154 results about "Spectral reconstruction" patented technology

The invention belongs to the technical field of spectral imaging and relates to a color digital camera single RGB (red green blue) image based spectral reconstruction method. The method includes: measuring the spectral reflectance of training samples; setting up a shooting environment, shooting the training samples and an RGB image of a reconstruction object and correcting the image; extracting RGB response values of the training samples and reconstruction object pixels, calculating Euclidean distance of the RGB value between any pixel and the training samples, performing ascending sort on the training samples from small to large according to the Euclidean distance, and taking the first p training samples as optimal training sample sets; calculating inverse distance weighting matrixes of the optimal training sample sets; expanding the response values of the optimal training sample sets and the pixels to be reconstructed; weighting response value extension matrixes and spectral reflectance matrixes of the optimal training sample sets by the aid of the inverse distance weighting matrixes; calculating spectral reconstruction transformation matrixes by the aid of a pseudoinverse method, and reconstructing the spectral reflectance of the corresponding pixels to acquire the spectral image of the reconstruction object.

The invention relates to a system for rebuilding spectrum of high spectrum intervention data and a method thereof. Based on the interference imaging mechanism of an HJ-1A high spectrum camera, the system adopts a particular data processing method to rebuild intervention data into spectrum data, which solves the practical engineering problems in processing ground system building data. The system comprises a relative radiation correction unit, an interference data de-noising unit, an apodization processing unit, a spectrum recovering unit and a spectrum checking unit; wherein, the spectrum recovering unit converts an interference data cube into a spectrum image data cube by the symmetrization processing of interference data; the spectrum checking unit carries out wavelength positioning to a real object spectrum image cube by checking the position of the characteristic spectral line of an imaging spectrometer. The technical scheme of the invention fills the blank of data processing of satellite-bone interference imaging spectrometers in China, and satisfies user demand for the data of satellite-bone interference imaging spectrometers.

The invention discloses a spectrum reconstruction method and system based on a multichannel imaging system, which are used for performing spectrum reconstruction according to the camera response values of a training sample to-be-selected set and a to-be-reconstructed sample set acquired by the multichannel imaging system. The method comprises the following steps: selecting training samples with most similar camera response values from the training sample to-be-selected set for each to-be-reconstructed sample in the to-be-reconstructed sample set respectively to obtain an initially-selected sample set; removing identical samples, and constructing a matrix by the spectral reflectivity information of all samples to perform PCA (Principal Component Analysis) analysis; computing the accumulating contribution rate of a characteristic value; screening the samples to obtain a training sample sub-set, performing spectrum reconstruction according to each training sample sub-set for each to-be-reconstructed sample, and selecting one reconstruction result according to a reconstruction error minimum principle for serving as the final spectral reflectivity value of each to-be-reconstructed sample. By adopting the spectrum reconstruction method and system, the chromaticity accuracy and spectrum accuracy of each reconstructed sample can be remarkably increased, and high-fidelity color reproduction can be performed. Meanwhile, the method and the system are easy and convenient to implement, and are suitable for popularization and application.

The invention discloses a hyperspectral image super-resolution reconstruction algorithm based on matrix structure sparse non-negative decomposition. According to the reconstruction algorithm, a low-spatial-resolution hyperspectral image and a high-resolution color image are united to reconstruct a high-resolution hyperspectral image, and the problem that an existing algorithm can not accurately restore the high-resolution hyperspectral image. The method comprises the realizing steps that (1) the low-resolution hyperspectral image and the corresponding high-resolution color image are input; (2) local and non-local self-similarity of the hyperspectral images is utilized for constructing a spectrum reconstruction target function based on the matrix structure sparse non-negative decomposition; (3) an alternating direction multiplier method is adopted for alternative solving to obtain an optimized spectrum material coefficient and a spectrum material base; (4) a matrix of the optimized spectrum material coefficient and a matrix of the optimized spectrum material base are utilized to reconstruct the high-resolution hyperspectral image. According to the method, the restored hyperspectral image is clearer, the image edge is sharper, and the spatial resolution of the hyperspectral image can be effectively increased.

The present invention relates to a super-resolution spectral imaging system and a super-resolution spectral imaging method based on a scattering medium. The super-resolution spectral imaging system comprises a calibration branch A, a scattering medium 4, a lens 5, a detector 6 and a branch B to be detected, wherein the calibration branch A comprises a light source module 1, a first collimation beam expansion system 2 and a pinhole 3, and the branch B to be detected comprises a light source 7 to be detected, a second collimation beam expansion system 8 and a target 9. According to the embodiments of the present invention, the point spread functions of the system are measured when the light source module outputs different wavelengths so as to construct the spectral point spread function (SPSF), the spectral reconstruction is achieved by using the compressed sensing (CS) method while the scattering medium with the appropriate scattering mean free path is matched, and the speckle receivedby the camera is subjected to convolution removing by using the point spread function corresponding to the wavelength of the light source to be detected, such that the maximum super-resolution imagingcan be achieved without the increase of the complexity of the system; and the super-resolution spectral imaging system has advantages of simple structure, easy control, low component cost, strong anti-disturbance capability and wide application field.

The invention discloses an optimal training sample selection method for a broad band spectrum imaging system. The method includes the steps that an actual broad band spectrum imaging system is built, and parameters of the spectrum imaging system are calibrated; a spectrum imaging commonly used sample set is prepared, and spectrum reflectivity of the spectrum imaging commonly used sample set is measured; by means of the actual spectrum imaging system, the sample set is shot, and a digital response value of the sample set is corrected according to the calibration information of the system; the sample set is reconstructed through a pseudo-inverse spectrum reconstruction method, and training samples are selected from the sample set to from a training sample set according to the spectrum error minimization principle; when each training sample is selected to add into the training sample set, all samples in the sample set are traversed once until a spectrum reconstruction error of the training sample set to a main sample set begins to converge to a certain minimum value Jmin, the optimal training sample set is locked, and selection of a board band spectrum imaging optimal training sample is achieved. The method has the technical advantages of remarkably reducing spectrum imaging complexity, improving spectrum construction precision and the like.

A hyper spectral image classification method based on wavelet threshold denoising and empirical mode decomposition relates to a hyper spectral image classification method in a remote sensing field. In the traditional method, the wavelet is not suitable for a non-linear and non-stationarity hyper spectral image, a first intrinsic mode function decomposed by using an empirical mode decomposition method has a loud noise and acquired classification result precision is not high. By using the method of the invention, the above problems can be solved. The method comprises the following steps: step 1. performing two-dimensional wavelet threshold denoising of the hyper spectral image; step 2. carrying out empirical mode decomposition and image reconstruction to the hyper spectral image which is performed with wavelet denoising; step 3. using a SVW classifier to classify the hyper spectral reconstruction image nIMFs so as to obtain the classification precision. The method can be used in the classification of the hyper spectral image.

The invention discloses a fabric image color calibration algorithm based on spectral reflectance reconstruction, relates to the field of fabric image color correction, and solves the problem that thereconstruction precision is affected by image acquisition color distortion in the existing method. The technical scheme includes: acquiring color training samples through collection; constructing a virtual imaging system, and calculating virtual system responses of all color training samples and corresponding spectral reflectance estimated values; performing color space conversion according to a color space conversion formula; adopting a defined spectrum reconstruction error evaluation function to determine a representative color training sample; obtaining a spectral response function of an actual imaging system according to the determined representative color training sample; and obtaining a corresponding reconstructed spectral reflectance through a Wiener estimation method, and obtaininga reconstructed RGB image through color space conversion. According to the fabric image color calibration algorithm based on spectral reflectance reconstruction, the quality of the fabric image is improved, and real yarn color information of the fabric image is obtained.

The invention provides a spectral reconstruction algorithm. The spectral reconstruction algorithm comprises the following steps: (1) acquiring a CIE XYZ value of the CIE color space of a sample to be tested; (2) converting the CIE XYZ value of the sample to be tested into a CIEL*C*H* value according to preset rules; (3) carrying out sorting on a reference spectral value set formed by reference spectral values to obtain a corresponding subarea spectral value set; (4) according to a main-component analysis method, selecting three main components with large contribution rate in a subarea spectral value set as a first group of main components and calculating out a primary spectral value according to a reconstruction formula; (5) selecting subarea spectral values in the subarea spectral value set according to the primary spectral value, and selecting obtained screening spectral values to form a screening spectral value set; and (6) according to the main-component analysis method, selecting three main components with large contribution rate as a second group of main components in the screening spectral value set, and calculating to obtain a tested spectral value.

The invention discloses a thermal infrared hyperspectral emissivity simulation method and a system, and the method comprises the following steps: extracting ground object feature parameters of all picture elements in TASI (thermal airborne spectrographic imager) multi-spectral emissivity original image data by utilizing image ground object continuous spectral data, further constructing a reconstruction and transformation matrix by utilizing the ground object feature parameters, performing spectral reconstruction and simulating and getting thermal infrared hyperspectral information. With the adoption of the method and the system, the continuous thermal infrared hyperspectral information with higher spectral resolution can be simulated and obtained from the thermal infrared TASI multi-spectral emissivity data and other thermal infrared multi-spectral satellite remote sensing data, thereby getting the thermal infrared data with the high spectral resolution while meeting high spatial resolution and high signal-to-noise ratio of the original multi-spectral remote sensing data.

The invention discloses a multichannel synthetic aperture radar imaging method based on a complete complementary sequence. The multichannel synthetic aperture radar imaging method includes following steps: 1, dividing echo data of various channels based on the complete complementary sequence into echo data which are obtained by means of utilizing two complementary sequences as radar transmitting signals independently; 2, performing azimuth Fourier transformation for the separated echo data; 3, performing azimuth spectral reconstruction; 4, performing range compressing; 5, correcting range migratory motion; and 6, performing azimuth compressing to obtain a final image. The multichannel synthetic aperture radar imaging method has the advantages that radar signal waveforms are easy to be generated, pulse compressing is easy to be realized, azimuth ultralow sidelobe is realized, azimuth resolution, image quality and orientation resolution are high, and an observation band is wide.

A spectral correction method of imaging spectrum reflectivity data comprises the following steps: 1, reading in the spectrum data; 2, normalizing a reflection spectrum; 3, calculating a correction coefficient; and 4, correcting the data. The method can be used for correcting the imaging spectrum reflectivity data generated after the spectral reconstruction by utilizing an atmospheric radiative transfer model in order to reduce the error and reduce the information extraction leakage rate. The method has a practical value and a wide application prospect in the hyperspectral remote-sensing mineral identification field.

The invention discloses a spectral reconstruction method based on frequency domain coding. The method comprises the following steps: firstly, a spectral imaging system collects spectral information ofa scene by using a mask, and the mask modulates the spectral dimension and the spatial dimension of the scene image; then the modulated image is obtained by projecting the spectral information onto abroadband substrate to modulate the spectral change, and the modulated image is mapped to a Fourier domain for multiplexing. Finally, the encoded images compressed in spectrum and space are collectedby the sensor, and the spectral information is reconstructed by decoding the spectral dimension. The method of the invention not only can improve the spatial resolution, but also simplifies the spectral encoding and decoding process in the process of collecting and reconstructing data, avoids the complex calculation load, and realizes the high-precision capture of the high-spectral data in a short time.

The invention discloses a quantum dot spectral imaging system which comprises a front telescope, a quantum dot array plate, and an image detector arranged in order. The image detector is at the back focal plane of the front telescope. According to the quantum dot array plate, quantum dot materials with different spectral transmission rate characteristics are parallelly made on a substrate along a spectral dimension direction, each quantum dot material covers one or more rows of detection pixels in the spectral dimension direction, and a quantum dot strip is formed. The scheme is based on the quantum dot spectral imaging system, compared with a traditional spectral imager, the system has the advantages of stable performance, a high energy utilization rate, a simple structure, a small volume, a light weight and low cost, the complexity and development cost of the system are effectively reduced, the system is suitable for airborne or spaceborne equipment, and especially the application requirement of load miniaturization is satisfied. In addition, an incident spectrum can be restored well by using a spectral reconstruction algorithm.

The invention is applied to the technical field of spectrum imaging and provides an imaging system and an imaging method for a compressed spectrum. The system comprises a detection light source, a light-splitting grating, a digital micro-mirror, a light-combining grating, a detector and a calculating and controlling unit, wherein the light-splitting grating, the digital micro-mirror, the light-combining grating and the detector are sequentially arranged in the spread direction of light; the calculating and controlling unit is used for controlling the digital micro-mirror to form a plurality ofcircularly-distributed sinusoidal coding graphics, adjusting the shapes of the sinusoidal coding graphics, receiving spectral images detected when the detector adopts different sinusoidal coding graphics and combining the spectral images; an original spectral image is reconstructed by inverse Fourier transform. The imaging system for the compressed spectrum adopts a double grating structure; by writing the sinusoidal coding graphics in the digital micro-mirror, the cycle and the phase of the sinusoidal coding graphics are changed, and different detected spectral images are obtained; finally,the compressed spectrum is reconstructed by using the inverse Fourier transform, so that the reconstruction time of a spectrum is shortened.

This invention provides an imaging spectroscopic data processing system and method, based on present status that there exists no mature system in imaging spectroscopic data processing realm capable of pre-processing data, intending by format-switch processing original data input to the said system, radiation scaling data according to equipment parameter, atmospheric correcting the data scaled, geometric correcting the data by attitude parameter, rebuilding spectrum by surface-scaling data and so on to realize the treatment of imaging spectroscopic data and automatic pre-processing of imaging spectroscopic data via computer technology. The invention improves appliance efficiency and effect of imaging spectroscopic data treatment, and promotes appliance industrialization progress of airborne imaging spectrum technology.

The invention provides an ultra-high harmonic detection device and detection method based on compressed sensing. A sensing circuit is installed on a measurement end of a power distribution network, asignal outputted by the sensing circuit passes through a high-pass filter at first, then is mixed with a pseudo-random sequence outputted by a D/A module of a multifunctional data collection card andis sent to a low-pass filter, low-speed sampling of the signal is realized by an A/D module of the multifunctional data collection card, and finally, the signal is uploaded by a USB bus to an upper computer to complete the reconstruction and display of the signal. The high-pass filter is designed to be adjustable in gain and can also condition the signal in addition to filtering the power frequency and other low-frequency signal interference. The low-pass filter is designed to be achieve adjustable in cut-off frequency, and then variable compression ratio detection can be realized. A synchronous trigger module of the multifunctional data collection card can achieve D/A and A/D synchronization, and construct an observation matrix with a phase deviation of 0 to improve the reconstruction precision. The ultra-high harmonic detection device and detection method provided by the invention have the distinct advantages of greatly reducing the sampling frequency and the number of sampling points of a measurement device while ensuring that ultra-high harmonics and the spectral reconstruction thereof are correct.

The invention belongs to the technical field of hyperspectral image processing, and discloses a spectral super-resolution adaptive weighted attention mechanism deep network data processing method, which comprises the steps of selecting two spectral reconstruction challenge data sets for verification; evaluation index selection: using a root mean square error RMSE and an average relative absolute value error MRAE as evaluation indexes, and then calculating the MRAE and the RMSE; constructing an adaptive weighted channel attention mechanism module; constructing a second-order non-local module based on partitioning; and constructing an adaptive weighted attention mechanism network and training. According to the invention, a brand-new adaptive weighted attention mechanism network is designed for spectral super-resolution, and a backbone network is formed by stacking a plurality of double-residual modules which realize double-residual learning through long and short connection. And a self-adaptive weighted channel attention module is embedded in the double-residual module, so that the channel characteristic response is recalibrated, and the characteristic expression capability of the network is enhanced.

The invention provides a spectral reconstruction method based on images of an RGB color camera. The method comprises a training stage and a reconstruction stage. The training stage comprises the following steps: obtaining a first RGB image by the RGB color camera under a light source L1; performing synthesis and white balance transformation computation on the first RGB image to obtain the first RGB image under standard light source LC, and calculating responsivity of the first RGB image; calculating the specific spectral reflection factor of the first RGB image under the standard light sourceLC; performing training and learning on the responsivity and the specific spectral reflection factor of the first RGB image under the standard light source LC to obtain a map. The reconstruction stagecomprises the following steps: obtaining a second RGB image by the RGB color camera under the light source L1; performing synthesis and white balance transformation computation on the second RGB image to obtain the second RGB image under the standard light source LC; calculating responsivity and specific spectral reflection factor of the second RGB image under the standard light source LC by themap to obtain spectral reflectivity of the second RGB image under the standard light source LC.