally, fluorescent light emitted by the sample is collected by a fluorescent light collecting module, and an infrared photothermal image in structured light illumination is obtained. The system breaks through the diffraction limit, the resolution ratio is increased by two times at most, the intrinsic infrared spectrum of the sample is obtained through mid-infrared photothermal imaging, and the chemical components and the structure of the sample are reflected. According to the invention, two technologies are combined, so that the intrinsic infrared spectrum information of the sample is provided under the sub-diffraction limit.

The embodiments of the disclosure provide an image data processing method for laser imaging, a computer device and a computer-readable storage medium. In the embodiments, in case where the gap distance corresponding to a previous laser is not an integer multiple of the preset width of pixel row, the image resolution for a scan area associated with a next adjacent laser is increased to the second resolution to decrease the width of pixel row in the associated scan area, thereby more pixel rows may be obtained in the same area in the raw scan image, and the pixel rows closer to the position actually scanned by the laser spot may be selected as the target pixel rows, which may decrease the deviation between the position of the pixel row actually scanned by the laser and the theoretical position of the pixel row, and further improve the laser scan imaging accuracy.

The invention discloses a pipeline inner wall defect detection method and device based on a pipeline robot and a medium, and relates to the technical field of pipeline detection.The method comprises the steps that multi-source detection data of the inner wall of a pipeline are collected and preprocessed, and a comprehensive detection data set of the inner wall of the pipeline is generated; based on the pipeline inner wall comprehensive detection data set, spiral sampling deconvolution processing is carried out to obtain pipeline magnetic signal cylindrical expansion domain data after deconvolution, and a resolution improvement index and an edge feature definition index are generated; calculating defect feature operator distribution according to the deconvoluted pipeline magnetic signal cylindrical expansion domain data and the pipeline inner wall comprehensive detection data set, and generating a suspected position point set; and through the suspected position point set, calculating a co-gradient co-orientation index and carrying out connected domain aggregation to generate a defect judgment set. According to the invention, through spiral sampling deconvolution processing, fine recovery of pipeline magnetic signal space distribution is realized.

The invention provides a seismic trace gather far offset energy preserving processing method, which comprises the following steps of: 1, decompiling field seismic data, and performing first arrival picking and trace gather extraction processing; 2, performing high-order dynamic correction processing on the extracted gather data to improve the influence of conventional dynamic correction stretching on the far offset; step 3, performing space-time variation cutting on gather first arrival waves and interference waves by adopting control points; step 4, performing offset resolution improvement processing on the gather data, and further improving the frequency dispersion problem of the far-offset data; and step 5, carrying out deconvolution, superposition and migration processing on the far-offset information preserving gather to obtain far-offset energy preserving seismic result data. According to the seismic trace gather far-offset energy maintaining processing method, recovery and maintenance of far-offset energy in seismic trace gather data can be realized, the imaging quality of seismic data of middle and shallow layers is improved, and recovery and maintenance of far-offset information are realized.

A technique is disclosed for analyzing and displaying the extent to which the images and structures inferred by a physically seeded multiscale network correspond to genuine resolution improvement through noise insensitive point spread function deconvolution, and the extent to which they correspond to the hallucination of realistic looking structures with realistic frequency contents. A relative modulation transfer function can be computed, which can represent the distribution of frequency components in a particular reconstruction (e.g., a volumetric reconstruction from high-resolution data) that are not robustly recovered by a different reconstruction (e.g., a volumetric reconstruction via processing of low-resolution data with a trained neural network). The high-frequency portion of these frequency components can represent hallucinations introduced by a trained neural network, and can be leveraged to filter the different reconstruction prior to further use.

The invention relates to the technical field of hot spot detection, in particular to a photovoltaic panel hot spot detection optimization method based on a double-model dynamic architecture. The method comprises the following steps: screening out a target visible light resolution according to hot spot confidence coefficients of hot spot matching regions in different super-resolution images corresponding to all hot spot regions in a visible light image, the number of the hot spot matching regions and the number of the hot spot regions in each super-resolution image; taking the target visible light resolution as the initial infrared resolution, and judging whether the initial infrared resolution needs to be adjusted or not according to gradient distribution, temperature distribution and corresponding hot spot confidence of the photovoltaic panel at different positions in the infrared image between the initial infrared resolution and the next super-resolution; if the adjustment is needed, taking the next super-resolution as a new initial infrared resolution until the adjustment is not needed, and obtaining a target infrared resolution; and the hot spot detection of the photovoltaic panel is realized. According to the invention, the hot spot detection efficiency is improved by obtaining the accurate detection resolution.

The invention relates to the technical field of underwater detection, and discloses a seabed sonar image feature enhancement processing method and a storage medium. The method comprises the following steps: acquiring multi-source seabed sonar image data, and performing space-time alignment and signal denoising processing to obtain a standardized sonar data sequence; performing multi-scale feature extraction on the standardized sequence to generate a feature map containing texture, shape and strength information; inputting the characteristic spectrum into a space-time attention network for characteristic weight distribution and fusion, and outputting enhanced characteristic representation; based on the enhanced features, a deep learning model is adopted to carry out resolution improvement and detail recovery, and a high-resolution sonar image is generated; and performing region division on the high-resolution image through an adaptive threshold segmentation algorithm, verifying feature consistency in combination with historical data, and finally completing feature enhancement processing. According to the method, the definition, the detail expressive force and the feature separability of the submarine sonar image are improved, and accurate recognition and analysis of submarine topography and targets are effectively supported.

The invention discloses a light off-axis structured light digital holographic microscopy super-resolution imaging method based on a Kramers-Kronig relation. In a light off-axis structured light digital holographic microscopy system, structural illumination patterns with different angle directions and phase shift amounts are loaded on an object light beam to acquire a structural illumination hologram, and an unloaded reference light beam is acquired to acquire a reference light intensity graph; the structure illumination hologram is combined with the reference light intensity graph to calculate complex amplitude by utilizing a KK relation, phase distortion of the complex amplitude is compensated to obtain distortion-removed complex amplitude, a frequency spectrum separation matrix is constructed by combining information during acquisition to perform frequency spectrum separation on high and low frequency components in the complex amplitude, and finally frequency spectrum synthesis is performed to realize super-resolution object light reconstruction. According to the invention, high resolution improvement can be realized, the interference problem of zero-order item frequency spectrum of the hologram under the light off-axis condition is effectively solved, the noise is obviously reduced, the imaging quality is improved, and the method has important application value in the fields of biological tissue observation, microstructure measurement and the like.

The invention discloses a displacement measurement system and method. The displacement measurement system comprises a laser, a beam splitting module, a resolution multiplication module, a detection module and an analysis module, a laser beam emitted by a laser enters a beam splitting module, and the beam splitting module splits the laser beam into signal light and local oscillation light; the signal light enters the detection module after passing through the resolution multiplication module, and the local oscillation light directly enters the detection module; the detection module generates an electrical signal according to interference signals of the signal light and the local oscillation light and transmits the electrical signal to the analysis module; the analysis module determines the displacement of the to-be-measured target based on the electrical signal; wherein the target to be measured is located in the resolution multiplication module, the signal light is reflected for m times in the resolution multiplication module and passes through the moving distance of the object to be measured for n times, the resolution is improved by n times, n is an even number larger than 2, and m is an integer larger than n. The displacement measurement system provided by the invention not only can improve the resolution during measurement, but also can reduce the influence of phase noise on displacement measurement.

The invention discloses a wide-area video monitoring double-spectrum ship small target registration method, which belongs to the technical field of image processing, and comprises the steps of acquiring image pairs and screening qualified image pairs, performing coarse registration, extracting ship target candidate regions, performing candidate region pairing based on spatial distance constraint to obtain spatial approximate candidate region pairs, and performing image matching based on the spatial approximate candidate region pairs. And performing image interpolation enhancement on the candidate region pair, constructing a multi-scale image pyramid, performing multi-scale step-by-step registration optimization, and outputting a high-precision registration result, thereby solving the technical problems of improving the resolution of the double-spectrum ship small target and improving the cross-modal registration precision in a wide-area monitoring scene, improving the registration concentration of the small target, and improving the registration accuracy of the small target. The problem of mismatching caused by cross-spectral feature isomerism is relieved, local feature expression of a low-resolution target is enhanced, registration robustness is improved, calculation efficiency and precision are balanced, and actual engineering deployment requirements are met.

The invention relates to a resolution improving device and method based on three-camera differential confocal, the device comprises a light source, a light path intermediate, an objective lens, an objective table, an imaging module and an industrial personal computer, the imaging module comprises a first camera placed before focus, a second camera placed on a focal plane and a third camera placed after focus; the first camera is arranged at a specified distance d in front of the focus through the first adapter ring, the second camera is arranged at the focal plane through the second adapter ring, and the third camera is arranged at a specified distance d behind the focus; light emitted by the light source is projected to a to-be-tested sample on the objective table through the light path intermediate and the objective lens, and light signals reflected on the to-be-tested sample are respectively transmitted to the first camera, the second camera and the third camera through three light paths; the industrial personal computer can control the objective lens and the objective table to move at least in the Z-axis direction. The acquisition precision of the PSF is improved by improving the positioning precision in the Z-axis direction, so that the resolution of the image acquired in the deconvolution process is improved, and the restoration effect of the three-dimensional microscopic image is improved.

The invention belongs to the field of magnetic particle imaging, particularly relates to a three-dimensional magnetic particle imaging enhancement method and system based on distillation structure contrast learning, and aims to solve the problems of anisotropic resolution and background noise interference caused by sparse scanning tracks in three-dimensional magnetic particle imaging. The method comprises the following steps: constructing a data set; building a two-dimensional isotropic teacher network model and a three-dimensional resolution improvement student network model; training a two-dimensional isotropic teacher network model and storing model parameters; the three-dimensional resolution improvement student network model obtains model parameters and then carries out training, an obtained three-dimensional training result is converted into two-dimensional training input data, and the two-dimensional training input data is input into the two-dimensional isotropic teacher network model to generate a supervision benchmark which is used for updating and optimizing the three-dimensional resolution improvement student network model; and after training, carrying out resolution improvement processing on the input three-dimensional magnetic particle imaging image. According to the method, the isotropic resolution of the three-dimensional magnetic particle image is improved, and background noise is effectively suppressed.

The application belongs to the technical field of optical lenses, and specifically provides a zoom movie lens, which comprises, from the object side to the image side, a first fixed lens group, a variable magnification lens group, a compensation lens group, a variable aperture and a second fixed lens group; the optical power of the first fixed lens group is positive; the optical power of the variable magnification lens group is negative; the optical power of the compensation lens group is positive; the optical power of the second fixed lens group is positive; wherein the variable magnification lens group and the compensation lens group are linked and arranged. The zoom movie lens provided by the application increases the resolution of the zoom movie lens and improves the stability of the performance of the zoom movie lens by adopting the four-lens group structure with positive, negative, positive and positive optical powers; the linked arrangement of the variable magnification lens group and the compensation lens group can ensure that the image surface is not moved during continuous zooming, realize parfocalization in the zooming process, and meet better image quality requirements.

This invention discloses an online super-resolution imaging method for scanning radar based on the minimization criterion. By establishing an azimuth echo convolution model and discretizing it, the super-resolution problem is transformed. Under a regularization framework, the minimization criterion is used to construct an analytically optimizeable cost function containing sparse prior information, transforming the forward-looking imaging resolution improvement problem into an optimization estimation problem. By dividing the antenna measurement matrix into rows and blocks, matrix operations are degraded into the continuous accumulation of vector operations, enabling real-time online updates of targets in the forward-looking region. Finally, the conjugate gradient method is used to degrade and invert the matrix, outputting the super-resolution result. This method can achieve rapid imaging with limited system memory through an online conjugate gradient super-resolution algorithm based on the minimization criterion. Compared with existing batch processing regularization methods, it eliminates false targets, significantly reduces computational complexity, and is beneficial for rapid imaging of scanning radar.

The application relates to the field of semiconductor optoelectronics, in particular to a self-powered up-conversion imaging array and a preparation method thereof. The technical key points are as follows: array pixel units are arranged on an insulating substrate and are mutually isolated; each unit contains a central up-conversion detection subunit and a peripheral photovoltaic power supply subunit; the photovoltaic unit is formed by connecting micro photovoltaic cells in series, and the anode and the cathode are connected with the cathode and the anode of the central up-conversion detection subunit respectively, so as to form an independent closed loop series driving circuit in the pixel; the central up-conversion detection subunit is used for receiving incident near-infrared light signals and converting the signals into visible light signals; the peripheral photovoltaic power supply subunit is used for absorbing incident light and converting the light energy into electric energy to provide a driving voltage and a driving current; the peripheral photovoltaic power supply subunit and the central up-conversion detection subunit meet the current matching condition; the effects of self-power supply at the pixel level, complete elimination of electrical crosstalk between pixels, improvement of imaging contrast and resolution, improvement of photon utilization rate and device fault tolerance are achieved.

A frequency-modulated system includes a signal generation module providing a first laser signal, a second laser signal, a third laser signal and a fourth laser signal with different frequencies to form an input laser signal in a first polarization state; the silicon optical chip divides an input laser signal into a detection signal and a local oscillator signal, receives a reflection signal component in a second polarization state as an echo signal, converts the reflection signal component into a first polarization state, and mixes the reflection signal component with the local oscillator signal; and the signal processing module performs decoupling according to the beat frequency signal to obtain distance information and instantaneous speed information of the target object. According to the disclosure, the dot frequency is improved, and smaller detection resolution is realized point frequencies in each frame and detection resolution are improved.

The application discloses a time-frequency electromagnetic exploration method and system for improving exploration depth and resolution, and relates to the technical field of exploration. The method comprises the following steps: arranging a multi-directional excitation source and a mobile remote end array observation system, and sparsely collecting time-frequency electromagnetic signals; reconstructing data and reducing noise based on compression sensing; constructing a time-frequency dual-domain joint inversion target function, introducing cross gradient constraints and physical constraints, and realizing intelligent collaborative inversion through depth reinforcement learning; outputting a high-precision three-dimensional geological model through Bayesian uncertainty analysis and multi-source data fusion verification; and the system comprises excitation, observation, data processing, intelligent inversion and interpretation visualization modules. The scheme improves exploration depth and resolution, improves data acquisition and inversion efficiency, and guarantees the accuracy of geological interpretation.

Transparent organic light-emitting diodes (OLEDs) can be used as light-emitting pixels in a near-eye display for augmented reality applications. The light from these pixels can be switchably tuned and / or steered with tunable beam-steering and focusing elements, also called tunable micro-lenses. These tunable micro-lenses are arranged in an array and mated to the array of pixels, for example, by embedding in a spectacle lens. The tunable micro-lenses use fast-switching half-wave plates to selectively focus and / or tilt light from the pixels. By switching the light from the pixels between resolvable positions / angles at a rate faster than the flicker fusion threshold (e.g., 60 Hz), the tunable micro-lenses can effectively double the apparent resolution of the near-eye display. And by switching between focusing and non-focusing states at the same rate, the tunable micro-lenses can effectively superimpose the virtual images from the pixels on the real-world image visible through the pixels.

The invention relates to the technical field of oil field seismic data processing, in particular to a method and device for improving seismic data resolution based on a wavelet domain residual network. The method comprises the steps that acquired data containing existing seismic data samples are input into a U-Net network for training, and the U-Net network comprises a residual module, a global context module and wavelet transformation; determining a total loss function of the trained U-Net network model; and according to the total loss function, adjusting and optimizing the U-Net network model to obtain an optimal model, and performing resolution improvement processing on the to-be-processed seismic data by using the optimal model to obtain a final processing result. The problems that in an existing seismic data resolution improving method, wavelet extraction difficulty is large, stratum quality factors are uncertain, well information depends on, parameters are complex and the like, component information and global context information in sample data and label data are not comprehensively utilized, and consequently the seismic data resolution cannot be further improved are solved.

The application discloses a multi-source sensor fusion method and device based on feature distillation, equipment and a readable storage medium, and relates to the technical field of data fusion. The method comprises the following steps: first, obtaining a first feature map extracted by a first sensor and a second feature map extracted by a second sensor; the performance of the first sensor is higher than that of the second sensor, and the resolution of the first feature map is higher than that of the second feature map; then, performing up-sampling processing on the second feature map to increase the resolution of the second feature map to be equal to the resolution of the first feature map; finally, distilling the features of the first feature map to the second feature map, and fusing the first feature map and the second feature map to obtain a fused feature map. The features of the high-performance sensor guide the feature extraction of the low-performance sensor, the perception accuracy of the low-performance sensor is improved, and the consistency of the effect of a subsequent perception algorithm platform on high-performance sensors and low-performance sensors is improved.

The invention relates to a flow field super-resolution reconstruction method, computer equipment and a readable storage medium. Obtaining a to-be-reconstructed multiphase flow simulation result corresponding to the to-be-simulated multiphase flow case; performing image mapping processing on the to-be-reconstructed multiphase flow simulation result to obtain to-be-reconstructed flow field image data which is in space-time alignment with the to-be-reconstructed multiphase flow simulation result; and through the trained flow field super-resolution reconstruction model, performing super-resolution prediction on the to-be-reconstructed flow field image data to obtain target reconstructed flow field image data of which the image resolution is higher than that of the to-be-reconstructed flow field image data. By converting low-resolution simulation data into image representation adaptive to deep learning and by means of a trained flow field super-resolution reconstruction model, a complex mapping relation between a high-resolution flow field and a low-resolution flow field is learned to realize rapid resolution improvement, and while the calculation precision of high-resolution flow field data is ensured, the calculation efficiency of the high-resolution flow field is improved. And the calculation efficiency of the high-resolution flow field data is effectively improved.

The present application relates to the field of VR technology, and provides a method for capturing screenshots in a virtual world and a virtual reality device for realizing partial screenshots. When capturing a screenshot, the method displays an operation box that changes with the user's viewpoint in the current virtual screen, so that the target area for capturing or recording the screen that meets the needs can be flexibly selected by adjusting the position information of the user's viewpoint relative to the current virtual screen, thereby realizing partial screenshots; and by creating a layer, the screen of the target area is superimposed and displayed on the current virtual screen, so that the user can preview the effect of the partial screenshot and ensure the accuracy of the partial screenshot. When the screenshot operation is received, the layer is hidden to avoid interference with the current virtual screen. Since the screenshot or recording operation is directly performed on the target area of ​​the current virtual screen, the original resolution of the partial screenshot image or the partial screen recording image can be guaranteed, thereby improving the quality of the partial screenshot image or the partial screen recording image.