69 results about "Spectral distortion" patented technology

The invention relates to the technical field of component detection, in particular to a dairy product component real-time detection system based on near infrared spectroscopy, which is provided with a feature acquisition module, a preprocessing module, a feature pre-analysis module, a feature recognition module, a feature marking module and a feature judgment module. Whether a near infrared spectrum confidence coefficient abnormal risk exists or not is judged through a feature pre-analysis module, local surface images and near infrared spectrums of all recognition light paths are obtained through a feature recognition module, an incidence relation is established, feature recognition light paths are marked through a feature marking module, light path synchronism analysis is carried out through a feature judgment module, and the accuracy of light path synchronism analysis is improved. And judging whether abnormal early warning is sent out or not. According to the method, the abnormal risk of the confidence coefficient of the near infrared spectrum is identified, spectrum distortion is identified and eliminated according to the actual condition of the dairy product to be detected, and the reliability and accuracy of real-time detection of dairy product components are improved.

The invention relates to the technical field of data synchronization, and particularly discloses a multichannel instrument data synchronization method and system. The method comprises the following steps: acquiring time sequence data and timestamps of each channel of a multi-channel instrument, calculating a data sampling period and a clock drift rate according to the time sequence data and the timestamps so as to quantify time synchronization deviation, extracting a transient energy ratio and a spectrum distortion degree of a time sequence signal, measuring signal stability, performing normalization processing, and combining dynamic drift risk indexes of the time sequence signal, the spectrum distortion degree and the signal stability so as to obtain a time sequence signal. The low-risk data form a synchronization group, the high-risk data are isolated and corrected and then merged into the synchronization group to form synchronization data, and through dynamic risk assessment and grading processing, the influence of single-channel abnormity on overall synchronization is avoided, the clock and signal stability is considered, and the synchronization reliability is improved.

Disclosed herein are scientific instrument support systems, related methods, computing devices and computer-readable media. A method of mitigating distortion of an optical emission spectrum obtained from an optical emission spectrometer is provided. The method may comprise a step of obtaining a spectrum recorded with the spectrometer and a respective one or more condition parameters indicative of an operating condition at a time of recording the spectrum. The method may further comprise a step of providing a model configured to output, in response to the one or more condition parameters, one or more transform parameters of a transformation to be applied to the obtained spectrum. A transformation may be applied in accordance with the obtained one or more transform parameters to the obtained spectrum to mitigate distortion of the spectrum due to a discrepancy between the operating condition and a baseline operating condition.

The invention discloses a light source system capable of continuously adjusting an attenuation ratio, and belongs to the technical field of optical radiation measurement and photoelectric detection. In order to solve the problems of wavelength dependence, discontinuous adjustment, spectrum distortion and the like existing in an existing light intensity adjusting scheme, a combined structure of an integrating sphere and a diaphragm cylinder is adopted, and the device comprises a light source, the integrating sphere with the high-reflection inner wall, a front-end adjustable diaphragm, the diaphragm cylinder with the extinction inner wall and a rear-end adjustable diaphragm. Continuous attenuation of output light intensity in a predetermined dynamic range is realized by adjusting the apertures of the front and rear diaphragms, the lower limit of attenuation ratio meets the requirement, and the dynamic range can reach more than three orders of magnitude. The system uses the integrating sphere to form a stable Lambert light source, the attenuation process depends on geometric parameters, spectral distribution and spatial uniformity basically remain unchanged, and the non-uniformity of a light-emitting surface is not greater than 5%. The invention further provides a calibration method, high-precision attenuation prediction is achieved through detector measurement and view factor model fitting, and the calibration method is suitable for weak cursor calibration, detector linearity testing and other applications.

The invention discloses a method for designing and evaluating a spectral range of a surveying and mapping load wave band, which comprises the following steps of: firstly, transmitting configuration parameters based on radiation under an imaging condition; the entrance pupil radiation characteristic result, the load imaging performance, the image texture characteristic and the spectrum distortion characteristic of the detection wave band designed based on different spectral range parameters at the surveying and mapping load entrance pupil after atmospheric radiation transmission are calculated; and secondly, according to the obtained entrance pupil radiation characteristic result, the imaging performance parameter calculation result, the image texture characteristic parameter calculation result and the spectral distortion characteristic calculation result, evaluating the comprehensive influence of different spectral range parameters on load imaging. According to the invention, the imaging quality and the image information amount of the surveying and mapping load can be evaluated by different spectrum broadening, and the optimal configuration of the spectrum range of the load detection wave band is realized.

The invention discloses online detection equipment for a medicine material, relates to the technical field of medicine material detection, and solves the problem of detection distortion caused by slight inclination of a medicine sample. The equipment comprises an equipment shell, a console and a detection bin, wherein a state detection assembly and an infrared detection assembly are arranged in the detection bin. An integrated circuit board in the console is integrated with a sensing acquisition module, a data processing module and a control module, the data processing module comprises a tiny inclination identification unit, a spectrum offset correction unit and a component data correction unit, and the real component content is calculated after the inclination angle is identified through pressure data and spectrum distortion is corrected. And the control module judges the qualified state of the sample in combination with a preset standard, controls the equipment to operate and sends a regulation instruction to an external production system. According to the equipment, the detection accuracy and the operation convenience are improved, the linkage of detection and production is realized, the online quality control requirement is met, and reliable support is provided for medicine production quality control.

This invention discloses a multi-objective optimization-based sparse unmixing method for hyperspectral images. The method acquires the hyperspectral image to be unmixed and an initial endmember spectral library; constructs a structured sparse control operator based on dynamic temperature decay and adaptive gating; performs adaptive dynamic grouping of endmember topology based on gradient feedback and cosine similarity; constructs a multi-objective optimization model combining reconstruction error and group sparsity error for iterative optimization; and uses the maximum curvature method to obtain the final unmixing result from the Pareto front. Compared with existing technologies, by constructing an adaptive dynamic grouping mechanism for endmember topology, this invention fully utilizes gradient feedback and cosine similarity to capture spectral distortion differences, ensuring that the identification of similar endmembers is directional. Simultaneously, this invention combines a structured sparse control operator and uses a pass-through estimator to effectively filter false endmember features. Through the synergistic effect of these two methods, multi-objective sparse unmixing of hyperspectral images is ultimately achieved, minimizing reconstruction error and optimizing group sparsity error.

The invention discloses a high-precision calibration method of a hyperspectral laser radar for photon counting, which belongs to the technical field of laser radars and comprises a spectrum calibration stage and a radiation calibration stage. Spectral calibration is used for extracting spectral response parameters of each channel based on wavelength-by-wavelength scanning of a monochromator and synchronous measurement of a beam splitter; a detection link is strictly decoupled through radiometric calibration, the effective attenuation coefficient of a water body is extracted in situ through a slope method, full-link radiation correction is carried out in combination with an air whiteboard reference and window transmittance, and the target intrinsic spectral reflectivity without interference is inverted. The method can be used for data correction, target accurate classification and identification and high-fidelity quantitative detection of the hyperspectral laser radar for photon counting, and solves the problem of serious spectral distortion of an original signal caused by non-uniform light source energy of the hyperspectral laser radar and complex scattering absorption of a water body.

The invention relates to the technical field of vision and spectral measurement, and discloses an automobile ornament chromatic aberration detection method based on image recognition, which comprises the following steps: setting a known spectral reference spot on site and collecting multiband imaging response and spectral color measurement data, and constructing closed linear mapping to estimate a pixel-level discrete reflectance spectrum; performing energy normalization on the estimated spectrum, and calculating a spectrum gravity center, a spectrum width, first-order differential energy and second-order differential energy to form a spectrum signature; constructing a self-adaptive threshold value based on a local reference region statistical median and a robust scale, and classifying abnormal pixels by adopting a determination logic of a spectrum angle and a signature standardization distance; the method is suitable for online and offline appearance detection scenes of a production line, and can distinguish the overall color cast and spectral distortion, thereby improving the interpretability and robustness of the whole area array detection.

This invention discloses a remote sensing image fusion method combining ratio transformation and distribution transformation, comprising: performing mean filtering on a panchromatic image and an upsampled multispectral image to obtain high-frequency components of the panchromatic image and the upsampled multispectral image; based on this, obtaining the missing high-frequency details in the multispectral image, denoted as the first high-frequency details; performing standard normalization on the first high-frequency details to obtain the second high-frequency details; calculating the mean and standard deviation of each pixel in each channel of the upsampled multispectral image; concatenating the upsampled multispectral image and the first high-frequency details and inputting them into a convolutional network to generate two affine transformation parameters; injecting the obtained mean and standard deviation into the second high-frequency details to generate high-frequency details with the same distribution as the upsampled multispectral image; and combining this with the upsampled multispectral image to obtain the final fused image. This method solves the problems of spectral distortion and detail distortion in existing remote sensing image fusion algorithms.

This invention discloses a method for verifying and confirming numerical prediction thrust spectrum results of propellers, belonging to the field of computational fluid dynamics (CFD) result verification and confirmation. While maintaining consistency between the physical model and boundary conditions, this invention encrypts the CFD calculation settings with joint discrete parameters and quantitatively characterizes simulation errors and uncertainties based on Richardson extrapolation. This invention employs the Lomb-Scargle periodogram method to directly process non-uniformly sampled data, avoiding spectral distortion caused by resampling interpolation and improving the accuracy of spectrum estimation. This invention automatically selects the optimal AR model order through information criteria, models colored noise backgrounds in actual engineering, and achieves adaptive noise modeling. This invention simultaneously evaluates the statistical significance of the global maximum peak value and each expected harmonic frequency in a single Monte Carlo simulation cycle. This invention uses a block bootstrap method to calculate the frequency band power confidence interval, maintaining the local correlation of the time series and achieving reliable confidence interval quantification.

The invention relates to the technical field of remote sensing image processing, in particular to a hyperspectral and multispectral remote sensing image fusion method, electronic equipment and a storage medium, and the hyperspectral and multispectral remote sensing image fusion method comprises the following steps: grouping a hyperspectral image and a multispectral image to be fused according to a spectral range; wherein the spectral range of the hyperspectral image in each group is matched with the spectral range of the multispectral image; for each group, fusing the hyperspectral image and the multispectral image in the group to obtain a fused image; and overlapping the fusion images corresponding to the groups to obtain a final fusion result. According to the scheme, the hyperspectral image and the multispectral image are grouped according to the spectral range, so that the spectral range of the hyperspectral band and the spectral range of the multispectral band in each group are matched, and the problem of spectral distortion caused by inconsistent spectral ranges is avoided.

The invention provides a hyperspectral image reconstruction method and system based on flow matching prior assistance, and the method comprises the steps: generating a low-dimensional condition vector based on compressed two-dimensional test data, and enabling the low-dimensional condition vector to be used for constraining a flow matching model process, and obtaining a hidden space vector; setting an initial iteration frequency and an initial signal vector, and determining a sensing matrix and measurement data; and based on the initial signal vector, the sensing matrix and the measurement data, performing loop iteration of N stages until a final reconstructed hyperspectral image is obtained, wherein the k-stage iteration specifically comprises two processes of generalized alternating projection and a de-noising device. And after N stages of iteration are completed, taking the hyperspectral image reconstruction estimation value of the Nth stage as a final reconstructed hyperspectral image. According to the method, the continuous probability modeling capability of stream matching is utilized, the spatial-spectral information recovery capability of the hyperspectral image is enhanced, and the problems of spectral distortion and spatial blur in a complex degraded scene in a traditional method are avoided.

Systems and methods for waveform repair. The methods comprise: receiving a signal of interest having a spectral distortion, the signal of interest having a sequence of symbols; generating an estimate for each symbol of a sequence of symbols in the signal of interest; and applying reconstruction filtering to the estimates to correct the spectral distortion.

The invention relates to the technical field of image processing, in particular to a liquid crystal filtering multispectral image generation method based on quantum inspiration. According to the invention, a quantum inspiration optimization structure is combined with liquid crystal filtering hardware, the filtering parameter optimization speed and precision are improved by referring to quantum superposition and parallelism characteristics, and the spectral band dynamic switching is realized by using the electrically-controlled adjustable characteristic of liquid crystal filtering; the problems that a traditional mechanical filtering structure is poor in adjustability and low in integration level and a traditional algorithm is low in high-dimensional data processing efficiency are solved, and high-quality original signals and efficient calculation support are provided for multispectral image generation. According to the method, an image quality analysis and feedback mechanism is constructed, the image quality is evaluated based on parameters such as spectral angular distance and residual noise energy, bad images are fed back to an optimization link to be reprocessed, an optimization frequency threshold value is set to avoid invalid circulation, the quality stability of output images is effectively guaranteed, and the method is suitable for large-scale popularization and application. And the problems of spectrum distortion, noise and difficulty in balancing details are solved.

The invention provides a hyperspectral image super-resolution reconstruction method and device, equipment and a medium, and relates to the technical field of remote sensing image processing. According to the method, the input low-resolution hyperspectral image is acquired and preprocessed, and the full-image semantic object mask is generated by using the pre-trained semantic segmentation model, so that the semantic index map is obtained; then, constructing a hierarchical global spectrum prototype memory bank, and aggregating cross-sample category-level spectrum priori through momentum update; designing a deep reconstruction network formed by a plurality of cascaded semantic prototype interaction units, and performing feature enhancement on the input initial feature map by combining local prototype perception, object pixel cross attention, window self-attention and a feedforward network; and finally, a high-resolution image is output through sub-pixel convolution reconstruction. According to the method, auxiliary images are not needed, spectral distortion and boundary blur are effectively relieved, and reconstruction fidelity and detail representation are improved.

The invention discloses a hyperspectral image compression network and compression method based on multi-scale spectrum and spatial feature enhancement, and mainly solves the problems of insufficient hyperspectral image spectrum modeling, insufficient spatial feature extraction and high network complexity in the prior art. The network comprises a main encoder, a main decoder, a super-prior encoder, a super-prior decoder and an entropy model. The main encoder comprises a spectral attention gating data unit, a convolution unit and a multi-scale spatial adaptive feature attention enhancement unit, and is used for converting an input image into potential representation and removing spatial and spectral redundancy; the main decoder and the main encoder are symmetrical in structure; the super-prior encoder comprises a convolution layer and an activation layer and is used for extracting auxiliary information from the output of the main encoder; the super-prior decoder and the super-prior encoder are symmetrical in structure; the entropy model is Gaussian distribution based on output parameters of a super-prior decoder. After the network is trained, lossy compression of a hyperspectral image can be realized. The method reduces the network complexity and spectral distortion, improves the reconstruction quality of complex ground feature details, and is suitable for earth observation, meteorological monitoring and the like.

The application provides an image fusion method and device and a storage medium, and belongs to the technical field of image fusion.The method comprises the following steps: importing a high-resolution hyperspectral image to be fused, a high-resolution multispectral image to be fused, a plurality of original high-resolution hyperspectral images and an original high-resolution multispectral image; performing down-sampling processing on the original high-resolution hyperspectral images respectively to obtain original low-resolution hyperspectral images; and constructing a training model, wherein the training model comprises a feature analysis network, a target vector field construction network and a predicted vector field construction network.The application effectively avoids the spectral distortion problem caused by the difference in spectral response of a sensor in a traditional fusion method, and overcomes the defects of lack of constraints in the intermediate process and insufficient stability of results in a single-step regression method.

The invention provides an adhesive component quantitative detection method and system based on spectral analysis, and relates to the technical field of analysis and detection.According to the method, near infrared spectral data of an adhesive sample are obtained, and environmental background spectral data are synchronously collected for background correction, so that interference is eliminated. And performing signal enhancement processing on the corrected data, extracting spectral features under different scales, and separating overlapped absorption peaks. In the decomposition process, for the possible layering or interfacial effect of the adhesive, an interfacial feature enhancement treatment is introduced to focus on the absorption features of the internal layered structure. A compensation factor of flow-induced spectral distortion is introduced based on the transmission speed of the production line, and characteristics related to the internal structure are corrected. And combining the corrected characteristics into a set, inputting the set into a quantitative prediction model established through supervised learning in advance, and outputting the concentration value of the adhesive component, so that high-precision quantitative detection of the adhesive component can be realized.

The invention relates to the technical field of culture medium detection, and discloses a fruit fungus culture medium pollution identification method and system based on hyperspectral imaging, and the method comprises the steps: obtaining a data cube of a hyperspectral image of a culture medium, executing non-negative tensor decomposition, and obtaining a group of space factor graphs and corresponding spectral vectors; calculating a Bhattacharyya distance between each spectrum vector and a preset pollution strain spectrum library, and generating a pollution affinity coefficient corresponding to each space factor graph; calculating the mahalanobis distance of each pixel point in the data cube, and generating a global spectral distortion graph; initializing a pollution potential field with the same spatial size as the hyperspectral image, and carrying out iterative updating through a reaction diffusion equation until convergence; and performing threshold segmentation on the converged pollution potential field to obtain a binary mask of the pollution area. According to the method, the analysis of the spectral dimension and the structural constraint of the spatial dimension are deeply fused by constructing the reaction diffusion model, so that the early and accurate identification of the culture medium pollution is realized.

Methods and apparatus (10) for Raman spectral analysis of a sample (12), such as a pharmaceutical dosage form, are disclosed. Delivery optics (16) are used to deliver probe light to a delivery region (13) on the sample, and collection optics (20) are used to collect, from a collection region (17) on the sample spaced from the delivery region, the probe light following scattering through the sample. Each of a plurality of target Raman spectral features are measured in the collected light, and a spectral distortion of the collected light arising during scattering through the sample is determined. A property of the sample is then quantified using the target Raman spectral features in combination with the determined spectral distortion, such that the quantified property is compensated for the spectral distortion.

The invention relates to the technical field of image processing, in particular to an unsupervised generative confrontation remote sensing image spatial-spectral fusion method and system, and the method comprises the steps: inputting a to-be-fused remote sensing image into a trained spatial-spectral fusion model, multi-scale local-global features of an MS image and a PAN image are extracted through a double-flow convolution-Transform generator in a space-spectrum fusion model, prior cross fusion is carried out on the same-scale local-global features of the MS image and the PAN image, then cross-scale fusion is carried out on prior cross fusion results of different scales, a recursive cascade cross fusion structure is formed, and the multi-scale local-global features of the MS image and the PAN image are obtained. Key fusion information is obtained; reconstructing the key fusion information based on the supplementary information to generate injection information, performing nonlinear correction on the up-sampled MS image and the injection information, and generating a high-spatial-resolution multispectral image corresponding to the remote sensing image; the method can improve the spatial resolution of the multispectral remote sensing image and reduce spectral distortion.

This invention relates to the field of nonlinear optics and discloses a method and system for multi-soliton spectral tunneling based on Airy pulse parameter control. The method includes the following steps: acquiring initial parameter data of the Airy pulse and initial state data of the multi-soliton; performing Airy pulse waveform generation processing based on the initial parameter data of the Airy pulse to generate Airy pulse waveform data; constructing a coupled nonlinear Schrödinger equation for accurate simulation to ensure the predictability of nonlinear interactions; and simultaneously placing the Airy pulse and the multi-soliton in a simulation platform to simulate their interaction. The generated spectral tunneling characteristic data can be analyzed in real time to determine whether the spectral tunneling process deviates from the expected energy transfer trajectory, ensuring spectral energy transfer efficiency and tunneling accuracy, and reducing spectral distortion and efficiency loss.

This invention relates to a hyperspectral image demixing method based on a graph wavelet transform convolutional network, belonging to the field of hyperspectral image demixing technology. The method includes: using a Mexican Hat graph wavelet filter to perform frequency domain decomposition on the undirected graph of the original hyperspectral image, obtaining low-frequency and high-frequency components, which are then input into a parallel dual-branch feature encoder network for feature learning, extraction, and adaptive recalibration. Enhanced low-frequency and high-frequency features are obtained and fused element-wise to obtain multi-frequency comprehensive features, which are then input into an endmember constraint and reconstruction module for abundance estimation. This yields a reconstructed hyperspectral image, which is compared with the original hyperspectral image. A multi-dimensional joint loss function is constructed and optimized to obtain a hyperspectral demixing model for hyperspectral image demixing. This method aims to solve the technical problem of existing technologies' inability to effectively distinguish the mixing characteristics of high-frequency boundary regions and low-frequency homogeneous regions, leading to boundary blurring and spectral distortion.

This invention discloses an infrared thermal imaging sensor and an infrared temperature measurement method, belonging to the technical field of infrared thermal imaging sensors. It includes: an infrared optical lens for receiving and focusing infrared radiation from within the field of view; and a multispectral infrared detector array positioned at the focal plane of the infrared optical lens for converting the focused infrared radiation into electrical signals. By integrating a multispectral infrared detector array with an adaptive blackbody and combining it with an internal temperature monitoring module, this invention achieves real-time online dynamic correction of detector non-uniformity response and thermal drift effects, significantly improving the accuracy and long-term stability of temperature measurement. Furthermore, by introducing an online lens contamination monitoring module and an active multispectral excitation unit, this invention can identify and correct spectral distortion caused by lens contamination in real time. Simultaneously, it utilizes active excitation to enhance the constraint on target emissivity, thereby improving the accuracy and reliability of temperature measurement for fixed or slowly varying targets.

The present application relates to the technical field of spectral detection, and discloses a crop canopy light field spectrum data correction method and a nutrient detection device. The method comprises the following steps: collecting crop canopy hyperspectral data, removing the background by using an optimized soil to adjust the vegetation index after radiation correction; constructing a light field heterogeneity parameter model combined with a red edge response factor, determining a red edge modulation coefficient through iterative optimization of a clustering evaluation index; calculating the Euclidean distance from a pixel to the clustering center of strong and weak light to generate a fusion weight coefficient; constructing a differentiated benchmark correction model, and performing linear weighted fusion twice on the spectral data and the nutrient content calculated by the double regression model by using the fusion weight. Through the construction of the light field heterogeneity representation parameter and the soft segmentation strategy based on the distance weight, the smooth transition of the strong and weak light regions is realized, the spectral distortion caused by uneven light is eliminated, and the accuracy and robustness of the crop nutrient detection under complex light field are improved.

The invention relates to a laser communication transmitter system, a communication method, a lunar laser communication ground terminal, a communication system, a computer readable storage medium and a computer program product. The laser communication transmitter system comprises a transmitting unit and a free space light transmitting mirror; a hollow-core optical fiber jumper wire is adopted between the transmitting unit and the free space light transmitting mirror; and an optical signal generated by the transmitting unit is transmitted to the lunar laser communication satellite-borne terminal through a signal uplink via the hollow-core optical fiber patch cord and the free space light transmitting mirror. According to the mode, the laser communication transmitter system based on the hollow-core optical fiber patch cord is adopted, the influence of the nonlinear effect of the optical fiber on optical signals is remarkably reduced, and the quality and the stability of signal transmission are improved. By using the hollow-core optical fiber, spectral distortion caused by self-phase modulation and a four-wave mixing effect is effectively reduced, and the risk that beacon light is buried by noise is reduced, so that the signal detection and demodulation performance of a receiving end is improved.

The present application relates to a hyperspectral image unmixing method based on graph wavelet transform convolution network, belonging to the technical field of hyperspectral image unmixing. The method comprises: using a Mexican Hat graph wavelet filter to perform frequency domain decomposition on the undirected graph of the original hyperspectral image, obtaining low-frequency components and high-frequency components and inputting them into a parallel double-branch feature encoder network for feature learning and extraction and adaptive recalibration, obtaining enhanced low-frequency features and enhanced high-frequency features and performing element-by-element addition fusion, obtaining multi-frequency comprehensive features and inputting them into an endmember constraint and reconstruction module for abundance estimation, thereby obtaining the reconstructed hyperspectral image and comparing it with the original hyperspectral image, and constructing and optimizing a multi-dimensional joint loss function to obtain a hyperspectral unmixing model for hyperspectral image unmixing. The present application aims to solve the technical problem that the existing technology cannot effectively distinguish the mixed characteristics of high-frequency boundary regions and low-frequency homogeneous regions, resulting in blurred boundaries and spectral distortion.

Systems and methods for waveform repair. The methods comprise: receiving a signal of interest having a spectral distortion, the signal of interest having a sequence of symbols; generating an estimate for each symbol of a sequence of symbols in the signal of interest; and applying reconstruction filtering to the estimates to correct the spectral distortion.

The invention provides a thin cloud pollution remote sensing image panchromatic sharpening method based on frequency domain decoupling. The problems of spectrum distortion, detail blurring, cloud residue and the like under thin cloud interference in an existing method are solved. Multispectral and panchromatic images are decomposed into amplitude (reflecting cloud layer intensity) and phase (representing space structure) components through fast Fourier transform, near-infrared amplitude is used as cloud robust prompt to guide cloud removal, and details are enhanced in combination with panchromatic phases. A modal self-adaptive frequency gating module is introduced to dynamically modulate band difference, and the amplitude and the phase are cooperatively constrained through an interactive frequency consistency module, so that the frequency domain spectrum-structure consistency is guaranteed. And finally, reconstructing a high-resolution cloudless multispectral image through spectrum enhancement and inverse Fourier transform. Experiments show that the method is remarkably superior to the prior art in real data, has high precision and robustness in the aspects of spectral fidelity and spatial definition, and is suitable for high-quality remote sensing image reconstruction and ground object observation.