424 results about "Polarization imaging" patented technology

The invention discloses a quartzite surface defect detection method, system and device, and relates to the technical field of image processing, and the method comprises the following steps: fixing a to-be-detected quartzite on a detection platform, and carrying out preprocessing; constructing a double-path polarization imaging light path; polarization parameter optimization is carried out based on the optical anisotropy characteristic of quartz stone, so that a polarization state difference is generated between a defect area and a normal area; under the condition of polarization parameter optimization, synchronously acquiring a first polarization image and a second polarization image through a double-path polarization imaging light path; performing polarization difference calculation on the first polarization image and the second polarization image to generate a polarization difference image; and carrying out contrast enhancement processing on the polarization difference image, identifying a defect area, carrying out defect classification, and outputting a quartz stone surface defect detection result. By optimizing polarization imaging parameter configuration and combining image processing, high-precision detection and intelligent classification of quartz stone surface defects are achieved, and the automation level and reliability of detection are improved.

The invention discloses an industrial area atmospheric environment monitoring management system and method, and relates to the technical field of industrial Internet of Things environment monitoring, and the method comprises the steps: triggering polarization imaging to scan a target region based on the time-space information of an abnormal event signal, obtaining the polarization characteristic data of a pollution plume, and generating a spatial distribution characteristic parameter; inputting the sound pressure abnormal indexes and the spatial distribution characteristic parameters into an industrial equipment leakage causal model, calculating a pollution source confidence coefficient, and when the pollution source confidence coefficient reaches a process safety dynamic threshold value, obtaining an industrial equipment traceability result and a leakage level; according to the traceability result of the industrial equipment and the leakage level, a hierarchical control instruction is obtained, meanwhile, the actually measured attenuation rate of the industrial pollutants is calculated, and the dynamic response effect is verified; according to the method, cross-modal fusion modeling is carried out on acoustic abnormal information and pollutant spatial distribution characteristics, so that the technical limitation that pollution source positioning is carried out by depending on a static empirical formula traditionally is broken through.

The invention belongs to the technical field of image processing and robots, and particularly relates to an image processing method and device and a robot, and the method comprises the steps: obtaining a structured light coding image, a polarization imaging image and a binocular depth image; constructing a multi-dimensional feature vector by fusing decoded contour phase, polarization analysis material characteristics and depth image data; dynamically calibrating the multi-dimensional feature vector according to the ambient light and the pose of the robot, correcting color deviation through an adaptive white balance algorithm, compensating a camera installation error, and obtaining a calibrated descriptor; based on the calibrated descriptor, utilizing an improved Hough transformation and template matching algorithm to identify the object category attitude, and calculating six-degree-of-freedom pose parameters through a triangulation method; and according to the six-degree-of-freedom pose parameters, a collision-free grabbing path is generated in combination with a robot kinematics model and obstacle information. Therefore, the problems of insufficient image analysis capability, single compensation mechanism, lack of path optimization capability and the like in the prior art are solved.

The invention relates to the technical field of unmanned aerial vehicle detection and identification, in particular to an unmanned aerial vehicle detection method and system based on visible light polarization imaging. According to the method, a channel state sequence with aligned time sequences is constructed by obtaining multi-source environment sensing data, cross-layer optimization parameters are dynamically calculated and decomposed into calculation and transmission characteristic quantities, and edge nodes are scheduled to cooperatively generate resource scheduling instructions; physical resources are matched to construct a virtual instance topology, a control instruction is generated through online reasoning of a dual-channel graph neural network training model, and finally the control instruction is fed back to the polarization sensor to achieve link calibration. According to the system, through fusion of polarization imaging and cross-layer optimization technologies, the accuracy and real-time performance of unmanned aerial vehicle detection in a complex environment are remarkably improved, meanwhile, computing resource consumption is reduced, and efficient spectrum multiplexing and power adaptive adjustment in a dynamic environment are achieved.

The invention belongs to the technical field of industrial detection, and discloses a defect detection method and system based on honeycomb catalyst stacking. Omnibearing image data of honeycomb catalyst stacking are obtained through a multi-angle polarization imaging technology, pixel-level polarization degree parameters are calculated to construct a global polarization feature map, and accurate distinguishing between an intrinsic porous structure and suspected defects is achieved. A blind area identification and virtual view angle reconstruction mechanism is introduced, so that the problem of a stacked edge detection blind area is solved; and a layered reflectivity compensation function is adopted, so that the optical interference of an interlayer overlapping region is eliminated. Texture features are extracted through multi-scale morphological filtering, multi-dimensional feature fusion is carried out in combination with polarization features, edge continuity indexes and correction reflection intensity, and a high-precision defect discrimination model is established. And for a low-confidence region, dynamically adjusting detection parameters and performing iterative optimization to form an adaptive detection closed loop. According to the invention, the detection precision and reliability are improved, and the defect position, type and severity can be accurately output.

The invention relates to the technical field of image fusion, and discloses a sparse aperture optical system polarization image fusion method based on deep learning, and the method comprises the steps: obtaining a linear polarization degree image, a polarization angle image, and a polarization intensity image through a sparse aperture optical system, and constructing a polarization image fusion model comprising an encoder, a multi-mode fusion module, and a decoder; the encoder comprises two branches for respectively extracting three polarization image features, the multi-modal fusion module comprises an edge gradient compensation module for extracting multi-stage edge features, a polarization attention mechanism for adaptively weighting fusion features and a residual aggregation module for reserving original features, and the decoder uses multi-core deconvolution to decode aggregation features; and constructing a loss function and training a model in combination with the characteristics of the polarization images, and inputting the three polarization images to be fused into the trained model to obtain a polarization fusion image. According to the invention, effective fusion of polarization imaging and sparse aperture imaging can be realized, noise can be effectively suppressed, and the contrast and resolution of imaging can be improved.

The invention provides a multi-scale polarization data fusion method based on a 4K polarization imaging detector, and relates to the technical field of image processing, and the method comprises the steps: carrying out the adaptive edge detection of four original polarization images, generating a comprehensive edge image, and carrying out the definition scoring; performing color calibration on each original polarization image based on the ambient light intensity and the color temperature, and outputting a polarization image after color calibration and a color deviation score; combining definition scores, color deviation scores and global brightness information entropies of the four polarization images after color calibration to construct a multi-objective optimization function, and solving the multi-objective optimization function in a multi-exposure parameter space to obtain an optimal exposure parameter combination; applying the optimal exposure parameter combination to image capture of the next period, and calculating global quality reward values of four newly collected polarization images; when the global quality reward value meets a preset condition, wavelet decomposition and frequency division multi-scale fusion are carried out on the polarization images after color correction, a fused image is generated, and the quality of the polarization images is effectively improved.

The invention discloses a laser radar and multispectral polarization imaging fused target identification system and method. The system comprises a sensor configuration and data preprocessing module, a cross-modal feature extraction and fusion module and a multi-task output and optimization module. The sensor configuration and data preprocessing module performs time-space synchronization processing on the collected original optical signals and laser signals in the environment to obtain multispectral image data and laser radar point cloud data; the cross-modal feature extraction and fusion module performs feature extraction and fusion enhancement processing on the multispectral image data and the laser radar point cloud data, and outputs high-dimensional semantic enhancement point cloud representation containing image semantics and point cloud geometry; and the multi-task output and optimization module processes the high-dimensional semantic enhanced point cloud representation and outputs a three-dimensional target recognition result, target speed information and a pixel-level depth map. Through module design and data processing, the defects in the prior art are overcome, and the accuracy of target recognition is improved.

The invention provides an underwater target identification method and system based on 4K visible light polarization imaging, and relates to the technical field of underwater target identification. High-resolution spectral intensity information and polarization physical characteristics of a target are synchronously captured by using a pixel-level registration RGB and polarization four-dimensional light field tensor coding technology; the limitation of information loss of single-mode data in a low-illumination or high-turbidity scene is overcome; the polar coordinate symmetry design of the annular direction sensitive convolution kernel is combined with the polarization degree matrix decomposition technology of the Fresnel reflection law to accurately quantify the optical reflection characteristic difference of the material; based on a cross-modal attention mechanism, dynamically learning a correlation weight of a spatial feature and a polarization feature, and strengthening a joint response of a target edge contour and an internal texture; a lightweight recognition network architecture is adopted, a polarization sensitive convolution kernel and a dynamic channel re-parameterization module are integrated, parallel efficient reasoning of target position, category and material information is achieved, and accurate recognition of an underwater target is achieved.

The invention discloses a building outer wall defect detection method based on infrared polarization imaging, belongs to the field of image processing, and aims to improve the accuracy and robustness of building outer wall defect detection. The method comprises the steps of firstly collecting building outer wall surface image data, and constructing an image data set; designing a channel disturbance intensity factor and a mutual information measurement strategy, constructing a multi-channel disturbance enhancement attention module, and extracting a multi-scale feature map; constructing a position sensing context fusion module, and enhancing a feature fusion effect through a position modulation coefficient; constructing a geometric guidance saliency modeling module, and generating a structural perception saliency map through the geometric guidance deviation factor; the above modules are integrated to construct a defect detection model, a to-be-detected image is input into the model, a defect area is output, and accurate detection and positioning of building outer wall defects such as cracks, falling and bumps are achieved.

The invention discloses a surface defect detection device and method based on polarized optical dark field imaging, and belongs to the technical field of industrial defect imaging detection, the device comprises a polarization camera, an imaging lens, a polarization annular LED light source and a sample to be detected which are coaxially arranged; wherein the polarization camera and the imaging lens form a polarization imaging system, the polarization annular LED light source emits linearly polarized light with an inclination angle, and the linearly polarized light is reflected and scattered on a sample to be detected; the linearly polarized light enters the surface of a to-be-detected sample, a small amount of reflection and most of scattered light of the to-be-detected sample are collected by the imaging lens and the polarization camera through a circular hole in the center of the polarization annular LED light source, and finally imaging is achieved. The defect depth can be calculated according to the defect width and the defect inclination angle, so that the defect depth is inversed by the low-frequency linear polarization degree, and the problem that the defect depth information cannot be effectively extracted in the prior art is solved.

The invention discloses a spectral polarization imaging three-dimensional reconstruction method and device based on a pixel array, and a medium, and relates to the technical field of image processing, and the method mainly comprises the steps: collecting original images, which are fixedly obtained by pixels, of corresponding spectral bands and polarization angles; extracting sub-image information under each spectral band and polarization angle according to pixel positions, and stacking to generate a four-dimensional data volume; constructing a polarization geometric model according to the four-dimensional data body, and constructing an actual expression of the linear polarization degree in combination with a Fresnel reflection theory; constructing a redundant dispersion constraint equation and a theoretical expression of the relation between the diffuse reflection spectrum polarization degree and the incident angle and the imaging target refractive index; substituting the redundant dispersion constraint equation into a combined residual function of the theoretical expression and the actual expression, and performing parameter inversion on the combined residual function; and carrying out three-dimensional shape recovery under imaging target surface normal reconstruction based on an inversion result. According to the method, time delay and space registration errors caused by time-sharing acquisition are eliminated.

The invention discloses a deep foundation pit crack feature coding detection method based on image recognition, and aims to solve the problems that a two-dimensional image is difficult to accurately characterize a three-dimensional crack, and the opening width and the slab staggering height are inaccurately estimated, the deep foundation pit crack feature coding detection method comprises the following steps of: calibrating and collecting through short time sequence and multi-polarization imaging, segmenting the crack to extract a center line, and dividing a left side domain and a right side domain; carrying out local reconstruction in a mask neighborhood by adopting a nerve radiation field or three-dimensional Gaussian sputtering, establishing a left implicit surface and a right implicit surface, fusing a polarization solution line, applying occupancy priori and seam-crossing repulsive potential in a gap region, combining a reference object to obtain an absolute scale, measuring a millimeter-level opening and a dislocation along a central line section, and generating a feature code; and threshold value and time sequence comparison judgment is carried out, and the technical effects of high-precision three-dimensional measurement, reliable risk assessment and traceable management of the deep foundation pit crack are achieved.

The invention provides an imaging method and system based on a polarization imaging lens and visual collaborative optimization, and a medium, and the method comprises the steps: integrating a four-way polarization filter array on the surface of an imaging sensor, and synchronously collecting a four-channel polarized light intensity image through single-frame exposure based on the imaging sensor; obtaining a three-channel RGB image, and inputting the four-channel polarized light intensity image and the three-channel RGB image into a polarization-RGB fusion neural network; polarization features and RGB features are extracted, heterogeneous information coupling optimization is carried out, and fusion features are obtained; constructing a polarization-color joint decoding network, carrying out polarization and RGB information collaborative decoding on the fusion features, training a polarization decoding process and a material classification task together based on an end-to-end joint optimization strategy, and outputting a material classification result; high-efficiency acquisition of original polarization information on a hardware level is guaranteed through the polarization filtering array; and an end-to-end joint training strategy is adopted to realize optimal decoupling and fusion of perception information.

The invention relates to the technical field of image recognition, in particular to a plate defect recognition and detection method based on an image recognition model. The method comprises the following steps: aiming at the technical problems of insufficient texture direction identification precision and difficulty in defect type distinguishing in a traditional plate surface detection method, the invention provides an intelligent detection solution based on polarization imaging. A multi-angle polarization measurement system is constructed through a fixed polarizer and a rotatable analyzer, and omnibearing polarization characteristic collection of the surface of a steel plate is achieved. A polarization intensity change model is established based on the Malus law, the polarization degree and polarization angle parameters of each pixel point are extracted, and a polarization parameter spatial distribution diagram reflecting the surface microstructure is constructed; through statistical analysis of polarization angle distribution characteristics, a rolling direction dominant angle is identified by adopting a peak detection algorithm, and accurate classification and severity evaluation of defects are realized in combination with anisotropy characteristic analysis.

The invention relates to the technical field of bionic navigation orientation and positioning, in particular to a carrier course angle measuring method based on polarized light imaging in a non-Rayleigh atmosphere. Comprising the steps of real-time sky polarized light imaging, atmospheric polarization mode resolving based on Mueller matrix imaging, non-Rayleigh polarization information interference removal, polarization orientation resolving based on Rayleigh scattering, space projection conversion and sun position information resolving. Alignment information of the sun in a carrier coordinate system is obtained by utilizing a Rayleigh scattering model and actual sky polarization imaging, and then a carrier course angle is obtained according to the orientation of the sun in a local geographic coordinate system, so that an orientation function is realized; meanwhile, the error influence caused by image registration and time-sharing rotation measurement of the multi-view-field polarization detector and the interference of environmental factors on a single point are avoided, the method can adapt to sunny and cloudy weather, is also applicable to cloudy and other non-Rayleigh weather, and is wide in application range.

The invention provides a video-level polarization spectrum imaging system and method. The system comprises an imaging unit, a polarization spectrum coupling unit, a data processing unit and an output unit, the imaging unit is used for imaging a target object on an imaging plane; the polarization spectrum coupling unit comprises a multi-channel spectrum modulation diaphragm and a micro-polarization focal plane array; the multi-channel spectrum modulation diaphragm is used for dividing incident light into a plurality of different spectrum channels; the micro-polarization focal plane array is used for converting the spectrum modulation light into polarized light of a plurality of different angles; the data processing unit is used for fusing the spectral information, the polarization information and the pixel information into multi-dimensional polarization spectral data; and the output unit is used for transmitting the polarization spectrum data to a computer. The system obtains spectrum information through a spectrum modulation diaphragm so as to distinguish the material composition and the chemical property of a target object; according to the system, polarization imaging is carried out through the micro-polarization focal plane array, and the detectability and the recognition rate of a target object can be improved.

The present invention discloses a device and method for semiconductor grain double-sided simultaneous fully equal optical path confocal imaging detection based on polarization separation imaging method. The device includes a CMOS or CCD polarization camera, a telecentric imaging lens, a polarization cube beam splitter, a right-angle image rotation prism, a semiconductor grain, and a transparent glass stage for placing the semiconductor grain, which are sequentially arranged in the optical path direction. The advantages of the device and method of the present invention are as follows: It can achieve double-sided simultaneous fully equal optical path confocal polarization imaging detection of the object to be measured, that is, △ = 0, without the need to use a large-depth-of-field telecentric lens to compensate for the optical path difference of double-sided imaging; the double-sided simultaneous polarization imaging detection device has a simple and compact structure and is easy to assemble and debug.

The invention discloses a broadband full-Stokes polarization imaging device based on non-orthogonal four-channel polarization beam splitting, which comprises a dielectric substrate, a non-orthogonal polarization beam splitting metasurface and a focal plane detection array, and is characterized in that the non-orthogonal polarization beam splitting metasurface is composed of a plurality of sub-wavelength unit structures; the sub-wavelength unit structure is a single-layer anisotropic medium nano-column array and is configured to perform intrinsic-loss-free non-orthogonal four-channel beam splitting on incident light and form four spatially separated focuses on the same focal plane, and polarization states corresponding to the four channels form a regular tetrahedron measurement base on a Poincare sphere; therefore, full Stokes parameter measurement is completed by four pixels; the sub-wavelength unit structure forms a super-pixel unit, and the super-pixel unit is aligned and integrated with a focal plane detection array in a two-dimensional array form to form a compact polarization camera facing focal plane segmentation. The device is simple in structure, easy to integrate with the CMOS front end, small in device size, low in power consumption and high in mass production feasibility.

The invention relates to a sea small target identification method based on polarization three-dimensional reconstruction, a computer and a ship, and belongs to the field of sea surface small target detection.The sea small target identification method comprises the following steps that S1, light intensity images of a sea surface detection scene in the four polarization directions of 0 degree, 45 degrees, 90 degrees and 135 degrees are collected through polarization imaging equipment, and polarization images are obtained; s2, calculating to obtain a linear polarization degree and a linear polarization angle based on the polarization image; s3, deducing the surface normal direction of the sea surface detection scene by adopting a lookup table method based on the linear polarization angle; s4, deducing the three-dimensional depth of the sea surface detection scene by using a shape polarization SfP technology according to the surface normal direction; s5, according to the three-dimensional depth, gradient values of the sea surface detection scene in the horizontal direction and the vertical direction are obtained through calculation; and S6, according to the gradient value, setting a threshold value of the gradient to distinguish the small target and the sea surface background in the sea surface detection scene, and completing the detection of the sea surface small target. The method is high in detection speed and high in precision.

The invention provides a wall surface damaged structure detection method based on polarization imaging, and relates to the field of image recognition, and the method comprises the steps: collecting damaged wall surface single-channel gray scale digital images in four polarization directions of 0 degree, 45 degrees, 90 degrees and 135 degrees to construct a polarization imaging data set; taking the 90-degree polarization direction image as input, and combining with a scale smoothing kernel and a non-Gaussian anisotropy kernel to obtain a multi-scale polarization structure enhanced feature map; generating a polarization angle feature and a polarization degree feature based on the polarization intensity image, and constructing a local structure reconstruction tensor; constructing a multi-direction geometric perception kernel by using the polarization degree features, extracting damage response features, and fusing the damage response features with the polarization intensity image to output a damage probability graph; and integrating the above steps into a unified wall surface damage structure detection model, and updating model parameters through an iterative optimization training strategy to realize accurate detection of a damage area.

The invention relates to the field of image processing, and discloses a wind turbine generator blade surface defect intelligent detection method based on image processing, which is used for improving the accuracy of wind turbine generator blade surface defect detection. Comprising the steps that pulse laser thermal excitation infrared thermal imaging and a multi-angle polarization light source array polarization imaging technology are fused, a bimodal data set is constructed, and thermal anomaly features of a dynamic thermal image sequence are extracted; and photoelastic stress characteristics are obtained through phase analysis and local energy filtering. Generating a fusion defect indication diagram by utilizing heat conduction blocking and stress concentration space coincidence strengthening mechanism fusion features, obtaining a defect probability distribution diagram, calculating a predicted risk value, matching a maintenance strategy library according to the risk value, outputting a maintenance scheme code, and generating a three-dimensional maintenance guide diagram. According to the invention, intelligent and accurate detection and maintenance guidance of blade surface defects are realized, and the comprehensiveness and accuracy of defect detection are improved.

The invention discloses an underwater image enhancement method and system based on binocular vision and polarization imaging. The method comprises the following steps: firstly, synchronously acquiring an orthogonal polarization image pair through a binocular polarization imaging unit, recovering scene depth information from the polarization image pair by using a binocular stereoscopic vision technology, and calculating a transmissivity graph; analyzing the polarization characteristic difference between target reflected light and backscattered light, establishing a polarization difference model, and separating background scattered light; solving an underwater imaging equation in combination with the depth information and a polarization difference model to obtain a preliminary restoration result of the target reflected light; and finally, further improving the image quality through the steps of multi-scale spectrum analysis, adaptive filter construction and post-processing by adopting a spectrum adaptive image enhancement technology. The system comprises a binocular polarization imaging unit, a calculation processing unit and an active illumination unit. According to the method, the problems of color distortion, low contrast and fuzzy details of the underwater image are solved, and the visual quality and usability of the underwater image are improved.

The invention provides a space target attitude optical detection device and method, and relates to the field of photoelectric detection and imaging, and the device comprises a Bessel polarization beam emission subsystem, a polarization characteristic and optical signal receiving subsystem, a polarization imaging subsystem, a timing subsystem and a control subsystem. The detection stability and accuracy are further improved through the self-healing property of the Bessel beam; through fusion of the visible light image and the polarization image, the target detection and identification capability can be enhanced, and the detection precision of the space target attitude is further improved.

The invention discloses a bionic visual positioning system and method based on multispectral perception and a storage medium, and the system comprises a front-end multidimensional optical perception module, a mesopic vision preprocessing module and a rear-end fusion perception and target positioning module, the front-end multi-dimensional optical sensing module is used for performing optical imaging, spectral decomposition and polarization imaging; the mesopic vision preprocessing module is used for carrying out image preprocessing and optical flow estimation; and the rear-end fusion perception and target positioning module is used for feature fusion and target identification and positioning. According to the bionic visual positioning mechanism, through a bionic compound eye structure + multispectral + polarization + optical flow fusion perception strategy, the perception bottleneck of a traditional visual system in a high-dynamic and complex illumination environment is effectively solved, and the bionic visual positioning mechanism has the advantages of high robustness, low delay, wide field of view, multiple modes and the like; the method is especially suitable for application scenes such as automatic driving, unmanned aerial vehicles, robots and the like with extremely high requirements on real-time performance and reliability.

The invention discloses an underwater polarization imaging device and method based on interference-free coded aperture correlation holography, and belongs to the field of computational optical imaging. According to the technical scheme, on the basis of an interference-free coded aperture correlation holographic system, two light paths are utilized, a target object hologram and a point spread function are synchronously collected through cross-polarization double light paths (0 degree and 90 degrees), after target information light is separated out through an underwater polarization imaging model, low-frequency background noise is filtered out through a nonlinear filtering reconstruction algorithm, and the target object hologram and the point spread function are obtained. And the phase-coded high-frequency object structure information is reserved, and the global estimation precision is optimized. According to the method, a complex optical path of traditional polarization imaging is simplified, the influence of local noise on polarization parameters is avoided, the image definition and the signal-to-noise ratio in a turbid underwater environment can be effectively improved, and the method is suitable for high-scattering scenes such as marine resource exploration, underwater robot navigation and military underwater target detection.

The invention belongs to the technical field of polarization imaging, and relates to a Sagnac broadband linear polarization imaging structure which comprises a second plane mirror, a first polarization beam splitter, a first polarization analyzer, a first imaging lens and a first focal plane array which are sequentially arranged on a first optical axis at intervals. The compensation plate is arranged close to the second plane mirror; the first plane mirror and the first polarization beam splitter are arranged on a second optical axis perpendicular to the first optical axis in a spaced mode and located on the side away from the compensation plate; the first blazed grating and the second blazed grating are reversely arranged at intervals and located on the light path between the first plane mirror and the second plane mirror, and the first blazed grating is close to the first plane mirror. The system provided by the invention is small in adjustment workload, high in accuracy, sufficient in shearing amount compensation, and good in broadband polarization imaging image precision and overall stability.

The invention discloses an end-to-end polarization hyperspectral image classification method and system, belongs to the technical field of deep learning and optical imaging, and solves the technical problems of low reconstruction process speed, limited precision, low information utilization rate in a classification process and weak feature extraction capability in the prior art. The method comprises the following steps: carrying out target shooting based on a snapshot type space coding hyperspectral polarization imaging system, and carrying out system coding on a shot image to obtain two-dimensional aliasing data; reconstructing the two-dimensional aliasing data into a polarization hyperspectral data cube by using the trained reconstruction network; training the classification network based on the polarization hyperspectral data cube to obtain a trained classification network; and performing joint fine tuning on the trained reconstruction network and the trained classification network to obtain a polarization hyperspectral image classification model, and performing polarization hyperspectral image classification by using the polarization hyperspectral image classification model. The method is used for realizing high-quality reconstruction and accurate classification of the polarization hyperspectral image.

The invention discloses a polarization imaging device and a polarization analysis method based on single-frame polarization collinear holography, and relates to the field of polarization imaging.According to the polarization imaging device, through combination of optical elements such as a half-wave plate, a Wollaston prism and a CCD camera, spatial multiplexing of a detector plane is carried out, and the polarization collinear holography is obtained. And four polarization collinear holograms can be simultaneously acquired by a single frame. According to the polarization analysis method, a Fourier transform auxiliary angular spectrum method is adopted to analyze an intensity distribution diagram of a polarization collinear hologram to estimate initial wavefront distribution, and an iterative finite support constraint selection method is adopted to solve the problem of twin images in the polarization collinear hologram. And further quantizing a complete Jones matrix and a complete Stokes parameter from the finally reconstructed complex amplitude information. The invention solves the problems of high complexity and long processing time caused by independent reconstruction of amplitude, phase and polarization in the traditional polarization holographic technology, and effectively solves the problem that the traditional off-axis polarization holographic imaging system limits the size of an imaging field of view.

The invention discloses a double-source multi-dimensional mass image self-adaptive compression and reconstruction method based on infrared identification-polarization screening, relates to the technical field of infrared polarization imaging and compressed sensing, and solves the problems that target detection is difficult, computing resources are limited and equipment lightweight is affected in the existing complex scene, and polarization characteristics are not sufficiently utilized in the existing compression and reconstruction method. The method comprises the steps that an image and target stacking fusion display system is constructed, a coaxial three-reflection infrared polarization double-source optical camera is used for imaging the display system, and earth scenery image data and radiation aircraft motion infrared and polarization images are obtained; a deep expansion reconstruction network is adopted to process infrared and polarization images, so that the moving target brightness channel quality and the target detection performance are improved in different compression ratio ranges, and the calculation complexity is remarkably reduced while the reconstruction precision and the calculation efficiency are considered.