114 results about "Ultrahigh resolution" patented technology

The invention discloses a method for reconstructing an ultrahigh-resolution pathological full slice. The method comprises the following steps: acquiring an effective tissue area image of a pathological tissue fragment by utilizing a fusion mask; an edge classification model and an image enhancement strategy are adopted to obtain an effective splicing edge of the effective tissue area image; constructing a multi-dimensional matching scoring system to calculate a geometric matching degree of a splicing edge, and screening out an optimal Top-N splicing scheme in combination with splicing quality evaluation of a deep neural network; and based on a genetic algorithm, combining global topological optimization, sub-pixel level fine tuning and a multi-dimensional matching scoring system to evaluate splicing quality, realizing iterative stitching of the effective tissue area image, and finally outputting an optimal splicing scheme. Reconstructing a full-slice image of the pathological tissue based on the optimal splicing scheme; the method has efficient and accurate automatic splicing and iterative optimization capability, and rapid reconstruction of the ultrahigh-resolution pathological full slice is realized.

The embodiment of the invention provides a high-resolution portrait photo processing method and device, equipment and a medium, and relates to the technical field of image processing. The method comprises the following steps: acquiring an original portrait picture, wherein the original portrait picture is a high-resolution portrait picture; performing skin segmentation on the original human image photo to obtain a skin area mask; slicing the original human picture to obtain a plurality of image slices; determining a target image slice from the plurality of image slices by adopting a skin area mask; processing the target image slice by adopting a preset flaw removing network to obtain a target flaw removed slice image of the target image slice; and generating a target effect picture corresponding to the original figure picture according to the target flaw removal slice picture and the original figure picture. Therefore, by firstly slicing and then removing the flaws, the flaw detection is avoided, the flaw removing precision is improved, the image processing of the ultrahigh-resolution large image is also realized, the image processing application range is expanded, and the image processing cost is reduced.

The invention discloses a one-dimensional ultra-wide field-of-view ultra-high spectral resolution dispersion coherent imaging spectrometer, which belongs to the technical field of spectral imaging, and comprises a first microlens array used for obtaining incident light of a one-dimensional ultra-wide field-of-view in a horizontal plane; the plurality of optical fibers are used for transmitting incident light of a one-dimensional ultra-wide field of view; the second micro lens array is used for collimating incident light from each optical fiber into broadband parallel light beams; the grating beam splitter interferometer is used for dispersing the broadband parallel light beams into a plurality of narrow-band parallel light beams with different propagation directions; the cylindrical lens is used for converging the two paths of coherent light of each narrow-band parallel light beam to a target block of the detector, and converging the narrow-band parallel light beams with different propagation directions to different target blocks respectively; and the detector is used for obtaining a continuous broadband ultra-high resolution spectrum according to the two paths of coherent light received by each target block. According to the invention, a one-dimensional ultra-wide field of view is obtained in a horizontal plane, and ultra-high spectral resolution can be obtained.

The invention belongs to the technical field of chemical detection, and particularly relates to a Raman-LIBS-mass spectrometry combined analysis device and analysis method.The Raman-LIBS-mass spectrometry combined analysis device comprises a multiple reflection time-of-flight mass spectrometer, an LIBS spectrometer, a Raman spectrometer and a common module; the common module comprises a first incident light path system, a second incident light path system, a high-definition observation system, a vacuum sample bin, a vacuum system and a control system; the first incident light path system is used for generating a femtosecond laser beam and performing three-dimensional denudation on a sample; and the second incident light path system is used for combining the tunable laser into a resonant ionization light path. Compared with the prior art, in-situ micro-area ultrahigh-resolution element and isotope analysis and imaging can be achieved, ultra-trace element and isotope analysis and isotope ratio analysis under strong matrix interference are achieved, and meanwhile high-resolution molecular structure and composition analysis and Raman scattering imaging can be conducted.

The invention discloses an ultrahigh-resolution immersive scene rendering method based on hexagonal picture segmentation. The method comprises the following steps: 1, determining a hexagonal segmentation style of an immersive display surface; 2, unfolding the immersive display surface into a plane; 3, acquiring 3D point information of each hexagonal surface, respectively calculating the width W and the height H of the enveloping quadrilateral surface, and calculating a local coordinate system V of the hexagonal surface S; 4, using W, H and V to construct a view cone and a viewpoint matrix of the three-dimensional scene; 5, setting a shade M of a plane expansion view for the hexagonal surface S; 6, calling a rendering scene of the 3D scene for the mask region by using information of the view cone and the viewpoint matrix, and rendering the mask region into a picture at the position S of the hexagonal surface; and 7, repeating the steps 5-6, and sequentially carrying out the same treatment on each hexagonal surface. According to the method and the device, the ultrahigh-resolution picture can be rendered in a scene segmentation form, and the method and the device are suitable for ultrahigh-resolution picture rendering under immersive display equipment.

According to the broadband ultrahigh-resolution spectrometer grating turntable precision control method based on the PSO-RBF neural network provided by the invention, the PSO algorithm is taken as an efficient global optimization method, the PSO algorithm and the RBF neural network are organically combined, the PSO global optimization and the online learning ability of the RBF neural network are fused, and the precision of the grating turntable of the broadband ultrahigh-resolution spectrometer is improved. An intelligent control system suitable for precise control of the grating rotary table is constructed, effective compensation of the system for nonlinear characteristics and parameter uncertainty is achieved, the positioning precision and the anti-interference capacity of the system are improved, and the requirement for ultrahigh-resolution spectral measurement under the condition of multiple diffraction is met.

The application belongs to the technical field of chemical detection, and particularly relates to a Raman-LIBS-mass spectrometry combined analysis device and analysis method. The Raman-LIBS-mass spectrometry combined analysis device comprises a multiple-reflection time-of-flight mass spectrometer, a LIBS spectrometer, a Raman spectrometer and a shared module. The shared module comprises a first incident light path system, a second incident light path system, a high-definition observation system, a vacuum sample bin, a vacuum system and a control system. The first incident light path system is used for generating a femtosecond laser beam and performing three-dimensional ablation on a sample. The second incident light path system is used for combining tunable lasers into a resonance ionization light path. Compared with the prior art, the application can realize in-situ micro-area ultra-high resolution element and isotope analysis and imaging, realize ultra-trace element, isotope analysis and isotope ratio analysis under strong matrix interference, and can also perform high-resolution molecular structure and composition analysis and Raman scattering imaging.

The application discloses a high-uniform seamless splicing system and a brightness adjusting method for light field display, and belongs to the technical field of light field display. The system uses a minimum display unit modular splicing architecture to realize super-high resolution display, adopts a composite microlens array to realize axial displacement of an imaging surface, covers a gap between adjacent display units through optical expansion of a pixel matrix, and eliminates parallax faults caused by physical splicing seams. The system integrates a distributed dynamic uniform light module, collects brightness data of a display area in real time, performs brightness compensation on a low-illumination area through array light source driving control, changes a light transmittance, and regulates and controls pixel-level brightness uniformity of the system. The scheme effectively solves the problems of pixel continuity loss and adjacent dimming area light coupling in high-resolution light field reconstruction. The system is widely applicable to light field three-dimensional display, and is a scheme for improving three-dimensional display effect.

The invention discloses a picture loading method and device, equipment, a storage medium and a computer program product, and the method comprises the steps: responding to a preview request of a client for a to-be-loaded picture, obtaining the size of a current display window of the client and the position information of the to-be-loaded picture, and determining an initial fragment of the to-be-loaded picture currently displayed by the client; determining a corresponding next-level target fragment according to the initial fragment; according to the position information of the target fragments, the average feature number and the size of the current display window, calculating a loading weight of each target fragment; and according to the loading weight of each target fragment, loading each target fragment on the client in sequence for display. According to the method, multi-fragment loading display is added, loading of the ultrahigh-resolution picture is supported, the picture fragments are optimized, the granularity of preview is refined, the weights of the loaded fragments are determined according to the image features, the preview experience of a key area is effectively improved, and the loading speed of the fragments which a user is interested in is increased.

The invention discloses an optical imaging lens and imaging equipment. The optical imaging lens sequentially comprises, from an object side to an imaging surface along an optical axis, a first lens having negative focal power and with the object side surface to be a convex surface and the image side surface to be a concave surface, a second lens having negative focal power and with the object side surface to be a concave surface and the image side surface to be a convex surface, a third lens having positive focal power and with both the object side surface and the image side surface to be convex surfaces, a diaphragm, a fourth lens having positive focal power and with both the object side surface and the image side surface to be convex surfaces, a fifth lens having positive focal power and with both the object side surface and the image side surface to be convex surfaces, a sixth lens having negative focal power and with both the object side surface and the image side surface to be concave surfaces, and forming a cemented lens group with the fifth lens, and a seventh lens having positive focal power and with both the object side surface and the image side surface to be convex surfaces. The optical imaging lens provided by the invention has the characteristics of ultrahigh resolution, good thermal stability, large imaging surface and convenience in assembly.

The present application relates to a kind of railway tunnel disease intelligent detection method, including mixed resolution image acquisition, mixed resolution normalization preprocessing, double channel parallel disease detection, physical quantity calibration based on dynamic precision coefficient, dynamic splicing algorithm based on ROI feature fusion and mileage error dynamic calibration based on semantic anchor point.The present application is characterized in that, by mixed resolution normalization preprocessing, the compatibility problem of heterogeneous hardware is solved, the disease feature extraction precision of key area is retained using double-scale cascade detection mechanism, with dynamic precision coefficient matrix, the pixel physical mapping deviation caused by heterogeneous resolution is corrected, the accuracy of disease quantitative analysis and physical quantity inverse calculation is ensured;The present application significantly improves the processing efficiency of ultra-high resolution image, eliminates the mileage cumulative positioning error in long-distance inspection, realizes the full-quantitative accurate detection of track traffic tunnel disease in low-cost, high-efficiency environment.

The invention relates to the technical field of video processing, and provides a video image quality evaluation method and device, and the method comprises the steps: carrying out the pyramid zooming of each video frame in a to-be-evaluated video frame sequence, and obtaining a plurality of pyramid images with different resolutions corresponding to each video frame; for each layer of image in the pyramid image corresponding to each video frame, uniformly sampling Gh * Gw image blocks with the resolution of fh * fw, and combining the image blocks to obtain a fragment image with the resolution of (Gh * fh) * (Gw * fw); performing space masking and time masking on the fragment images to obtain a target image sequence; and inputting the target image sequence into a video image quality evaluation model to obtain a score output by the video image quality evaluation model, the video image quality evaluation model being obtained by training based on a target image sequence sample and a score tag corresponding to the target image sequence sample. According to the method and the device, the ultrahigh-resolution video frame sequence to be tested is accurately evaluated, and the evaluation result conforms to subjective perception.

This invention discloses an ultra-high resolution compressed spherical beamforming sound source identification method, comprising the following steps: 1) constructing a two-dimensional off-grid compressed spherical beamforming model in the spherical harmonic domain based on Taylor expansion; 2) providing prior assumptions for the unknown variables in the two-dimensional off-grid compressed spherical beamforming model in the spherical harmonic domain, and calculating the joint probability density function of the unknown variables; 3) solving for the unknown variables in the two-dimensional off-grid compressed spherical beamforming model in the spherical harmonic domain according to the joint probability density function of the unknown variables, and estimating the sound source DOA and sound source intensity. This invention overcomes the basis mismatch problem, accurately estimates the off-grid sound source DOA and quantizes the sound source intensity, and achieves ultra-high resolution.

The utility model discloses an ultrahigh-resolution piezoelectric controller, and relates to an ultrahigh-resolution piezoelectric control technology. In order to solve the problems of limited DAC resolution, slow closed-loop control response speed, large signal noise and drift, multi-DAC superposition error accumulation and the like, the utility model provides a controller circuit board which comprises a power supply unit, a main control unit, a driving unit and a sensing servo unit. The power supply unit provides a stable multi-stage power supply; the master control unit adopts a 32-bit single-chip microcomputer and integrates a DA conversion circuit, an AD conversion circuit and an analog signal input unit. The driving unit amplifies the main control signal and drives the piezoelectric ceramic actuator. The sensing servo unit comprises a signal conditioning circuit and a PID closed-loop control circuit. The utility model also discloses an ultrahigh-resolution piezoelectric controller comprising the circuit board. The piezoelectric driving device is widely applied to the fields of precise motion control, nanoscale positioning, micro-nano manufacturing, acoustic equipment, medical equipment and the like, and high-precision, low-noise and stable piezoelectric driving control is achieved.

The application discloses an unmanned aerial vehicle active avoidance method and relates to the technical field of unmanned aerial vehicles.The method comprises the following steps: SA: weather self-adaptive sensing: acquiring the physical characteristics of an external environment through an anti-scattering laser radar group, an ultrahigh resolution millimeter wave radar group and an anti-turbulence sound wave positioning group; SB: multi-modal decision fusion: aligning laser radar data and millimeter wave radar data, and obtaining the confidence interval of each sensor through Gaussian process regression calculation to determine the weight size of the sensor; SC: hierarchical fault-tolerant control: adjusting the sensor combination according to the visibility of the external environment, and determining the emergent obstacle avoidance trajectory according to the set UWB positioning module and the inflatable buffer structure. According to the application, the sensor combination can be automatically switched according to the visibility, and after the emergency mode is triggered, the inflatable buffer structure is started, so that the safety of the unmanned aerial vehicle hovering or landing trajectory can be ensured, and the obstacle avoidance success rate is improved.

The invention discloses an ultrahigh-resolution image semantic segmentation algorithm based on semantic feature enhancement, and belongs to the field of computer vision. The method is characterized by comprising the following steps: step 1, inputting an original image I0; 2, preprocessing the original image I0 to obtain an image I1 and an image P1; 3, performing double-branch feature extraction on the image I1 and the image P1; step 4, carrying out feature fusion on the feature map obtained through the double-branch feature extraction in the step 3 to obtain a final feature map; and 5, generating a final semantic segmentation mask with the same resolution as the original image I0 through the final feature map obtained in the step 4. According to the ultrahigh-resolution image semantic segmentation algorithm based on semantic feature enhancement, lossless compression and information enhancement are realized for the original image by using the advantages of wavelet transform in the aspects of time-frequency analysis and multi-resolution representation of the signal, and dual-branch feature extraction is adopted, so that the image segmentation accuracy is improved. And the contradiction among the segmentation precision, the memory consumption and the reasoning speed is balanced.

The invention provides a bone evaluation method based on neural network enhancement and finite element analysis. The bone evaluation method comprises the following steps: acquiring an ultrahigh-resolution CT image of a living vertebra; performing domain conversion processing on the CT image by adopting a generative neural network; carrying out resampling and segmentation on the enhanced CT image; constructing a bone trabecula microstructure model in the cancellous bone mask area; assembling the RVE unit and the cortical bone model into a complete vertebral finite element model, and applying a multi-working-condition load to the RVE unit to calculate anisotropic material parameters; and carrying out axial compression finite element analysis on the complete centrum model, and judging the bone health state. According to the method, information of two dimensions of bone mass and bone, especially contribution of a bone trabecula microstructure to bone strength, is comprehensively considered, bone degeneration and fracture high-risk patients can be recognized earlier, and a scientific basis is provided for clinical early intervention.

The application discloses an equal-wavenumber division waveband noise reduction method for an infrared Fourier transform spectrometer, and is applied to fine spectral detection with wide spectral range and ultrahigh resolution. When designing a low-temperature infrared Fourier transform spectrometer, a low-temperature filter wheel is arranged in an optical path in front of a detector, and the low-temperature filter wheel is driven by a low-temperature motor. A plurality of filters are arranged on the filter wheel, the central wavelengths of the filters are different, the central wavelength intervals are different, the central wavelengths and the bandwidths of the filters are designed according to equal central spectral intervals, each filter has a narrow bandwidth, and according to a spectral range of a required detection target, a corresponding filter is selected to realize spectral detection with ultrahigh spectral resolution of a concerned spectral range. For the infrared Fourier transform spectrometer with high spectral resolution and wide spectral bandwidth, the measurement brings the advantage of reducing noise caused by out-of-band photons on the detector, and low temperature of the filter reduces infrared background and photon noise caused by the filter body.

The present invention relates to the field of computer vision, and specifically to a balanced global and local representation network and method for change detection in ultra-high resolution remote sensing images. The network proposed in the present invention can retain more change features while effectively balancing global and local representations, and finally use deep semantic information to guide the generation of change maps. Specifically, the network proposed in the present invention uses a visual mamba feature encoder instead of ordinary convolutional neural network feature encoding, and uses a gated fusion method, a balanced global and local representation method, and a deep semantic refinement method to process the features of the visual mamba feature encoder. It maintains more change features, while balancing global and local features, and uses deep semantic information features to suppress pseudo-change areas and enhance change areas. The present invention uses the above-mentioned network in conjunction with an ultra-high resolution remote sensing image change detection method to greatly improve the performance of ultra-high resolution remote sensing image change detection.

The invention relates to the technical field of image processing, in particular to a real-time image intelligent splicing processing system based on multi-path fusion, which comprises a data preprocessing module, a parameter generation module, a frame synchronization module and a fusion output module, the data preprocessing module is used for generating a corresponding full-resolution correction image and a corresponding low-resolution thumbnail; the parameter generation module is used for generating a splicing control parameter set; the frame synchronization module is used for synchronizing the splicing control parameter set with the corresponding full-resolution correction image; the fusion output module is used for generating and outputting a spliced full-resolution video stream; a real-time splicing process is decoupled into two links of intelligent parameter calculation based on a low-resolution thumbnail and parameter-based full-resolution image accurate execution, so that a real-time intelligent splicing effect with low delay, high image quality, scene self-adaption and efficient bandwidth in a real-time splicing process of an ultrahigh-resolution video is realized.

The invention discloses an ultrahigh-resolution image target detection method and system based on weak segmentation, and the method comprises the steps: obtaining an original ultrahigh-resolution image, segmenting the original ultrahigh-resolution image through a multi-overlapping-degree weakening segmentation mechanism, and obtaining a plurality of sub-image instance sets with different overlapping degrees; inputting the plurality of sub-graph instance sets into a convolutional neural network feature extractor in parallel, and fusing the multiple weak segmentation features of the plurality of sub-graph instance sets input in parallel to obtain a fused feature graph; and inputting the fused feature map into a target detection model for prediction to obtain a plurality of candidate frames detected by the same target in the sub-maps with different overlapping degrees, processing the plurality of candidate frames by adopting a weak segmentation boundary consistency optimization mechanism, and outputting a final target detection result. High calculation efficiency is kept while small target information is not sacrificed and a target truncation problem is effectively avoided.

This invention relates to the field of computer technology and discloses a distributed display control method and system supporting multimedia file display. The method includes, in response to a display command, acquiring a multimedia file and loading it to a playback control terminal, generating an overall display screen, and splitting it into multiple sub-screens using a graphics card internal splicing method to form multiple display signals. These signals are then sent to multiple correspondingly connected encoding transmission nodes, encoded as network signals, and combined at the display control terminal into a virtual extended signal source. In response to a display arrangement command for the extended signal source, the display control terminal associates the extended signal source with a decoding display node and sends control commands to the decoding display node to decode the network signals received from the encoding transmission node into display signals and drive the corresponding display terminal to display the image. This invention supports the splicing and output of ultra-high resolution multimedia content, enabling direct display without the need for additional playback equipment or manual switching.

The invention discloses a dynamic range compression image enhancement algorithm for an ultrahigh-resolution high-dynamic image and hardware thereof, and the algorithm comprises the steps of RGB-XYZ color space conversion, n-frame average maximum brightness statistics, adaptive brightness zooming, dynamic mapping factor calculation, segmented low-bit mapping and the like, and achieves high-dynamic range precise compression. Hardware adopts a modular design, and ASIC / FPGA deployment is supported. According to the method, the dynamic range compression from the high-bit high-dynamic image to 8-bit display is efficiently completed, the details of a dark part and a medium-brightness region are reserved to the maximum extent, and the visual effect of an original scene is restored; a traditional high-complexity algorithm is abandoned, the calculation amount and the on-chip storage cost are reduced by simplifying the design, and low-time-delay and low-power-consumption deployment is achieved; and adaptive parameters and dynamic mapping are adopted, so that the problems of overexposure and underexposure caused by a fixed mapping curve are avoided, the quality of a reconstructed image is remarkably improved, and the high-quality display requirement in a low-delay and low-power-consumption scene is perfectly met.

A circulating refrigeration system without liquid helium consumption. The system comprises: a liquid-helium-free refrigerating machine (40); a copper shielding tube (10), one end of the copper shielding tube (10) being connected to a shielding cylinder (5) of the liquid-helium-free refrigerating machine (40), and a first-stage cold head connector (11) and a second-stage cold head connector (13) being provided at the other end of the copper shielding tube (10); a first-stage cold shield, which is connected to the first-stage cold head connector (11) by means of a copper braid and is arranged on the outer ring of a μ metal cover; the μ metal cover, which is connected to the first-stage cold head connector (11) by means of the copper braid and is connected to the first-stage cold shield by means of a first-stage cold shield copper braid, wherein the μ metal cover, as a fixing ring, is in vacuum connection with a lower pole piece of an electron microscope, and the μ metal cover is connected to a Z-axis piezoelectric ceramic holder in a sample chamber (8) by means of a thermally insulating gasket; and a sample stage fixing member (28), which is connected to the second-stage cold head connector (13) by means of the copper braid. The present application can achieve a required liquid-helium temperature range environment for a long time, thereby greatly reducing the experiment cost, reducing mechanical vibrations, improving the transverse resolution, and thus realizing an ultrahigh resolution of an electron microscope.

This invention relates to the field of satellite simulation technology, and discloses a method, device, and electronic equipment for simulating ultra-high resolution multi-view satellite images. The method includes: constructing a rigorous imaging geometric model between ground points in a preset three-dimensional scene and image points of a satellite sensor based on atmospheric refraction correction and solid tide correction; generating global illumination realistic shadows under the rigorous imaging geometric model based on solar radiation, and simulating the pixel output of the satellite sensor according to the global illumination realistic shadows to obtain a multi-view satellite image model; simulating different view images of the satellite sensor based on the multi-view satellite image model to obtain multi-view satellite images, and determining the rational function model of the rigorous imaging geometric model. This invention can overcome the limitations of traditional DSM / DOM data sources, taking into account the requirements of multi-view, ultra-high resolution, ultra-high precision, and fine detail, and achieving engineering simulation output of multi-view satellite images at the 0.2m level.

The present invention relates to an optical frequency detection chip, a demodulation parameter calibration method, and an optical frequency demodulation method. The optical frequency detection chip of the present invention is loaded on a substrate with a first coupler as an input port and a first Mach-Zehnder interferometer and a second Mach-Zehnder interferometer corresponding to the two outputs of the first coupler; the two interferometers each have four outputs, each output is detected by a photodetector, and the four outputs of each interferometer are divided into two pairs, the phase difference between the two interference signals of each pair is 180°, and the phase difference between the two pairs is 90°. The two pairs of signals change with a sine or cosine function, and the change corresponds to the measured optical frequency information. The present invention can measure the optical frequency in real time and quickly, and measure the optical frequency increment with ultra-high resolution. At the same time, the robustness of the chip measurement is improved by calibrating the non-ideal phase deviation of the two 90° mixers.

The present application belongs to the technical field of face attribute fidelity editing, and relates to a super-resolution face attribute lossless editing method, device and readable storage medium; an original super-high resolution source image is input into a local perception network, a core topological anchor point set of a target face is output, a forward spatial affine transformation mapping operator is solved to perform affine clipping and normalized projection on a region of interest containing the target face in the original super-high resolution source image, and a local feature sub-manifold image is obtained; the local feature sub-manifold image is input into a generative adversarial network to output an evolved feature manifold image; a matrix inversion operation is performed on the forward spatial affine transformation mapping operator to obtain an inverse spatial mapping operator to perform topological inverse projection transformation on the evolved feature manifold image, and output a low-dimensional evolved image; the low-dimensional evolved image is enlarged and rotated to a physical coordinate system of the original super-high resolution source image by using a high-order resampling interpolation algorithm, and a high-dimensional registered evolved image is obtained.

The utility model provides a brace-shaped magnetic resonance oral cavity wireless coil and magnetic resonance imaging equipment. The magnetic resonance oral cavity wireless coil comprises a plurality of wireless resonant rings and a flexible substrate, each wireless resonant ring is of a flexible loop structure, the wireless resonant rings are arranged in an array mode, every two adjacent wireless resonant rings are coupled, the wireless resonant rings are used for receiving magnetic resonance signals, the wireless resonant rings are arranged on the flexible substrate, and the flexible substrate is arranged on the flexible substrate. The flexible substrate is used for being attached to an oral cavity area. The unique tooth socket-shaped structure is tightly attached to oral anatomy, the sensitivity is high, upper and lower dentitions can be imaged at the same time, full-oral scanning is achieved, the process is greatly simplified, the coil has ultrahigh resolution and excellent signal-to-noise ratio, dental crowns, root canals and periodontal tissues can be accurately captured, and excellent soft tissue contrast is provided. The compact and high-adaptability design gives consideration to the comfort and diversified requirements of the patient.

The invention provides a method for estimating the biomass of vegetation between photovoltaic panels, and the method comprises the steps: obtaining a global vegetation index image of all photovoltaic arrays and a local vegetation index image of a part of photovoltaic arrays in a predetermined region, and carrying out the calculation of an inter-panel vegetation index image according to the local vegetation index image, adjusting the resolution of the inter-plate vegetation index image to be consistent with the resolution of the global vegetation index image; reconstructing the global vegetation index image according to the inter-plate vegetation index image after the resolution is adjusted to obtain a global reconstructed vegetation index image; and according to a pre-constructed inversion equation between the local vegetation index image and the biomass and the global reconstruction vegetation index image, calculating to obtain a spatial distribution diagram of the biomass between the photovoltaic array plates. According to the method, ultrahigh-resolution remote sensing image data does not need to be adopted, the high-precision global vegetation index image covering the large-area photovoltaic array is efficiently and rapidly obtained, and the estimation precision and efficiency of the inter-plate vegetation biomass in the photovoltaic array are improved.

The application provides an efficient super-high-resolution image restoration method based on dynamic dataset distillation, comprising the following steps: obtaining an original degraded image dataset and an original clear image dataset, and constructing a complete dataset; after down-sampling the original degraded image dataset and the original clear image dataset, respectively sending them into a ViT encoder and a CLIP encoder for feature extraction, calculating the variance of the first feature map extracted by the ViT encoder as a first complexity score, and calculating the variance of the second feature map extracted by the CLIP encoder as a second complexity score; based on a dynamic sample selection mechanism, assigning a complexity weight and an uncertainty weight to each image data, and calculating a selection score for each image data; selecting image data with a high selection score to construct a data subset, and fine-tuning the data subset based on a convolutional neural network; and training an image restoration model based on the data subset. The application solves the high cost problem of current deep learning in image restoration.