124 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 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 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 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.

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.

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.

The invention discloses a resolution improving method and device, electronic equipment and a readable storage medium, and relates to the technical field of image processing, and the method comprises the steps: obtaining a current frame image of a first resolution, a historical frame image of a second resolution adjacent to the current frame image, and an image change parameter; performing sampling operation on the current frame image at a second resolution to obtain a first color parameter of each first pixel point in the current frame image after the sampling operation; a second color parameter of each second pixel point in the historical frame image is corrected based on a color clamping parameter and the image change parameter, a third color parameter of each second pixel point is obtained, and the color clamping parameter is determined based on the first color parameter; and performing color mixing operation on the first color parameter of each first pixel point in the current frame image based on the displacement parameter and the third color parameter of each second pixel point in the historical frame image to obtain the current frame image with the second resolution. The color effect of an image after super-resolution can be improved.

The application discloses a low-contrast image joint enhancement and super-resolution reconstruction method, which comprises the following steps: firstly, performing multi-scale Gaussian filtering on the low-contrast image; secondly, expanding the imaging dynamic range of the quotient image by using an S function; thirdly, performing color correction on the expansion result to obtain a contrast-improved image; and finally, optimizing the resolution and detail information of the contrast-improved image by a blind super-resolution reconstruction model constrained by a total variation to obtain a detail information and resolution improvement result. The low-contrast image joint enhancement and super-resolution reconstruction can help to overcome the influence of image adverse factors, such as low contrast / resolution and fuzzy details, and lay a foundation for image feature extraction and image interpretation.

The application discloses a kind of extreme resolution ratio hyperspectral image panchromatic sharpening methods based on implicit priori of convolutional neural network, including reading original hyperspectral image and corresponding panchromatic image, and pretreatment is carried out;Using the hyperspectral image and panchromatic image after pretreatment, training data set and test data set are constructed;The implicit priori convolutional neural network based on is constructed, and the implicit priori convolutional neural network based on includes low-resolution ratio panchromatic sharpening submodule and space spectrum adjustment submodule;Using training data set, the implicit priori convolutional neural network is trained, and the optimal implicit priori convolutional neural network after training is obtained;Using test data set, input optimal implicit priori convolutional neural network and predict high spatial resolution hyperspectral image.The method of the application solves the problem of hyperspectral image panchromatic sharpening under extreme resolution ratio effectively by this step-by-step resolution improvement and space spectrum adjustment strategy.

The present invention relates to the technical field of data processing. Disclosed are a data-analysis-based resolution improvement method and system for a mass spectrometer. The method comprises: training a resolution factor model; on the basis of an original mass spectrum and an original fragment map, determining basic information of a compound to be tested, determining a target signal-to-noise ratio of the original mass spectrum on the basis of a mass spectrum of a standard compound, and performing denoising processing on the original mass spectrum by means of a denoising method; on the basis of the denoised original mass spectrum and the mass spectrum of the standard compound, determining a baseline reference region; identifying features of peaks in the mass spectrum, separating overlapped peaks by means of the features of the peaks, and performing clustering analysis on all the peaks on the basis of a resolution factor; and performing dimension reduction processing on a class of peaks having data dimensions exceeding a dimension threshold value. In this way, data inconsistency caused by baseline drifts is reduced or avoided. Finally, the resolution of a mass spectrometer is improved by means of the separation of overlapped peaks, the clustering of peaks, and dimension reduction processing of the peaks, thereby ensuring the efficiency of resolution improvement.

The invention discloses a seismic data resolution improving and weak signal enhancing method, and belongs to the technical field of geophysical seismic data processing. According to the method, a high-precision seismic single channel synthesized by logging information is introduced as a physical constraint to be input into the conditional generative adversarial network, constraint is provided for a conditional generative adversarial network generator in the training and reasoning process, the physical constraint is used as auxiliary supervision of a conditional generative adversarial network discriminator, and the authenticity of a generation result and the geological rationality are improved. Meanwhile, a composite loss function combining image loss, adversarial loss, perception loss, a total variation regular term and well control matching loss is designed, and unification of pixel-level error constraint, detail feature keeping and geological physical consistency is achieved. According to the method, weak reflection signals can be enhanced under the condition of low signal-to-noise ratio, high-frequency details are recovered, structural illusion is suppressed, and the resolution and the signal-to-noise ratio of seismic data are effectively improved. The method provided by the invention still has good actual data generalization ability after synthetic data training.

A height measurement device (100) according to the present disclosure is provided with a height measurement information acquisition unit (4) that, while moving relative to an object (Ta) to be measured in the horizontal direction, acquires height measurement information for each measurement section (Mes) that is divided in each of one direction of the horizontal direction and an orthogonal direction that is orthogonal to the one direction, and acquires height measurement information for each measurement section (Mes) that is divided in each of the one direction of the horizontal direction and the orthogonal direction that is orthogonal to the one direction. Height measurement information for measuring the height of an object (Ta) to be measured is acquired. A height measurement device (100) is provided with a control unit (5) for increasing the resolution of height measurement information by adding, to the height measurement information, a plurality of pieces of second height measurement information obtained by shifting the position of a measurement section (Mes) by a predetermined amount (Loff) within the range of the length (Lpv) of the measurement section (Mes) after the plurality of pieces of first height measurement information have been obtained.

The invention relates to a display device, a display method thereof and projection display equipment, the display device comprises an imaging device, a micro-lens assembly and a pixel expansion device which are sequentially arranged in the light path direction, the imaging device comprises a plurality of pixel units distributed in an array mode, and light rays form first pixels after passing through the pixel units; the micro-lens assembly comprises a plurality of micro-lenses, the micro-lenses correspond to the pixel units in the light path direction so that the first pixels can be shrunk into second pixels after passing through the micro-lenses, and a gap exists between every two adjacent second pixels; and the pixel expansion device can respectively shift a plurality of second pixels by a preset distance along a plurality of preset directions, and fill the second pixels into the gaps, so that the size of the pixels expanded by the pixel expansion device is integer multiples of that of the first pixels. According to the invention, high-resolution improvement in a real sense can be realized, and the display effect is improved.

This system uses both visible and invisible light images, and employs machine learning with appropriate training data to perform image correction such as noise reduction and resolution improvement. [Solution] The image processing device comprises a machine learning unit that performs learning and inference for processing non-visible light images using training data, an environmental information acquisition unit that acquires information about the surrounding environment, an effectiveness determination unit that determines the effectiveness of visible light images as training data based on the environmental information acquired by the environmental information acquisition unit, and a training data selection unit that determines whether a visible light image is effective as training data for a non-visible light image that is temporally corresponding to that visible light image, based on the effectiveness determined by the effectiveness determination unit, and selects training data that has been determined to be effective.

This invention discloses a nonlocal visual enhancement and resolution improvement method, belonging to the field of image processing technology. First, the degraded image to be processed is acquired, and the initial scattering map and initial scene radiance are estimated. Then, a variational scene restoration model is constructed. Finally, the model is iteratively solved using the semi-proximal alternating direction multiplier method. In each iteration, the optimized scattering map, auxiliary variables, and high-resolution scene radiance are updated sequentially until convergence, at which point the high-resolution scene radiance is output as the enhanced image. This invention employs the aforementioned nonlocal visual enhancement and resolution improvement method. By incorporating visual enhancement and resolution improvement into the variational scene restoration framework, it uses nonlocal fractional gradient regularization and depth super-resolution prior joint constraints, and utilizes the semi-proximal alternating direction multiplier method for efficient iterative solution. This approach can simultaneously suppress artifacts and color distortion, restore clear details, and significantly improve the overall visual quality of the final image.

The application discloses a method for improving the imaging resolution of a while-drilling azimuthal gamma logging device based on regularization processing, and relates to the technical field of oilfield logging, and comprises the following steps: acquiring azimuthal response sensitivity data of the while-drilling azimuthal gamma logging device; collecting multi-sector gamma imaging data of the azimuthal gamma logging at each depth according to the azimuthal response sensitivity data; and performing regularization processing on the multi-sector gamma imaging data to obtain new gamma data close to the real radioactivity intensity distribution of a formation, so that the imaging resolution of the while-drilling azimuthal gamma logging is improved. The application solves the problems of low imaging resolution and large measurement error of the existing logging.

The invention relates to an ARD construction method based on GF-1 / WFV and HLS, and aims to solve the problem that the existing analysis ready data (ARD) HLS time resolution is difficult to meet high-frequency monitoring and multi-cloud scene application requirements. The method comprises the following steps: fusing GF-1 / WFV data with HLS data based on a deep learning conditional generative adversarial network (cGAN); carrying out atmospheric correction on the GF-1 / WFV image based on a 6S model, carrying out cloud and shadow masking by adopting an automatic multi-scale feature combination (MFC) technology, carrying out geographical correction by adopting an AROP technology, and carrying out resampling to 30 meters to match the HLS data resolution; bRDF normalization and sensor band-pass adjustment are carried out through cGAN, spectral difference between GF-1 / WFV and HLS data is effectively reduced, and the effect is good through verification of multiple quality evaluation indexes such as SSIM, MBE, RMSD, Sobel filter, histogram and visual comparison and the like. According to the method, the time resolution of the fused and expanded HLSG data set is improved to 1.5 days, the ARD data availability is improved, and the method is suitable for application scenes of high-frequency monitoring and has wide popularization and application values.

The present invention discloses a method for optimizing the display of low-resolution images on a double-layer ink electronic paper display, comprising: determining whether the first image is a low-resolution image in response to the electronic paper display receiving a display screen signal corresponding to a first image; obtaining a display pixel group in response to the first image being a low-resolution image; obtaining a first upper-layer driving voltage and a second lower-layer driving voltage corresponding to the display grayscale value corresponding to the display pixel group based on the display grayscale value; driving native pixels using the first upper-layer driving voltage and the second lower-layer driving voltage of the display pixel group to which they belong, and driving interpolated pixels using the first upper-layer driving voltage of the display pixel group to which they belong and the second lower-layer driving voltage of the adjacent display pixel group. The present invention leverages the inherent characteristics of electronic paper displays to achieve image super-resolution, thereby improving the display quality of the electronic paper display.