65 results about "Ghost imaging" patented technology

The invention relates to the technical field of underwater imaging, in particular to an underwater multispectral ghost imaging system based on deep learning image super-resolution reconstruction, which comprises a light modulation module, a multispectral light intensity data acquisition module and a control and image reconstruction module, the control and image reconstruction module is respectively connected with the light modulation module and the multispectral light intensity data acquisition module, the control and image reconstruction module controls the light modulation module to modulate an illumination light beam and then project the illumination light beam to a target object, and the modulated illumination light beam is reflected by the target object and then received by the multispectral light intensity data acquisition module; and the multispectral light intensity data acquisition module sends the received reflected light beam to the control and image reconstruction module, image reconstruction is performed through a pre-trained deep neural network in the control and image reconstruction module, and a super-resolution image is output. The underwater multispectral ghost imaging system is provided in combination with a deep learning image super-resolution reconstruction algorithm, and the imaging quality and resolution are improved.

The present invention proposes a method and system for optimizing a large-scale ghost imaging search and rescue system in a foggy environment. The method selects a light source based on ZEMAX, and places a beam splitter behind the light source to form two light beams, obtains a detection light path and a reference light path, sequentially arranges a lens, an object to be detected, and a barrel detector on the detection light path, and arranges a layer of fog simulation on the lens object to be detected, and arranges a surface detector on the reference light path, and simulates through ZEMAX, obtains the light field and light intensity of the two detectors, calculates the Gaussian point spread function width according to the relationship between the light field and the light intensity, calculates the depth of field according to the lens parameters, and optimizes to minimize the Gaussian point spread function width and maximize the depth of field as optimization targets to obtain the optimal imaging parameters. The present invention uses Mie scattering combined with the concept of depth of field in traditional imaging to optimize the parameters of the ghost imaging system, so as to achieve large-scale imaging in a foggy environment.

The invention discloses a ghost imaging target reconstruction method based on FSD-GIRA, and relates to the technical field of digital image processing. According to the technical scheme, a fuzzy clustering algorithm is used for classifying feature values of an optical field intensity measurement matrix of an optical path detector, the feature values are reconstructed through singular value decomposition, dimensionality reduction processing of measurement data of a ghost imaging system is achieved, and the ghost imaging target reconstruction method based on FSD-GIRA is obtained. A WE-FOMP-CG algorithm is proposed, and recovery of sparse signals is optimized by combining generalized orthogonal matching pursuit, a conjugate gradient method and a multi-threshold wavelet noise reduction method; and selecting the most relevant sensing matrix array in each iteration, solving a linear equation set by using PCG, carrying out wavelet preprocessing and multi-threshold de-noising on a result, and updating and recovering estimation. The calculation efficiency and the convergence speed are improved, and a more accurate recovery result is provided in a high-noise environment. The invention also provides a WBDH algorithm, and the algorithm employs a dual denoising mechanism, including wavelet transform and a BM3D method, so as to guarantee that the restored image details and structures are reserved, and further improve the reconstruction quality of the ghost imaging target.

The invention discloses a single-pixel imaging one-dimensional signal enhancement method based on coding image similarity, which belongs to the technical field of computational optical imaging, and comprises the following steps of: reconstructing an image by using a one-dimensional signal by using a traditional ghost imaging method; obtaining a coded image, and calculating a similarity index; for any coded image, calculating a weight coefficient according to similarity, and performing weighted average denoising on the one-dimensional signal to obtain an updated one-dimensional signal; using a momentum rule to balance the relative weight of the one-dimensional signal before and after updating; and performing one-dimensional signal updating on all the coded images, and reconstructing the images again by using the updated one-dimensional signals. According to the method, the problem that the signal-to-noise ratio of a one-dimensional signal is reduced due to factors such as insufficient sampling rate and weak illumination intensity during high-speed single-pixel imaging can be effectively solved, and the method is adaptive to coding masks adopting orthogonal projection modes such as a Hadamard base and a Fourier base, and has an important reference value for future high-speed single-pixel imaging.

The invention provides a large-range ghost imaging search and rescue system optimization method and system in a heavy fog environment, and the method comprises the steps: selecting a light source based on ZEMAX, placing a beam splitter behind the light source to form two light beams, obtaining a detection light path and a reference light path, sequentially arranging a lens, a to-be-detected object and a barrel detector on the detection light path, and carrying out the optimization of the large-range ghost imaging search and rescue system. A layer of heavy fog simulant is arranged on a to-be-measured object of the lens, a surface detector is arranged on a reference light path, simulation is carried out through ZEMAX, light fields and light intensities of the two detectors are obtained, the Gaussian point spread function width is calculated according to the relation between the light fields and the light intensities, and depth-of-field calculation is carried out according to lens parameters. And optimization is carried out by taking the minimization of the Gaussian point spread function width and the maximization of the depth of field as optimization targets, and optimal imaging parameters are obtained. According to the invention, the Mie scattering is combined with the depth-of-field concept in the traditional imaging to optimize the parameters of the ghost imaging system, so that large-range imaging can be realized in a heavy fog environment.

The invention discloses a ghost imaging image reconstruction method and device and a storage medium, and the method comprises the steps: firstly obtaining a low-quality reconstructed image generated by a ghost imaging system, and then inputting the low-quality reconstructed image into a trained LD-GIRNet network for processing, and obtaining a high-quality target reconstructed image. The network is of an encoder-decoder architecture, and the core of the network is that a plurality of lightweight decoding reconstruction units are embedded in an encoder and a decoder. The LDRU integrates a channel-space attention joint enhancement module or a detail enhancement multi-scale residual module to enhance features and suppress noise. Meanwhile, the network is connected with a decoder and an encoder through a residual compensation fusion module, and shallow detail features and deep semantic features are effectively fused. According to the method, the lightweight design of the model is realized, the parameter quantity is remarkably reduced, the image quality, noise robustness and detail recovery capability of ghost imaging reconstruction at a low sampling rate are effectively improved, and the method has excellent generalization performance.

This invention discloses a method, device, and storage medium for ghost imaging image reconstruction. The method first acquires a low-quality reconstructed image generated by a ghost imaging system, and then inputs it into a trained LD-GIRNet network for processing to obtain a high-quality target reconstruction image. The network employs an encoder-decoder architecture, with multiple lightweight decoding and reconstruction units embedded in both the encoder and decoder. LDRU integrates a channel-spatial attention joint enhancement module or a detail enhancement multi-scale residual module to enhance features and suppress noise. Simultaneously, the network connects the decoder and encoder through a residual compensation fusion module, effectively fusing shallow detail features with deep semantic features. This invention achieves a lightweight model design, significantly reducing the number of parameters while effectively improving the image quality, noise robustness, and detail recovery capability of ghost imaging reconstruction at low sampling rates, exhibiting excellent generalization performance.

A scan circuit applicable to driving a plurality of light-emitting-diodes of a plurality of scan lines of a display panel is provided. A plurality of switching components are configured, having each switching component connected with one of the scan lines. A plurality of power transistors are disposed corresponding to the scan lines, having each power transistor connected between one scan line and a ground terminal. A low-dropout regulator circuit or a global operational amplifier is alternatively connected to the plurality of switching components in common for selectively generating an output voltage to at least one of the scan lines and avoiding ghost imaging on the display panel. By employing the proposed scan circuit, the invention is effective in achieving de-ghost functioning and avoiding conventional signal coupling issues on the display panel. In addition, redundant power waste consumption can be significantly reduced at the same time.

The invention discloses a vector magnetic field measurement method based on correlated structured light. Firstly, special correlated structured light is designed, the correlated structured light is divided into two beams after entering a beam splitter, one beam is called reference light and is emitted to a CCD, and information is recorded by the CCD; and the other beam of light is signal light, the signal light enters the atomic pool, the light is irradiated to the PD detector after penetrating through the atomic pool, and information is recorded by the PD. Because the light interacts with the magnetic field and the atoms in the atom pool, the information recorded by the PD detector changes along with the change of the size and the direction of the magnetic field. Information recorded by the CCD and information recorded by the PD are subjected to correlation operation, so that a light absorption diagram of atoms can be obtained, and information of a magnetic field can be obtained through the light absorption diagram. The vector magnetic field measurement method based on the correlated structured light can be applied to the fields of space flight and aviation, geophysics, military affairs and the like. According to the scheme provided by the invention, the special correlated structured light can be generated by controlling the hologram, the light passes through the ghost imaging system, and then correlated imaging is carried out, so that the information of the magnetic field can be obtained. In addition, the device is simple, and a new path is provided for developing a small and compact magnetometer.

The present invention discloses a spectral measurement method for frequency-domain ghost imaging based on dispersion stretching. Based on the existing frequency-domain ghost imaging technology based on dispersion stretching, the present invention cuts the target spectrum, and sets multiple dispersion modules with different frequency bands to respectively perform time-domain stretching based on dispersion Fourier transform on multiple optical pulses after spectrum segmentation, and then uses a preset pseudo-random code to simultaneously perform intensity modulation in the time domain on the multiple time-domain stretched optical pulses, thereby realizing time-domain to frequency-domain mapping coding modulation. The present invention also discloses a spectral measurement device for frequency-domain ghost imaging based on dispersion stretching. The present invention can solve the deficiencies of the existing frequency-domain ghost imaging technology based on dispersion stretching in terms of the detection bandwidth range. On the basis of realizing high-precision and large-bandwidth range modulation of the frequency-domain optical field, it can achieve large-bandwidth and high-resolution detection of the spectral target with low-cost detection means, and improve the ability of the frequency-domain ghost imaging technology to detect spectra.

This invention proposes a crosstalk-free multi-image encryption method based on computational ghost imaging. The method first encrypts multiple plaintext images using a computational ghost imaging system and binarizes the bucket detector value sequences to obtain multiple sets of binary data sequences of the same length. Then, the obtained sets of binary data sequences are converted into a grayscale image with decimal pixel values. Finally, a pixel bit position scrambling algorithm is used to scramble the pixel values ​​of the grayscale image to obtain the final ciphertext image. During decryption, the multiple sets of binary data sequences corresponding to the multiple plaintext images are recovered through the reverse pixel bit position scrambling operation. Then, legitimate users use the key to decrypt their respective plaintext images, and the decrypted image is unaffected by crosstalk noise from other images. This invention solves the crosstalk noise problem in optical multi-image encryption and increases the encryption capacity, which is beneficial for improving the transmission and storage efficiency of multi-image information.

The invention discloses a multi-image hiding encryption method based on deep learning and ghost imaging. Comprising the following steps: 1, converting a secret image into a spectrogram through two-dimensional fast Fourier transform (FFT); 2, a designed equal-resolution image steganography model ERIH-Net is utilized, the spectrogram and the carrier image serve as input of the ERIH-Net model, and a secret-containing image is generated; 3, sampling the secret-containing image for multiple times through a ghost imaging system to obtain a BD value sequence of the one-dimensional bucket detector; 4, reconstructing a secret-containing image by using the light field information re-modulated by the ghost imaging system and the acquired BD value through a correlation reconstruction algorithm; 5, taking the secret-containing image as the input of an ERIH-Net model image extraction sub-network, and extracting a frequency spectrum image; and 6, decrypting the secret image through two-dimensional inverse Fourier transform (IFFT). According to the invention, the security and information hiding capacity of the optical image encryption system can be improved; by cutting part of high-frequency information in the spectrogram, the transmission quantity of the ciphertext can be reduced.

This invention discloses a compressed optical encryption scheme based on ghost imaging and image authentication. During encryption, the target object is illuminated using Hadamard speckle lighting, and the intensity of the reflected light is collected by a bucket detector. To improve data transmission security, a steganography method is introduced to hide the data. The collected bucket signals are encoded into a series of random binary speckle sequences and hidden within a projection pattern used to illuminate the authentication image. During the measurement of the authentication image, diffraction data is collected using the bucket detector to obtain the sequence authentication key data. Once the receiver is authenticated, the corresponding key is sent to recover the secret image. This invention introduces an authentication method, improves the security of information transmission, compresses the data, and the proposed encryption method has high decryption quality and security.

The application discloses a kind of ghost imaging reconstruction and classification method and equipment based on hybrid network, belong to the field of computational imaging technology, will be based on Hadamard matrix generation light speckle sequence formed by fixed light speckle sequence;Then, based on this light speckle sequence sampling expanded MNIST data set, generate light intensity sequence, light speckle sequence data;Then, construct and train GRU-CNN network model;Finally, using the model trained to calculate correlation imaging reconstruction and target identification classification.The conventional ghost imaging technique is often faced with the problem of poor image quality under low sampling conditions.In order to solve this problem, the application proposes a GRU-CNN network (GCGI) model combined with deep learning, which can efficiently reconstruct high-quality images and accurately identify and classify targets under very low sampling rates by fusing light intensity sequence and light speckle information, and has strong practical value.

The invention provides a video image defogging and low illumination enhancement method and system, and relates to the technical field of video processing, and the method comprises the steps: synchronously obtaining a video frame sequence, a reference frame and a light intensity correlation value of a scene, carrying out the ghost imaging reconstruction through employing a Fourier lamination imaging method, and carrying out the ghost imaging reconstruction; fusing the reconstructed image with the fog concentration gradient, the illumination attenuation coefficient and the turbulence disturbance intensity extracted from the video frame sequence to generate a fused feature map; then, a pulse coupling neural network is adopted to divide fog-effect, low-illumination and turbulence regions, and a capsule network is utilized to perform dynamic routing and feature reconstruction on each region to obtain a feature vector set; and finally, performing adaptive restoration on each region in combination with the features of the reference frame to generate a clear image. According to the invention, the image quality and stability of the digital twin system in complex outdoor scenes such as foggy days, low illumination and turbulence can be effectively improved.

The application discloses a two-stage modulation-based underwater polarization ghost imaging method and device, and relates to the fields of optical imaging and ocean detection. The method comprises the following steps: acquiring basic data of a target water area and constructing a backscattering point spread function; performing statistical decorrelation modulation on an original reference speckle sequence based on a first correlation coefficient, a second correlation coefficient and the backscattering point spread function, to obtain a primary modulation reference speckle distribution sequence; performing polarization space modulation on the primary modulation speckle sequence, to obtain a secondary modulation reference speckle distribution sequence; irradiating a target to be detected according to the secondary modulation reference speckle distribution sequence, acquiring a double-polarization bucket detection signal, and constructing a polarization difference signal sequence; and performing correlation reconstruction on the polarization difference signal sequence and the secondary modulation reference speckle distribution sequence, to obtain a reconstructed image. The application solves the problems of serious backscattering interference and insufficient imaging signal-to-noise ratio in a strong turbid underwater environment, and realizes high-quality underwater polarization ghost imaging.

The application discloses noise-eating ghost imaging with depth information, first uses the characteristics that light reaches different distances of objects at different times, acquires two-dimensional images of scenes at each depth, and reconstructs the depth information of the object. The noise-eating algorithm makes the noise of the reconstructed image reduced. In this paper, two layers of slices with high depth resolution are used for experiments. In the normal lighting condition, laser and random speckle can be used, and a darkroom is not needed. Compared with radar imaging, the application has lower cost, simpler equipment and is more convenient to debug. In the field of remote sensing detection, the application can not be affected by extreme environment and has faster imaging speed. Compared with the original image, the noise-eating algorithm has an NRSS value increase of 45%, a Brisque increase of 25% and a Niqe increase of 33% in the image quality evaluation. Compared with the untreated image, the application greatly reduces the influence of noise on the depth information of the image.

This invention discloses a noise-free ghost imaging method and system, relating to the field of photoelectric imaging technology. The method includes generating a two-dimensional matrix of random numbers, binaryizing the two-dimensional matrix according to grayscale information to obtain a binary two-dimensional matrix, dividing the two-dimensional matrix into P groups of two-dimensional binary matrices, using the P groups of two-dimensional binary matrices as projection matrices, projecting them onto a target object X, and using a bucket detector composed of an array detector and an FPGA board to acquire the reflected light intensity values ​​of the P groups of target objects as collected data, simultaneously performing binarization processing to obtain the P groups of bucket measurement signals; calculating the target object matrix based on the P groups of two-dimensional binary matrices and the P groups of bucket measurement signals, and reconstructing the target object image. This invention utilizes the binary nature of binary data to achieve noise-free imaging, overcoming the problem of existing methods where unavoidable noise prevents the reconstruction of high-quality images.

This invention discloses a computational ghost imaging method based on conditional generative adversarial networks (GANs) for low sampling rates. This method can extract internal features from images in harsh environments or at low sampling rates. By further improving and optimizing traditional autoencoder algorithms, it effectively solves the problems of image quality and speed in reconstructed images under low sampling conditions. Compared with traditional CGI, CSCGI, CNN-CGI, DCAN-CGI, and DGI methods, this invention can reconstruct target images faster at low sampling rates, and significantly improves both PSNR and SSIM, effectively filtering out background speckle. Practical physical experiments further verify the feasibility of this method. While ensuring the quality of the reconstructed image, the high efficiency of GPU multi-core parallel computing can significantly shorten both the training time of the neural network and the target object reconstruction time, which is of great significance for engineering applications.

The invention relates to a ghost imaging neural differential analysis method under a selected plaintext attack condition in the technical field of computational imaging and optical security. The method comprises the following steps: firstly, designing a differentiated plaintext, and deducing a projection speckle by adopting differential calculation; then, constructing a dimensionality reduction simulation training set and an experimental test set based on the structural features of the projection speckles; then, constructing a one-dimensional signal noise reduction neural network model, and training the one-dimensional signal noise reduction neural network model by adopting the dimension reduction simulation training set; and finally, inputting the experimental test set into the trained one-dimensional signal noise reduction neural network model to obtain deciphered speckles, and based on the deciphered speckles, adopting ghost imaging correlation operation to obtain a cracked plaintext image. That is to say, based on the basic principle of ghost imaging encryption, the inherent linear property of the ghost imaging light path is utilized, and the fusion architecture of the differential attack and the neural network is used, so that the cracking of various ghost imaging encryption methods can be realized, and the universality and reliability of ghost imaging encryption analysis can be improved.

This invention relates to a variable-resolution ghost imaging method for axially moving targets, comprising: determining the axial speed and distance of the moving target, as well as the size of the moving target and the distance from the moving target to the optical projection system; setting speckle parameters; generating a human-eye-like non-uniform variable-resolution speckle pattern with high resolution at the fovea, low resolution around the periphery, and a uniformly varying radius of the fovea region based on the speckle parameters, the moving target's motion parameters, and scene information; projecting the human-eye-like non-uniform variable-resolution speckle onto the axially moving target for sampling and measuring the reflected light intensity; and performing a second-order correlation operation between the reflected light intensity and the projected speckle information to obtain the final reconstructed image. This invention utilizes a human-eye-like non-uniform speckle pattern with a varying fovea radius to reconstruct axially moving targets, maintaining a constant proportion of the fovea region occupied by the target during movement, thereby improving the imaging quality of axially moving targets.

The invention discloses a super-resolution ultrasonic holographic ghost imaging device based on photoacoustic waves and an imaging method thereof, comprising a collimated light source, a filtering beam expansion optical path, a spatial light modulator, a projection imaging optical path, a photoacoustic converter, a water tank located above the photoacoustic converter to press a sample and blocked with a film, and a sound wave detection and acquisition system, wherein the collimated light source emits parallel light, the diameter of the parallel light is enlarged and spatially filtered through the filtering beam expansion optical path, and the light beam with spatial coding is modulated into a light beam with spatial coding through the spatial light modulator, and the light beam with spatial coding is irradiated to the photoacoustic converter through the projection imaging optical path to generate a sound wave with a corresponding intensity distribution, and the sound wave changes in amplitude and phase after passing through the sample, and then enters the water tank through the film to continue to propagate and be received, and the sound wave detection and acquisition system reconstructs a hologram of the transmittance and phase change of the sound wave passing through the sample. The invention can image the ultrasonic hologram of the sample so as to fully understand the composition, morphology and other structural information of the sample.

The invention relates to a ghost imaging method based on computer-generated holography, a computing device and a readable storage medium, and the method comprises the steps: constructing a matrix on an object plane, further generating a corresponding positive and negative matrix set, generating a holographic phase diagram corresponding to the positive and negative matrix set on a light source plane through a computer-generated holography technology, and obtaining sets # imgabs0 # and # imgabs1 #, # imgabs2 #, # imgabs3 # and # imgabs4 #; each holographic phase diagram in # imgabs2 # and # imgabs3 # is loaded into a spatial light modulator to serve as an illumination speckle to be projected to the plane of an object to be measured, the total light intensity of each illumination speckle after transmission or reflection is received by a barrel detector, and a set # imgabs4 # and # imgabs5 # formed by signals received by the barrel detector is obtained; the real light intensity distribution # imgabs8 # and # imgabs9 # formed when each holographic phase diagram in # imgabs6 # and # imgabs7 # is propagated to an object plane is calculated by using a Fresnel diffraction formula, a set # imgabs10 # and a set # imgabs11 # are obtained, and finally, a high-resolution image of an object to be measured is reconstructed by using # imgabs12 #, # imgabs13 #, # imgabs14 # and # imgabs15 # in combination with a second-order correlation function. The imaging resolution can be improved under the condition that the imaging system is not changed, and the method has wider applicability.

The present application provides a kind of ghost imaging monitoring method and system based on environmental coupling simulation and image enhancement, the method comprises: setting initial light source, the initial light source is handled with splitter matrix model to obtain probe light path light source and reference light path light source, based on the probe light path light source and reference light path light source, respectively construct probe light path and reference light path;In probe light path, lens, object to be measured and bucket detector are set in sequence, and surface detector is set in reference light path, high gauss blur is introduced in probe light path to simulate heavy fog environment, and random phase screen based on Perlin noise is introduced to simulate turbulent environment.The present application builds ghost imaging system through Python, uses Gaussian simulation to simulate heavy fog environment and turbulent environment, and combines the concept of histogram equalization in digital image processing, the imaging quality of ghost imaging is optimized by non-linear mapping gray scale chart, and the imaging quality of ghost imaging system under long-distance condition can be improved.

The present application is applicable to the field of data processing technology, and provides a method, apparatus, equipment and storage medium for monitoring hydropower station equipment, including: using ghost imaging technology to collect light field distribution information and reflected light intensity information of equipment monitoring points; inputting the light field distribution information and reflected light intensity information into a trained leakage monitoring model for processing to obtain monitoring results of the equipment monitoring points, and the leakage monitoring model is obtained by training the initial model using a sample training set. In the above scheme, ghost imaging technology is used to collect light field distribution information and reflected light intensity information of equipment monitoring points, so that the collected light field distribution information and reflected light intensity information of the equipment monitoring points are not affected by the on-site light intensity, and accurately reflect the current status of the equipment monitoring points. On this basis, by processing the light field distribution information and reflected light intensity information based on the leakage monitoring model, it is possible to accurately identify whether the hydropower station equipment is leaking, thereby improving the accuracy of the monitoring results.

An apparatus for pseudo thermal light source ghost imaging and range sensing using narrow-band spontaneous emission includes: a pseudo thermal light source including a resonator type laser diode; a temperature controller configured to control a temperature of the pseudo thermal light source; a current controller configured to control a current of the pseudo thermal light source; a beam splitter configured to split a laser beam emitted from the pseudo thermal light source into a first beam and a second beam; and a temporal correlator configured to infer a target range to an object through temporal coherence between the first beam and the second beam, wherein the pseudo thermal light source oscillates a laser beam having second order coherence when a specific current is applied according to temperature.

The invention discloses a calculation ghost imaging reconstruction method based on a Vision Transform and a U-Net network, and the method comprises the following steps: 1, enabling a Hadamard matrix of a cut cake sequence to serve as a phase mask, sequentially embedding the Hadamard matrix into a spatial light modulator, sequentially projecting the Hadamard matrix to a target image, and collecting a total light intensity sequence through a barrel detector; 2, partitioning the Hadamard matrix of the cake cutting sequence and the light intensity sequence, performing linear projection on each block to obtain a high-dimensional vector token, adding a position code to each high-dimensional vector token, inputting the high-dimensional vector token into a Transform encoder for processing, and converting the output into a two-dimensional matrix; and step 3, inputting the two-dimensional matrix obtained in the step 2 into the U-Net network to enhance transmission and fusion of feature information until a loss function of the ViT-U-Net network is kept stable, and obtaining a result of calculating ghost imaging. The image reconstructed by the method is high in quality and high in reconstruction efficiency.

A high signal-to-noise ratio ghost imaging method based on orthogonal modulation of light spot of light source, a special light spot pattern is used to irradiate a sample to obtain a high signal-to-noise ratio image. The special light spot pattern makes the functions of the intensity of the light source irradiated on different positions of the sample changing with time orthogonal to each other, for example, the light intensity fluctuations at different positions have different frequencies. By detecting the light signal intensity fluctuations through the object and analyzing the frequency components of the signal, the shape of the object can be restored. The signal-to-noise ratio of the image is greatly improved. Correspondingly, the number of light spot patterns required for clear imaging is greatly reduced, thereby improving the imaging speed.

According to the method for evaluating the uncertainty of the deep learning reconstruction result of the ghost imaging system, the input data uncertainty caused by the characteristics of the ghost imaging system is combined, and the accuracy of uncertainty evaluation is improved. The method comprises the following steps: firstly, introducing a Bootstrap resampling technology to simulate a large amount of sample data obeying input data uncertainty distribution; then, different Bootstrap resampling is implemented in each optimization step by utilizing the characteristics of neural network training iterative optimization, so that the input data uncertainty is introduced in the network training process; and finally, in an actual prediction process, according to a proposed improvement mode, a Bayesian neural network framework is combined with Bootstrap resampling, and uncertainty estimation of a ghost imaging system deep learning reconstruction result is given. According to the method provided by the invention, the model uncertainty during prediction of the training network can be reduced, the generalization ability of the network to the input uncertainty is improved, the accuracy of prediction uncertainty distribution is enhanced, and meanwhile, the image reconstruction quality is not obviously reduced.

The application discloses a kind of turbulent flame geometry three-dimensional tomography method and system based on multi-probe CGI, comprising: N single-pixel detector is arranged around flame to form multi-probe detection array, dynamic space illumination modulator is used to generate structured light field to illuminate flame, each detector synchronously acquires barrel detection signal;Through compressed sensing reconstruction algorithm combined with convex optimization solving method, flame two-dimensional projection image under each view angle is recovered;Using adaptive histogram equalization, combined with filtering method, the calculation ghost imaging noise is inhibited, and two-dimensional projection image preprocessing is realized;Based on multi-view geometric projection relationship, construct tomographic projection matrix, use algebraic reconstruction technique combined with full variation regularization iterative algorithm to solve three-dimensional voxel distribution, and the flame front geometry three-dimensional structure is reconstructed.The application uses single-pixel detector to replace traditional area array camera, with the advantages of low cost, strong ability to resist harsh environment, high time resolution, suitable for high space-time resolution three-dimensional measurement.