223 results about "Spectral reconstruction" patented technology

The invention discloses an image deblurring method based on a rotation perception multi-dimensional attention and blurring sensitivity self-adaptive distribution mechanism, aiming at the defects of the prior art in the aspects of complex blurring processing and image reality sense improvement, and belongs to the technical field of image processing, and the method comprises the following steps: preprocessing a collected picture to obtain a training set; training an image deblurring model by using the training set; and performing image deblurring by using the trained image deblurring model. According to the invention, by designing a plurality of innovative modules and combining fuzzy degree adaptive selection, kernel estimation and dynamic weight distribution driven by a physical model, a multi-dimensional rotation perception attention mechanism, spectrum reconstruction anti-noise deblurring and detail enhancement of perception loss optimization, the image deblurring effect and processing efficiency are effectively improved; the method is especially suitable for processing blurred images with high complexity and high resolution.

A pharmaceutical hyperspectral reconstruction method based on a coded aperture snapshot spectral imaging (CASSI) system includes: collecting and processing original pharmaceutical hyperspectral images to obtain augmented pharmaceutical hyperspectral images; performing simulated spatial encoding on the augmented pharmaceutical hyperspectral images to obtain encoded measurement images; performing spectral inverse shift on the encoded measurement images, then performing inverse encoding to obtain inversely encoded three-dimensional hyperspectral images, using the augmented pharmaceutical hyperspectral images as target images, and constructing a training set and a testing set according to the inversely encoded three-dimensional hyperspectral images and the target images; constructing a deep symmetric neural reconstruction network, and training and testing the deep symmetric neural reconstruction network; and deploying a tested deep symmetric neural reconstruction network onto the CASSI system, real-time collecting pharmaceutical measurement images using the snapshot coded imaging system, and performing computational reconstruction on the pharmaceutical measurement images to obtain reconstructed three-dimensional hyperspectral images.

The invention discloses an abnormity monitoring method and system for a digital pressure transmitter, and relates to the technical field of transmitter monitoring, and the method comprises the following steps: obtaining an original pressure response sequence in a high-frequency pressure pulsation environment, and synchronously recording a sampling clock pulse sequence in an acquisition process, establishing double-track mapping based on the original pressure response sequence and the sampling clock pulse sequence, and forming an input condition with a unified time baseline; and performing instantaneous phase difference extraction on the double-track mapping on the unified time base line. Through double-track mapping, phase difference extraction and nonlinear cross spectrum reconstruction, the stroboscopic aliasing risk is quantified and recognition in advance is realized, and the sensitivity and accuracy of abnormal monitoring of the digital pressure transmitter are improved; meanwhile, the multi-scale risk amplification and reversible time-frequency rearrangement technology is combined, covered high-frequency components are separated and recovered, a real pressure pulsation curve is output after dynamic correction, continuous and stable monitoring is guaranteed, and pressure measurement reliability and operation safety are improved.

A regional soil moisture monitoring method based on airborne spectral reconstruction optical satellite remote sensing, the method comprising: acquiring satellite remote sensing data of a region to be monitored, and collecting unmanned aerial vehicle remote sensing data of a local region in the region to be monitored; on the basis of the unmanned aerial vehicle remote sensing data of the local region, performing spectral reconstruction on the satellite remote sensing data of the region to be monitored, so as to obtain reconstructed satellite spectral data of the satellite remote sensing data; and inputting the reconstructed satellite spectral data into a pre-trained soil moisture inversion model, so as to obtain the soil moisture content of the region to be monitored. Therefore, soil moisture is accurately and quickly monitored.

The invention provides a hyperspectral target detection method based on generative self-supervised learning and wavelet transform. The hyperspectral target detection method comprises the following implementation steps: acquiring a pre-training / testing sample set and a fine tuning sample set; constructing a spatial-spectral reconstruction model based on wavelet transformation and performing generative self-supervised pre-training on the spatial-spectral reconstruction model; constructing a priori constrained hyperspectral target detection network model and carrying out fine tuning training on the model; and obtaining a hyperspectral target prediction result and a detection result. In the pre-training process, the multi-scale spatial and spectral features can be effectively extracted and fused by combining a dual-branch information embedding module of wavelet transform, so that an encoder can learn feature representation from multi-scale spatial-spectral information; and the hyperspectral target detection network model is finely adjusted by using a priori constrained cross entropy loss function, and nonlinear transformation is performed on a prediction result, so that background information can be suppressed by fully utilizing a priori target spectrum, and the hyperspectral target detection precision is improved.

The invention provides a spectrograph based on a fast adjustable filter, the spectrograph comprises the fast adjustable filter, a photoelectric detector, a data acquisition card and a spectrum reconstruction module which are connected in sequence, the fast adjustable filter successively filters spectral components of different wavelengths in the working bandwidth of the spectrograph in sequence, and after each filtering, the spectral components of different wavelengths in the working bandwidth of the spectrograph are obtained. The photoelectric detector detects an optical signal with corresponding light intensity and converts the optical signal into an electric signal, and when the wavelength of the filtered spectral component is within the wavelength range of the to-be-detected light, the light intensity of the optical signal and the current of the electric signal are reduced; the data acquisition card performs current acquisition on each electric signal in sequence to obtain a to-be-measured current vector; the spectrum reconstruction module reconstructs the light spectrum to be measured according to the collected current vector to be measured and a pre-stored spectrum transmission matrix. The spectrograph is high in detection speed, small in size and simple in detection method.

The invention provides a water body extraction method and device based on multi-source and multi-scale optical remote sensing data, and relates to the field of remote sensing data processing. The method comprises the following steps: extracting water body features in a remote sensing image by using a pre-training module to generate a water body feature map; inputting the water body feature map into a backbone network, obtaining multi-source remote sensing data of a high-resolution satellite except a panchromatic wave band, and inputting the multi-source remote sensing data into a spectrum reconstruction module in stages according to different spatial resolutions; the spectrum reconstruction module processes the water body feature map and the multi-source remote sensing data according to different spatial resolutions, and extracts spectrum feature information under each spatial resolution; and fusing the spectral features under the plurality of spatial resolutions by using a fusion attention convolution module to obtain a water body region image in the remote sensing image. According to the method, the problems that the data source is single and the spectral feature information of the water body cannot be fully utilized in the water body extraction process are solved, and the influence caused by manual labeling errors is corrected.

The invention discloses a saturated spectrum processing method for wide-range detection of an absorption spectrum of a tunable diode, and relates to the technical field of spectrum gas detection. According to the saturated spectrum processing method for wide-range detection of the absorption spectrum of the tunable diode, an effective spectrum signal is obtained by performing denoising and baseline correction on an original absorption spectrum signal, a plurality of spectrum characteristic parameters are extracted to calculate a saturated quantized value, and a complete absorption spectrum line contour is reconstructed by adopting a spectrum reconstruction algorithm according to the quantized value, so that the wide-range detection of the absorption spectrum of the tunable diode is realized. The method comprises the following steps of: constructing a saturation quantitative evaluation system, a layered self-adaptive spectrum reconstruction strategy, a dual-mode detection fusion mechanism and a working condition compensation model, calculating weights of an absorption spectrum and a wavelength modulation spectrum through an S-type function, correcting a preliminary concentration value by combining environment temperature, pressure and a compensation model, and outputting an accurate final gas concentration. Without additional hardware, wide-range high-precision gas detection is realized, and environmental interference and saturation influence are eliminated.

The invention belongs to the field of reconfigurable on-chip spectrometers, and particularly discloses a reconfigurable on-chip spectrometer based on a low-loss GST superlattice phase change material. According to the invention, the GST-SL2 material is combined with the double-cascade micro-ring array architecture, so that the performance of the device is improved in a breakthrough manner, a large amount of spectral response can be generated only through a limited number of micro-ring resonators on a chip, the flexible regulation and control of the spectral resolution and the working bandwidth are supported, and the customization requirements of different application scenes are met; a resolution regulation and control array is formed by cascading # imgabs0 first MRRs which are arranged at equal intervals in the extension direction of a bus waveguide, the lengths of resonance micro-rings of the first MRRs are increased progressively, and due to the cascading design, through the synergistic effect of the multiple MRRs, the overall regulation and control capacity is remarkably enhanced, and it is ensured that sampling points can uniformly cover the whole working bandwidth of an on-chip spectrometer; the measurement bandwidth regulation and control array divides the working bandwidth into a plurality of channels, the irrelevance of a system response function is successfully enhanced, and a reliable basis is provided for spectrum reconstruction.

The invention relates to the technical field of spectral imaging, in particular to a high-resolution snapshot hyperspectral imaging system and method based on deep learning. The system comprises a coding receiving module and an algorithm decoding reconstruction module. The coding receiving module comprises an imaging unit, a broadband multispectral filtering array and a detector; a target spectrum cube is imaged on the surface of the broadband multispectral filtering array through the imaging unit, the broadband multispectral filtering array performs spectral response coding on incident light and the incident light is received by the surface of the detector, and efficient coding compression from three-dimensional spectrum information to two-dimensional plane information is realized; the algorithm decoding reconstruction module comprises a spectrum reconstruction network; and combining the spectrum channel coding curve of the broadband multispectral filtering array with the data received by the detector, and inputting the combined data into a spectrum reconstruction network to obtain a high-quality spectrum cube. The method has the advantages that coding and decoding are jointly designed, and the spectral imaging resolution is improved through super-structure surface forward design and neural network fusion of spectral reconstruction.

The invention provides a scene-adaptive tunable calculation hyperspectral imaging system, and provides a hardware-guided spectrum multi-head attention structure module which can introduce partial parameters of a hardware system into a spectrum reconstruction model as priori knowledge and fully utilize optical characteristics of the hardware system to guide a network feature extraction process. Dynamic switching between scanning type data acquisition and snapshot type imaging is realized based on adjustability of LCoS-SLM, hardware coding and software decoding parameters of the system are modified in combination with a scene adaptive mechanism, and the adaptive capacity of the system in a complex scene is improved; that is to say, hardware flexibility and an algorithm are deeply fused, the precision and the dynamic adaptive capacity of spectral imaging are effectively improved, especially the performance in a complex dynamic scene, and a brand new scheme is provided for practical application of the hyperspectral imaging technology.

The invention discloses an inland water area remote sensing reflectivity weak supervision spectrum super-resolution reconstruction method and equipment, and relates to the technical field of remote sensing data processing and remote sensing image spectrum enhancement, and the inland water area remote sensing reflectivity weak supervision spectrum super-resolution reconstruction method mainly comprises the steps: constructing a spectrum super-resolution data set; pre-training a spectrum degradation network and a space degradation network by using a degradation loss function, constructing a spectrum super-resolution network, training the spectrum super-resolution network by using an SA loss function according to the spectrum super-resolution data set, and obtaining a trained spectrum super-resolution network; and carrying out image super-division on the remote sensing image to be subjected to spectral super-division to obtain a super-division high-spectral-resolution image. By implementing the inland water area remote sensing reflectivity weak supervision spectrum super-resolution reconstruction method and equipment provided by the invention, the precision and robustness of spectrum reconstruction in a water body scene can be improved.

The invention discloses a novel on-chip coding calculation type hyperspectral imager, and relates to the technical field of spectral imaging. The system specifically comprises an optical imaging assembly which is used for carrying out space focusing on light of a target scene to form a two-dimensional light field; the spectrum coding module is used for modulating the spectrum information of the two-dimensional light field through a periodic micro-nano structure array to generate a coded light signal; the photoelectric sensor is used for converting the coded optical signal into digital image data; the spectrum reconstruction module is used for decoding the digital image data based on a deep learning algorithm and generating a three-dimensional data cube containing space-spectrum information; wherein the periodic distribution parameter of the micro-nano structure array is matched with the point spread function characteristic of the optical imaging assembly. The invention aims to reduce the manufacturing cost of a spectrum encoder, improve the production efficiency of the spectrum encoder and solve the matching problem of the spectrum encoder and an imaging system.

The invention relates to a pixel registration method for RGB reconstruction of a hyperspectral image and a CNN model optimization method. Through the steps of spectral image acquisition, corresponding RGB image acquisition, establishment of a spectral image data set subjected to manual cutting registration, parameter optimization of a spectral reconstruction model, spatial pixel registration of RGB images and spectrums, visualization of training logs of the reconstruction model and parameters of a test data set and the like, the method optimizes the accuracy of network training; and the quality of the reconstructed spectral image is improved. Firstly, features of an RGB image and a spectral image are extracted through an SIFT algorithm, then spatial registration is carried out on the image of each channel by using an FLANN algorithm to enable pixels of the RGB image and the spectral image to be aligned, and finally end-to-end training, testing and prediction are carried out through a CNN network added with an attention mechanism, so that the spectral image after RGB image reconstruction is obtained. According to the spatial registration technology provided by the method, the training effect of the data set is effectively improved, and the quality of the spectral image reconstructed by RGB is improved by adding an attention mechanism, so that the spectral image is further utilized to carry out spectral analysis.

The invention provides a spectrograph, a spectrum reconstruction method and device and electronic equipment, and relates to the field of spectrum analysis. The spectrograph comprises a chip and a peripheral circuit, the chip comprises a photoelectric response layer, a gate layer and an electrode lead-out layer, and a target semiconductor is arranged in the photoelectric response layer; the photoelectric response layer is used for converting an incident spectrum into a photocurrent signal; the gate layer is used for changing the photoelectric response characteristic of the target semiconductor and covers the upper and lower surfaces of the photoelectric response layer; the electrode lead-out layer is used for outputting the photocurrent signal to external equipment, and the external equipment is used for reconstructing an incident spectrum according to the photocurrent signal and the photoelectric response characteristic of the target semiconductor; the peripheral circuit is used for maintaining the chip to work. According to the invention, the semiconductor with adjustable photoelectric response characteristics is used as the core of the photoelectric response layer, so that the spectrometer has the spectral analysis capability from infrared to terahertz wave bands, does not need to depend on a huge optical assembly and a precise mechanical displacement assembly, and can effectively solve the problems in the prior art.

The invention relates to an aircraft spectral data dynamic acquisition method, which comprises the following steps of: designing working modes of a voltage modulation reconstruction type spectrograph, including a full-wave band spectral acquisition mode, a specific wave band spectral acquisition mode and a standby mode; determining use conditions; when the spectrograph works in a full-band spectrum acquisition mode, setting the voltage of the full-band spectrum as m1 groups of voltages, detecting an incident spectrum to obtain m1 groups of light intensity signals, and performing spectrum reconstruction calculation to obtain all spectrum information; when working in a specific wave band spectrum acquisition mode, setting the voltage of a specific wave band spectrum as m2 groups of voltages, m2 < = m1, detecting an incident spectrum to obtain m2 groups of light intensity signals, and performing spectrum reconstruction and calculation to obtain specific spectrum information; in a standby mode, a single voltage is set, an incident spectrum is detected to obtain a group of light intensity signals, and spectrum reconstruction is not carried out. According to the method, the problems of bandwidth occupation, storage pressure, energy consumption increase and the like caused by whole-process full-amount transmission are solved, and meanwhile, the differentiated requirements of different task stages for specific data are met.

The application discloses a hyperspectral imaging chip, device and system based on an adjustable reconfigurable metasurface, and the chip comprises a metasurface unit array, a metasurface reconfiguration module, a metasurface adjustment module, a CMOS image sensor and a spectrum reconstruction and identification module, the metasurface unit array comprises a plurality of metasurface units arranged on a silicon layer; the metasurface reconfiguration module is used for adjusting the number of metasurface units included in the reconfigurable metasurface unit array; the metasurface adjustment module is used for connecting the silicon layer of the metasurface unit array through wires, and is used for changing the spectral response of the metasurface unit by adjusting the voltage of the wires; the CMOS image sensor is used for detecting light signals from the reconfigurable metasurface unit array after modulation; and the spectrum reconstruction and identification module is used for performing hyperspectral reconstruction and imaging according to the light signals detected by the CMOS image sensor. The application has adjustable spectral response, ultrahigh spectral resolution, real-time dynamic monitoring capability and self-adaptive imaging capability.

The invention discloses a method for synchronously realizing medium-wave infrared spectral imaging metasurface design and spectral image reconstruction. The method comprises the following steps: firstly, establishing a'structure parameter-spectral response 'database of a metasurface unit through simulation software; a forward prediction network is then constructed to predict its spectral response curve from the structural parameters. On the basis, a plurality of metasurface units with differentiated spectral characteristics are arranged on a two-dimensional plane in a spatial-spectral coding mode, and an input hyperspectral image is coded through convolution operation to generate a measurement image. And the target hyperspectral data is recovered from the measurement map in cooperation with a spatial-spectral decoding network. According to the method, metasurface structure design and reconstruction network training are integrated, structure parameters and decoding network weights are updated at the same time through back propagation, collaborative optimization of hardware and software is achieved, the spectrum reconstruction precision can be remarkably improved, the design period can be shortened, and the method is suitable for large-scale popularization and application. The method has a wide application prospect in the fields of spectral imaging, spectral detection, identification and the like of a middle-infrared band.

The invention discloses a computing spectrum reconstruction method based on CS-Unet and a spectral imaging system. The calculation spectrum reconstruction method comprises two parts, namely TwIST pre-reconstruction and Unet coupling reconstruction, wherein the TwIST pre-reconstruction is used for preprocessing an initial coding spectrum image to generate a pre-reconstructed spectrum image; and the Unet part couples the pre-reconstructed image and the initial coding spectral image in the spectral dimension and inputs the coupled image and the initial coding spectral image into a network for fine reconstruction, and finally a target space-spectral image is recovered. The imaging system is composed of a spectral image coding module and a CS-Unet reconstruction module, a metasurface spectral modulator applied to the spectral image coding module is composed of 36 silicon-based all-dielectric structures, and rich spectral response is achieved by adjusting the period, the size and the direction angle of three basic C4 symmetric units, namely a round unit, a cross unit and a square unit. According to the method, high-robustness and high-imaging-quality spectrum reconstruction is realized in a visible light wave band (400-700 nm), and the method has relatively high engineering applicability.

A spectral sensing device and associated methods for enhanced diagnostic capabilities across medical fields are disclosed. The device incorporates multispectral, hyperspectral, and photoplethysmography technologies to analyze biological features including tongue, oral cavity, saliva, teeth, and other tissues. The system transforms any standard RGB camera into a multispectral imaging device by training machine learning models to associate RGB values with spectral values inside and outside the visible spectrum using measurements from a single pixel detector spectroradiometer. A multimodal neural network with separate processing branches analyzes spectral images, non-spectral images, and tabular data to predict microbiome composition. The system generates health risk assessments by comparing predicted microbial abundances to thresholds associated with various medical conditions. Near-infrared sensing enables blood flow monitoring, subcutaneous imaging, and assessment of physiological parameters. Convolutional neural networks enable spectral reconstruction without requiring the physical device, while supporting continuous monitoring in various form factors.

The invention discloses a near infrared spectrum reconstruction method based on stage perception mixed prior and a two-channel optical imaging system, and the method comprises the steps: constructing a deep expansion network model of stage perception mixed prior based on RGB guidance, and the network model comprises k cascade reconstruction stages, each reconstruction stage comprises a degradation perception residual gradient descent module and a near-end mapping module. According to the method, a degradation sensing residual error gradient descent module is introduced, the difference between a sensing matrix and an actual degradation process is effectively reduced through degradation learning, and the iteration process of a deep expansion network is divided into two stages of spectral space preliminary feature extraction and space detail deep extraction by applying a segmented optimization strategy; the network reconstruction precision is guaranteed, the optimization of the calculation efficiency is improved by adjusting the network complexity in stages, and the completeness and quality of the reconstructed image are guaranteed while the calculation overhead is reduced.

The invention relates to the technical field of moisture prediction, in particular to a 3D reconstruction and illumination adaptive coal surface moisture prediction method, which comprises the following steps of: planning an unmanned aerial vehicle route to collect an image, reconstructing coal pile surface space structure information, forming a local triangular grid, dividing an illumination continuous area, establishing a space corresponding relation between the illumination area and a hyperspectral pixel, and predicting the moisture content of a coal pile. And geometric registration and reflection transition correction are executed, depression points and shoulder positions of a reflection curve are detected in the identification interval, moisture absorption characteristics are constructed, a moisture migration link is established in the slope direction, and a coal surface moisture prediction result is obtained. According to the method, a spatial structure and illumination distribution are coupled, a dynamic correction relation is established to unify reflection information, image registration and spectrum reconstruction are combined to strengthen geometric consistency, a moisture migration trend is extracted by utilizing depression point and slope correlation, link and accumulation distribution is formed, and a corrected reflection characteristic represents a spectrum attenuation rule. The spatial continuity and numerical stability of moisture identification are ensured, and the water content grade division is coherent and comparable.

The invention provides a spectrum reconstruction method based on double-branch heterogeneous feature fusion, and relates to the technical field of computational imaging and artificial intelligence, and the method comprises the steps: obtaining a two-dimensional compression measurement image vector, constructing a local-global feature fusion deep neural network model, and constructing a local-global feature fusion deep neural network model; the method comprises a local feature extraction module, a global context extraction module, a feature fusion module and a decoder, a composite loss function is designed, the composite loss function comprises reconstruction loss, physical prior loss and feature regularization loss, end-to-end training is performed on a local-global feature fusion deep neural network model, and the trained model is deployed. And rapid spectrum reconstruction is realized. According to the method, the rapid reasoning capability of the deep neural network is combined with a novel parallel feature extraction and fusion architecture, and local and global information is effectively integrated and constrained in a feature space through a well-designed network model and a composite loss function, so that rapid and high-precision reconstruction of a hyperspectral image is realized.

The invention discloses a calculation spectrometer based on a random multimode waveguide grating and a spectrum reconstruction method. Comprising a silicon substrate layer, a silicon dioxide buried oxide layer, a lithium niobate waveguide layer, a silicon dioxide coating layer and an interdigital electrode which are sequentially stacked from bottom to top, a mode multiplexer, a multi-mode waveguide spiral line and multi-mode waveguide gratings are formed on the lithium niobate waveguide layer, the mode multiplexer is communicated with the multi-mode waveguide spiral line, the multi-mode waveguide spiral line at least comprises a section of multi-mode straight waveguide, a plurality of non-periodically distributed multi-mode waveguide gratings are arranged on the multi-mode straight waveguide, and the multi-mode waveguide gratings are arranged on the multi-mode straight waveguide. And each multimode waveguide grating unit adopts a Bragg grating structure and comprises two groups of grating tooth structures which are respectively positioned on two sides of the multimode straight waveguide. According to the invention, the electrical control logic can be simplified, the disordered resonance of different modes in the spiral line can provide high spectral reconstruction resolution, and meanwhile, the lithium niobate electro-optical modulation can realize rapid and low-power consumption measurement channel scanning.

The invention provides a method and system for identifying sorghum varieties based on spectrum reconstruction and a storage medium, and belongs to the technical field of sorghum variety identification. The method comprises the following steps: acquiring hyperspectral data of a sorghum grain sample and a corresponding pseudo RGB image through a hyperspectral image acquisition system; a mapping relation between pseudo RGB and hyperspectral data is established by using an ASTEM-MST + + model, a spectrum reconstruction model is established, and the ASTEM-MST + + model integrates an adaptive feature enhancement module into a multi-stage spectrum converter network, so that the sorghum spectrum reconstruction quality is improved. A real RGB image of a sample collected by an industrial camera replaces a pseudo RGB image to be input into a reconstruction model, corresponding hyperspectral data is reconstructed, then a region of interest is obtained, spectral data is extracted, variety labels are calibrated and then input into an SSA-RF model for training, a variety identification model is constructed, and the sorghum variety is identified.

The invention discloses a frequency data analysis method and device based on self-supervised learning, and a storage medium. The method comprises the following steps: S1, constructing a standard spectrum tensor; s2, executing frequency change feature extraction operation to generate a frequency change feature map; s3, determining a frequency section of which the hopping intensity is greater than a preset hopping threshold value, constructing a hopping section mask, and generating a structure covering spectrum tensor; s4, inputting to an improved ConvTransform model, and generating a frequency spectrum reconstruction tensor and a residual feature tensor; s5, constructing a frequency spectrum reconstruction loss function and a residual error fitting loss function, executing a self-supervised training process, and constructing a frequency guidance feedback graph; and S6, applying to frequency data analysis, and generating a frequency spectrum reconstruction result, a residual error response result and a hopping section positioning result. According to the invention, the abnormal change identification capability and the frequency spectrum reconstruction precision in the frequency data analysis process are improved.

The invention provides a hyperspectral spectrum data fusion method and a hyperspectral spectrum data fusion system capable of widening a spectral band, and the method comprises the steps: carrying out the wavelength domain matching of Vis-NIR hyperspectral data obtained by a ground object spectrometer based on the visible light band of a hyperspectral image, and storing the resampled Vis-NIR hyperspectral data in a spectrum library. Performing similarity measurement on the spectral vector of each pixel in the hyperspectral image and all standard spectrums in the spectrum library to generate a spectral feature fingerprint map and a spectral response coefficient map; and performing spectrum reconstruction on each pixel in the hyperspectral image data in combination with the spectral feature fingerprint map and the spectral response coefficient map. And finally, obtaining a hyperspectral image cube after broadened spectrum fusion. According to the scheme, on the basis of wavelength domain matching, the spectral feature fingerprint map, the spectral response coefficient map and the like, the hyperspectral image cube obtained through final fusion covers a wider spectral range, and the original high spatial resolution is reserved.

The invention belongs to the technical field of spectrum reconstruction and machine learning, and particularly relates to a mapping relation model between a substance image and a spectrum thereof, a construction method and a substance spectrum reconstruction method. Preparing a plurality of groups of standard solutions with different substance types and different concentrations, and obtaining standard spectrums of the standard solutions by adopting a spectrograph; respectively placing each group of standard solutions in a transparent container, placing a colorimetric card on one side of the container, shooting from the other side to obtain an image containing the standard solutions and the colorimetric card, and constructing an image-spectrum sample pair; and constructing a nonlinear mapping relation model between the image and the spectrum through deep neural network learning. The method comprises the following steps: preparing a solution from an object to be detected, placing the solution in a transparent container, placing a colorimetric card on one side of the container, and taking a picture from the other side to obtain an image containing the solution and the colorimetric card; and obtaining a corresponding spectrum according to the obtained model. According to the method, non-contact and non-destructive spectrum reconstruction of an unknown solution can be finally realized.

The present invention provides a method for implementing a micro spectrometer module based on a metal microcavity array, so as to improve its spectral resolution and reduce the process complexity of the preparation method of the intracavity dielectric layer in the microcavity array; the micro spectrometer module includes a microcavity array with a Fabry-Perot structure having a metal thin film as a reflective layer and a detector array; the method for improving spectral resolution includes a method for obtaining a spectrum by based on preset calibration information of quasi-monochromatic light and spectral reconstruction, and increasing the number of microcavity units in the microcavity array; the preparation method of the intracavity dielectric layer in the microcavity array is to obtain intracavity dielectric layers with a large number of different thicknesses through fewer process steps, so as to implement a microcavity array with a large number of microcavity units.

The application discloses a kind of light field regulation detection method and device based on computing spectral reconstruction, belong to spectral analysis technical field.The method is by introducing medium-metal composite nanostructure, integrates metal nanoisland array and silicon nitride nanocolumn array on quartz substrate, realizes electric field enhancement using the local surface plasmon resonance effect excited by metal nanoisland array, simultaneously realizes wide spectral range phase coding by medium nanocolumn array;Super surface chip is integrated with single point detector, combines pre-calibrated super surface sensing matrix A and enhancement factor matrix E, and reconstructs spectrum using compressed sensing or Tikhonov regularization algorithm.The application keeps the advantages of miniaturization and no moving parts, realizes directional high sensitivity detection to weak signal, and signal-to-noise ratio is improved.