24 results about "Spectral compression" patented technology

The invention discloses a farmland field segmentation method and system based on multispectral SAM model and multitask learning guidance, and belongs to the field of field segmentation. The problem that in the prior art, channel number matching is conducted in a simple channel compression or deep convolution mode, and the challenges that the boundaries of cultivated land parcels are complex, diversified and subtle are difficult to effectively deal with is solved. The method comprises the following steps: performing spectrum compression on a multispectral remote sensing image through a U-Net structure fused with a CBAM attention mechanism to generate a three-channel pseudo visible light image; the image is input into an SAM-ViT encoder subjected to LoRA fine tuning, and global semantic features are extracted; a task token sequence is constructed, explicit interaction between the token and the image features is realized through a bidirectional Transform module, and high-resolution fusion features are generated; calculating cross-task attention, and outputting a multi-task mask through inner product operation; and performing post-processing by using a watershed algorithm to generate a field boundary. The method is used in the field of cultivated land identification.

The invention relates to the technical field of remote sensing image processing, in particular to a few-sample hyperspectral remote sensing image classification method and system based on multi-path evolution. The method comprises the steps of generating a spectral attention weight by utilizing spectral compression reconstruction according to acquired multi-scale deep feature representation, and performing three-dimensional convolution evolution on weighted features based on adaptive multi-path feature evolution to obtain information fusion features; performing weighted fusion on the information fusion features of the three paths through a dynamic gating mechanism; and obtaining a hyperspectral image classification result according to the fused features. According to the invention, self-adaptive evolution and cross-path selective fusion of multi-scale features are realized; the deep features subjected to multi-level feature coding are used for final classification reasoning, so that high-precision hyperspectral image classification is realized under the condition of extremely few samples.

The invention discloses a display screen color gamut proofreading method, device and equipment and a storage medium, and relates to the technical field of screen display, and the method comprises the steps: collecting screen spectrum data based on a multispectral camera, and synchronously collecting an environment spectrum; processing the screen spectrum data in real time through spectrum compression coding, and outputting compressed spectrum data; performing spectral feature reconstruction based on the compressed spectral data to obtain a reconstructed spectrum; performing target color gamut mapping processing on the reconstructed spectrum, and outputting a mapped spectrum; and correcting the mapping spectrum based on the environment spectrum to obtain a corrected target spectrum, and converting the corrected target spectrum into a driving signal. According to the invention, the interference of the ambient light on the color gamut correction is accurately offset by synchronously collecting the environmental spectrum for processing, the high precision of the reconstructed spectrum is guaranteed through low-loss spectrum compression reconstruction, and the balance between real-time performance and precision is achieved.

The invention relates to the field of signal frequency detection in microwave photonics, and discloses a broadband instantaneous frequency measurement receiving system based on Brillouin spectrum compression, which utilizes a dual-tone pump optical carrier signal carried on an intensity modulator and an optical filter to generate a single Brillouin pump source regulated and controlled by unknown radio frequency. A stimulated Brillouin structure narrowed by an optical fiber ring cavity and a single-pumping reference source subjected to phase shift beat frequency, a signal in a 45GHz range can be compressed to a bandwidth of 2.5 MHz, and meanwhile, an original signal can be subjected to optical domain down-conversion processing by a reference source signal, so that a low-frequency signal easy to process electronically is obtained; besides, a double-fiber annular cavity design is adopted, the line width of the Brillouin resonant laser is further compressed through mode matching of interference of light waves in the cavity, test precision reduction caused by frequency spectrum compression is made up, the resolution ratio can reach + / -2.2 MHz, and finally the Brillouin broadband instantaneous frequency measurement receiving technology is achieved.

Apparatuses and methods are disclosed for encoding and decoding mesh data. Encoding techniques are disclosed including coding a mesh into a bitstream. The coding includes generating a base mesh from the mesh, obtaining connectivity and geometry data of the base mesh. Then, subdividing the base mesh, obtaining connectivity and geometry data of the subdivided mesh. Coding proceeds by generating GFT coefficients, based on a Graph Fourier Transform (GFT), using displacement data, and then, coding into the bitstream the coefficients and connectivity data of the base mesh. Decoding techniques are disclosed including decoding the mesh from the bitstream. The decoding includes decoding from the bitstream connectivity data of the base mesh and subdividing the base mesh, obtaining connectivity data of the subdivided mesh. Decoding proceeds by decoding from the bitstream the GFT coefficients and reconstructing the mesh based on the decoded connectivity data of the subdivided mesh and the decoded coefficients.

The invention discloses a double-path spatial-spectral domain joint compressed sensing imaging method and device, and belongs to the field of high-resolution spectral imaging and compressed imaging. Target light is divided into two paths through the beam splitter prism, and one path is imaged to the high-spatial-resolution detector through the imaging lens. And the other path enters a spatial spectrum compression measurement light path. A spatial-spectral joint coding matrix is generated for a spatial-spectral compression measurement light path, a DMD is introduced into the light path to serve as a spatial light modulator to load the joint coding matrix to carry out high-resolution spatial-spectral joint coding on a target scene, and then a prism is used as a dispersion element to carry out dispersion on the coding scene. And finally, carrying out space and spectrum down-sampling measurement on the coded information of dispersion through an imaging lens by using a low-spatial-resolution detector to obtain a low-spatial-resolution two-dimensional spectrum aliasing measurement value. An original high-space and high-spectral-resolution image can be reconstructed through a back-end reconstruction algorithm on the collected high-spatial-resolution gray-scale image and the low-spatial-resolution spectral aliasing image.

Systems and methods for generating spectrally compressed photons from broadband emitted photon pulses. The system can include an input coupling having a first coupling ratio and an output coupling having a second coupling ratio. The first coupling ratio is configurable and the second coupling ratio is fixed. A cavity can be connected to the input coupling and the output coupling. A controller in electrical communication with the coupling device can be configured to change the first coupling ratio of the input coupling.

This invention discloses a hyperspectral image classification method and related apparatus based on a dual-branch Mamba-Former network. First, the hyperspectral image data to be classified is input. Then, spectral compression and local spatial feature extraction are performed using the SS-ResNet module, and classification tokens are added to generate a token sequence. The token sequence is then input into the parallel encoding branch, MAPE Block, for feature encoding. Further, the classification tokens from the parallel encoding branch are extracted and deeply fused using Fusion-MLP to obtain the final discriminative feature representation. Finally, the fused features are input into a classifier to predict the land cover categories in the hyperspectral image. This invention effectively integrates the local inductive bias of CNNs, the global alignment of the global context branch MHSA, and the linear, efficient long-range modeling capabilities of Mamba, improving the accuracy of deep learning applied to hyperspectral image classification.

The invention discloses an interpolation filter design method based on fuel measurement signal multi-phase decomposition, and belongs to the technical field of filter digital signal processing. According to the method, the cut-off frequency and transition band characteristics of an interpolation filter are optimized through the multi-sampling-rate conversion theory and the multi-phase decomposition technology, and filtering parameters are dynamically adjusted in combination with an adaptive algorithm; the high-precision reconstruction of a fuel measurement signal is realized; the core is that a rear digital low-pass filter is designed, a frequency spectrum compression and expansion technology is used for inhibiting an aliasing effect, and calculation efficiency is improved through a multiphase decomposition structure; the method is especially suitable for an aviation fuel measurement system, can balance data redundancy and signal fidelity under different sampling rates, and significantly improves the measurement precision and the system stability.

A signal transmission method, apparatus, device, and storage medium based on spectrum compression, relating to the field of optical communication technology, includes performing partial response filtering and low-pass filtering on a target transmitted signal according to the principle of maximizing the main lobe energy within a preset target bandwidth range, thereby achieving spectrum shaping of the signal and outputting a target compressed signal; downsampling the target compressed signal; and then photoelectrically modulating the downsampled signal before transmitting it to the receiving end. This application effectively reduces the signal bandwidth, enabling low baud rate and low bandwidth transmission of signals under ultra-high baud rate conditions.

The application discloses a large-oblique TOPS mode ground plane BP self-focusing method based on improved spectrum compression, and comprises the following steps: representing a BP image in a range frequency domain, obtaining a spectrum compression function by using a stationary point principle; obtaining a spectrum support region center of a certain point in a scene based on the spectrum compression function, and replacing radar positions of each time of the spectrum support region center of the certain point in the scene with a beam rotation center to obtain a new spectrum support region center; obtaining a final spectrum compression function of a TOPS mode based on a beam rotation center of a frequency domain mode introduced by the new spectrum support region center; converting the BP image to an azimuth frequency domain based on the final spectrum compression function of the TOPS mode to obtain an error function, and obtaining a SAR image of the TOPS mode finally completing self-focusing and sidelobe suppression based on the error function and an azimuth window function. The application greatly reduces the operation complexity of a BP algorithm processing TOPS mode data.

The invention discloses a fuel measurement-oriented time sequence signal interpolation filter design method, and belongs to the technical field of digital signal processing. The method is based on a multi-sampling-rate conversion theory, the cut-off frequency and transition band characteristics of an interpolation filter are optimized, filtering parameters are dynamically adjusted in combination with an adaptive algorithm, and high-precision reconstruction of a fuel measurement signal is achieved. The method is characterized in that a rear digital low-pass filter is designed, the aliasing effect is effectively inhibited through a frequency spectrum compression and expansion technology, and the calculation efficiency is improved by utilizing a direct implementation structure. The method is particularly suitable for an aviation fuel measuring system, data redundancy and signal fidelity can be balanced in different sampling rate states, and the measuring precision and stability of the system are remarkably improved.

The application discloses a kind of training-free snapshot photometric stereo vision methods based on spectral compression imaging, comprising: (1) setting multiple different wavelength monochromatic light sources to simultaneously irradiate object from different directions, form high-dimensional spectral-angle data cube;(2) the spectral-angle data cube is modulated in space by coded aperture, along the spectrum dimension is sheared translation by using optical dispersion element, finally integrates on single-channel image sensor, generates single two-dimensional gray compressed measurement image;(3) inverse problem model is constructed, and multiple illumination image sequence is decoupled and recovered from single two-dimensional gray compressed measurement image using iterative optimization algorithm based on physical drive;(4) the surface normal map of the measured object is predicted by inputting multiple illumination image sequence into pre-trained photometric stereo vision network module.The application can realize low-cost, high data efficiency and high physical fidelity three-dimensional reconstruction by single snapshot.

The application discloses a kind of space spectral domain joint compression sensing method for infrared spectrum image, belong to high resolution spectral imaging and compression imaging field.The method for realizing the application is: generating space spectral domain joint compression imaging encoding matrix, this matrix is used as encoding template and is loaded to DMD, so that it is modulated to target light information in space, using diffraction grating to modulate spectral image after spatial modulation, using low-resolution infrared camera to modulate the image after space and spectrum downsampling, obtain low-resolution two-dimensional aliasing image.Through space compression imaging and spectral compression imaging, the limitation of infrared camera spatial resolution and spectral resolution is broken through, and the low spatial low spectral resolution infrared image collected can restore the original high spatial high spectral resolution infrared image using reconstruction algorithm.The application utilizes the spatial, spectral correlation of spectral image, and compresses data before data acquisition by compression imaging method, which significantly reduces the data amount of acquisition, storage and transmission.

This invention provides an optimal scene coordinate system establishment method for rapid temporal imaging using medium- and high-orbit SAR, relating to the field of radar imaging technology. Specifically, this invention first proposes four methods for establishing the scene coordinate system under large squint mode. By analyzing the characteristics of spectral compression performance and spectral tilt distribution under different coordinate systems, a maximizing scene coordinate system suitable for large-scene imaging is derived. This coordinate system maximizes the mapping bandwidth of a single processing operation, thereby improving imaging efficiency. Furthermore, this invention provides a method for selecting the scene coordinate system under different imaging conditions. By determining whether the scene is regular and whether the scene area is smaller than a preset size, a suitable scene coordinate system is selected to meet the imaging requirements of medium- and high-orbit SAR under large squint mode and reduce the computation of redundant data.

The application discloses a spectrum compression display method based on FPGA real-time feature extraction and two-stage clustering, relates to the technical field of long-time wideband radio frequency spectrum compression display, and comprises an FPGA real-time preprocessing module, an offline feature storage module, a rear-end adaptive clustering module and a visual rendering module. The FPGA real-time preprocessing module accesses wideband IQ data output by a high-speed analog-to-digital converter, carries out a sliding window fast Fourier transform according to a preset time step, extracts multi-dimensional single-frame spectrum features, calls a configurable weighted incremental pre-clustering algorithm to process the features, automatically merges clusters with the highest similarity when the number of clusters exceeds the upper limit of hardware storage, and outputs the features and unique identification of the clustering clusters. The application can adapt to the feature priority requirements of different scenes, has stable pre-clustering accuracy, has high data compression efficiency, greatly reduces the storage and operation resource consumption of long-time spectrum monitoring, and can meet the real-time response requirements of user interactive zoom viewing.

This invention relates to the field of millimeter-wave near-field imaging technology, and particularly to a handheld SAR three-dimensional fast near-field imaging method based on local spectral compression, comprising: acquiring raw echo data, dividing the complete synthetic array into 2 M‑1 1. Calculate a first-order subarray; for each first-order subarray, compute a uniform sampling grid with a uniform sampling rate in the (u,v,n) coordinate system, and convert it into a first-order non-uniform sampling grid in the (x,y,z) coordinate system; then, in the first-order non-uniform sampling grid, pair 2... M‑1 Reconstructing the first-order subarrays yields 2 M‑1 One first-level sub-image; for 2 M‑1 Each first-level sub-image is coherently superimposed pairwise to obtain at least two second-level sub-images. It is then determined whether these sub-images meet preset conditions. If not, the pairwise coherent superposition process is iteratively executed until the preset conditions are met, yielding the 3D reconstruction result. This solves the problems of low imaging quality and excessive computational complexity inherent in traditional imaging methods.

The application discloses a GCBP imaging method and system of a non-normal side array multichannel SAR of an ultrahigh-speed diving platform, and the method comprises the following steps: constructing a non-normal side array multichannel SAR geometric model; performing pulse compression and sub-aperture division on echo signal data; constructing a multichannel steering matrix and calculating a weight vector of reconstructed sub-aperture echo signal; constructing a sidelobe suppression window function; traversing all grid points in an imaging area, substituting the weight vector and the sidelobe suppression window function into integral operation of BP imaging, performing BP imaging processing on echo signal in the sub-aperture, and sequentially performing two-stage spectrum compression, azimuth up-sampling and spectrum decompression processing on the sub-aperture multichannel BP image after coherent accumulation; and traversing all sub-apertures, coherently fusing all sub-aperture BP images, and obtaining a non-lattice lobe high-resolution SAR image; and the multichannel steering matrix and the sidelobe suppression window function are fused in the sub-aperture BP integral operator, so that ghosting can be directly eliminated in the BP imaging process, and the anti-fuzzy performance of the SAR image is improved.

The present application relates to the technical field of remote sensing image processing, in particular to a multi-path evolution-based few-sample hyperspectral remote sensing image classification method and system. The method comprises generating spectral attention weights by using spectral compression reconstruction according to the obtained multi-scale deep feature representation, performing three-dimensional convolution evolution on the weighted features based on adaptive multi-path feature evolution to obtain information fusion features; the information fusion features of the three paths are weighted and fused through a dynamic gating mechanism; and the classification result of the hyperspectral image is obtained according to the fused features. The present application realizes adaptive evolution and cross-path selective fusion of multi-scale features; the deep features coded by the above multi-level features are used for final classification reasoning, so that high-precision hyperspectral image classification is realized under the condition of few samples.

The invention discloses a three-dimensional imaging method and device, a program product and a storage medium, and the method comprises the steps: dividing sub-apertures with equivalent phase centers at the same position in a multiple-input-multiple-output (MIMO) array into the same sub-aperture group, and obtaining a plurality of sub-aperture groups, the sub-aperture is obtained by dividing a receiving antenna and a transmitting antenna in the MIMO array in advance, and the equivalent phase center is the center of a center connecting line of each antenna in the sub-aperture; performing frequency spectrum compression on the plurality of sub-aperture groups based on the echo data acquired by the plurality of sub-aperture groups in the transmitting and receiving period, and determining a plurality of target sub-images corresponding to the plurality of sub-aperture groups; and coherently superposing the plurality of target sub-images to obtain a target three-dimensional image. According to the invention, the problem of low imaging efficiency in the prior art is solved, and the effect of efficient imaging is achieved.

To provide a fiber amplifier system with high power and narrow linewidth.SOLUTION: A fiber amplifier system 10 includes an optical source providing an optical seed beam, and an FM electro-optic modulator (EOM) 20 that frequency modulates the seed beam to broaden its spectral linewidth. The system also includes an AM EOM 24 that amplitude-modulates the seed beam to provide an amplitude modulated seed beam that is synchronous with the frequency modulated seed beam. The system also includes a nonlinear fiber amplifier 28 that receives the seed beam that is AM-modulated and FM-modulated, the amplitude modulated seed beam causes self-phase modulation in the fiber amplifier that phase-modulates the seed beam as it is being amplified by the fiber amplifier that acts to cancel the spectral linewidth broadening caused by the frequency modulation.SELECTED DRAWING: Figure 1

This invention discloses a multispectral super-resolution imaging device based on compressed sensing, belonging to the field of optical microscopy. The device comprises a light source and beam expansion system, a polarization modulation and structured illumination microscopy system, an coded spectral compression imaging system, a synchronization control system, and a data processing and reconstruction system. This invention solves the problem of the trade-off between increasing the number of spectral channels and maintaining imaging speed in existing spectral-resolution structured light super-resolution microscopy. By combining compressed sensing with single-snapshot spectral coding and dispersive imaging, structured illumination, and a two-step depth unfolding network reconstruction algorithm based on a physical model, this invention can rapidly and synchronously reconstruct super-resolution images of multiple spectral channels with only a small number of compressed images acquired, significantly improving the spectral resolution of imaging without sacrificing the imaging frame rate.

The invention discloses a lightweight speech enhancement method based on a grouped dual-path LSTM (Long Short Term Memory). The lightweight speech enhancement method comprises the following steps: downloading and preprocessing a VoiceBank + DEMAND data set required by a model; performing short-time Fourier transform on the noisy voice to convert the noisy voice into a frequency domain; compressing the high-frequency spectrum to an equivalent rectangular bandwidth (ERB) sub-band space by using a frequency band compression module; the compressed spectrum features are input into an encoder to extract high-order time-frequency features, in-depth modeling is carried out on contextual information in time and frequency dimensions through a grouping double-path long and short term memory module, and then the features are restored through a decoder; reconstructing an original spectrum resolution by means of a frequency band recovery operation; converting a result from a frequency domain to a time domain through short-time inverse Fourier transform, and reconstructing a voice waveform; constructing a joint loss function; and the result is converted from the frequency domain to the time domain through short-time inverse Fourier transform (iSTFT), the voice waveform is reconstructed, and the performance of the proposed model is evaluated. Through spectrum compression and a grouping parallel modeling strategy, while the voice perception quality, the voice definition and the background noise suppression capability are improved, the model parameter quantity and the calculation complexity are remarkably reduced.

The application discloses a snapshot spectral compression imaging method based on L1 norm and low rank technology, studies data fidelity and regularization in hyperspectral image reconstruction, models noise, and uses L1 norm to measure fidelity of the recovered hyperspectral image; a plug-and-play deep low rank prior is designed based on deep learning as a regularization term, the designed deep low rank prior is expressed as designing two neural networks to generate two matrices representing low rank characteristics of the hyperspectral image, random Gaussian noise is inputted to train the two networks in a self-supervised manner; the fidelity term with L1 norm and the regularization term with the deep low rank prior are combined as a hyperspectral image reconstruction optimization algorithm, and the hyperspectral image reconstruction optimization algorithm is solved based on a multiplier alternating direction method to obtain a target hyperspectral image. In the application of hyperspectral image reconstruction, the application has significant advantages, and higher fidelity and better imaging quality can be realized.