206 results about "Polarization imaging" patented technology

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 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 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 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 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 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 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 bridge underwater safety detection method based on opto-acoustic dual modes, and relates to the technical field of underwater engineering detection, and the method comprises the following steps: obtaining water area particle size distribution and concentration data by using a multi-frequency acoustic suspended sediment concentration profiler, establishing an environmental parameter database, configuring a shipborne detection device according to the initial environmental parameter database, and detecting the underwater safety of a bridge according to the shipborne detection device. The method comprises the following steps: setting a three-axis stabilized platform carrying a polarization imaging camera and a multispectral LED array, deploying a multi-beam detection module and ADCP equipment, and establishing a unified coordinate system; multi-angle original intensity images are collected through a polarized optical imaging module, and multi-exposure fusion is carried out after processing is carried out through a mutual information minimization algorithm. An optical image and an acoustic point cloud are deeply fused by adopting a graph neural network, the detection precision is improved through dynamic weight optimization of an attention mechanism and a space-time registration algorithm, a multi-index fusion model is constructed based on fuzzy logic and a D-S evidence theory for adaptive early warning decision, upgrading from single-parameter alarm to multi-dimensional decision is realized, and the false alarm rate is reduced.

Embodiments can relate to systems and methods for generating hyperspectro-polarimetric images. The system can include a metasurface having a plurality of super pixels configured to encode spectral and polarization image data into spatial intensity distributions. The system can include an image capture device having at least one image sensor and a plurality of subsets of pixels configured to capture the spatial intensity distributions encoded by the metasurface. The system can include a processor in communicative connection with the image capture device. The processor can be configured to receive pixel data from the image capture device, generate captured image input data based on the pixel data, and access at least one computational reconstruction model configured to decode the captured image input data, and generate image output data based on the decoded captured image input data.

The utility model relates to the technical field of super-structure lenses, and particularly discloses a fully-integrated polarization imaging device based on a polarization beam splitting super-lens. The fully-integrated polarization imaging device based on the polarization beam splitting superlens comprises a nanopillar array, a transparent medium substrate and an image sensor, a nanorod array is arranged on one side surface of the transparent medium substrate; the image sensor is arranged on the other side, far away from the nanorod array, of the transparent medium substrate; the image sensor is provided with at least two pixel areas; the light beams output from the nano-pillar array pass through the transparent medium substrate and then are split and focused to the pixel area. According to the utility model, the nanorod array, the transparent medium substrate and the image sensor are integrated together, so that the interference of an external light path is reduced, the imaging stability and reliability are improved, the structure is compact, and the size is small. The nanorod array can accurately control polarization and phase of light, high-resolution imaging is achieved, and the problems that an existing polarization system is low in integration degree, large in size and low in resolution are solved.

The invention discloses a polarization degree and light intensity image fusion method based on bidirectional cross attention. The method belongs to the technical field of polarization imaging, computational imaging and image processing. The technical problems that in the prior art, dynamic balance between structural detail keeping and polarization saliency enhancing is generally difficult to achieve, and particularly under the condition of a complex background or a low signal-to-noise ratio, texture missing or noise artifacts are prone to occurring in a fusion result are solved. A fusion mechanism capable of establishing a two-way information interaction relationship between a light intensity image and a polarization degree image is provided, and further processing is carried out aiming at a noise problem, so that more stable and more reliable polarization fusion imaging is realized.

The invention belongs to the technical field of photoelectric detection imaging, and particularly relates to a dim target enhancement device and method based on range gating polarization imaging, the device comprises a transmitting system and a receiving system, the transmitting system comprises a pulse laser, a signal modulation circuit and a transmitting optical system; the pulse laser is used for emitting laser beams; the signal modulation circuit is used for generating a high-frequency modulation electric signal and modulating a laser beam emitted by the pulse laser; the emission optical system is used for focusing and adjusting the light beam direction and the emergent light polarization state; the receiving system comprises a receiving optical system, a focal plane polarization detector, a synchronous control board, a signal processing board, an image processing board and display control equipment; according to the device, the pulse illumination module, the time synchronization control module and the polarization imaging module are integrated, and an image definition evaluation algorithm is combined, so that synchronous acquisition and fusion of target polarization information and distance information are realized.

A building material defect visual detection system driven by artificial intelligence is characterized in that a multispectral imaging module is used for performing high-resolution image acquisition on the surface of a building material under different illumination conditions and wave bands, and comprises a visible light camera, a near-infrared camera and a polarization imaging unit; the texture, color, reflectivity and microstructure information of the surface of the building material can be synchronously obtained, the self-adaptive illumination control unit is linked with the multi-spectral imaging module, and the intensity, angle and spectral distribution of a light source are automatically adjusted according to the type of the material of the building material, so that the contrast ratio of a defect area and a normal area is enhanced. Through deep linkage of the multispectral imaging module and the self-adaptive illumination control unit, the system quickly identifies the material type through first frame image analysis or RFID reading at the beginning of detection, and calls an optimal illumination parameter combination from a preset strategy library, for example, low-angle polarized light is started for a metal plate to suppress glare, so that the material type can be quickly identified. Near-infrared penetration of the composite panel is enhanced to reveal internal debonding.

The invention discloses a self-reference three-phase polarization imaging system and method based on an integrated photon neural network, and the method comprises the steps: setting a fixed phase difference in a silicon-based photon chip to achieve three-phase projection and single-port reading, outputting a three-phase interference signal at the same time, obtaining all phase and polarization information in one-time imaging to achieve full-polarization measurement, and carrying out the single-port reading of the silicon-based photon chip; according to the invention, the effective resolution ratio is consistent with that of an original detection array, power and temperature drift is eliminated, single-frame acquisition has no time sequence error and motion artifact, a stable and compact single-port self-reference reading mode is realized through a three-phase interference measurement architecture constructed on a chip in combination with channel normalization and rotation correction, and complete Stokes parameters and polarization information can be measured. According to the system provided by the invention, the MZI is adopted, so that the phase bias can be flexibly adjusted, and different bandwidth requirements can be met by adjusting an interference path. The silicon-based photon platform has a broadband response characteristic and can cover a visible wave band, so that the device can keep stable interference performance in a communication wave band.

The invention discloses a bamboo defect detection method based on computer vision, which comprises the following steps: S1, acquiring a bamboo surface image to obtain polarization intensity image data; s2, preprocessing the polarization intensity image to generate a polarization degree image, a polarization angle image and a cross parallel difference image; s3, direction candidates are extracted based on the polarization angle and the difference information, and a bamboo grain direction field is obtained; s4, performing streamline segmentation by using the bamboo grain direction field, dividing the image into superpixels and expanding the superpixels into a one-dimensional sequence; s5, inputting the one-dimensional sequence into the double-flow state space model, and performing parallel processing on longitudinal and transverse branches to output candidate results; s6, detecting rhythm units between adjacent bamboo joints, calculating multi-class consistency indexes and generating an abnormal response diagram; and S7, fusing the defect candidate result and the abnormal response diagram, and finally outputting a final detection result. According to the method, efficient automatic detection of bamboo surface defects is realized through polarization imaging and double-flow state space modeling.

The invention discloses a multi-angle polarization imaging method and system for micro scratches on the surface of an aluminum plate, and the method can convert the physical difference which cannot be distinguished by naked eyes and a common camera into a significant gray difference on an image through calculating a Stokes parameter and constructing a polarization feature image, thereby forming a high-contrast detection image, and improving the detection precision. The detection sensitivity and the signal-to-noise ratio are obviously improved; the method is extremely sensitive to polarization state disturbance caused by changes of the surface microstructure of the aluminum plate, interference of a uniform background can be effectively restrained, and therefore the probability of missing detection and false detection is remarkably reduced.

The invention relates to the technical field of unmanned aerial vehicle water conservancy exploration, in particular to an unmanned aerial vehicle water conservancy exploration intelligent system and method based on multi-source data fusion, and the system comprises an image collection module which is used for collecting water surface information and outputting an original image signal through an airborne multispectral polarization imaging unit; and the image preprocessing module is used for executing geometric correction based on the original image signal so as to output a corrected image signal. According to the method, a fusion weight calculation mechanism is introduced among multiple spectral bands, and the target detection depth and the spectral signal-to-noise ratio model are combined, so that the adaptive weight fusion of the multi-source spectral data is realized, and the complementarity of information of different bands is effectively enhanced; the spectrum contrast ratio, the detail fidelity and the target prominence of the generated exploration image are obviously improved, and higher accuracy is provided for quantitative analysis and feature recognition of the water body environment.

The first aspect of this invention provides a polarized illumination system for providing illumination light to biological tissue, comprising at least one laser, a lens, and a polarization-maintaining fiber. The second aspect provides a polarized imaging system for receiving light reflected or scattered by biological tissue after illumination, comprising an analyzer, an imaging lens, and a photosensitive device. The third aspect provides another polarized imaging system, comprising an imaging lens, a polarizing beam splitter, and a photosensitive device. The fourth aspect provides an endoscope system comprising the aforementioned polarized illumination system and polarized imaging system. This invention utilizes the different depolarization characteristics of scattered light at different depths to achieve separate imaging of specular reflection light, surface scattered light, and deep scattered light, greatly improving the visibility of mucosal surface and deep mucosal information, and significantly enhancing the visibility of various mucosal lesions.

This invention discloses a method and system for intelligent weather image acquisition and recognition, specifically relating to the field of weather image recognition technology. It involves continuous polarization imaging of the sky region while simultaneously acquiring spatial attitude and temporal information; preprocessing the polarization image data and extracting Stokes parameters to obtain standardized sky polarization light field data; then using a theoretical Rayleigh scattering model to analyze the polarization degree distribution pattern at different wavelengths to obtain theoretical sky polarization pattern characteristics; based on the polarization light field data, identifying the spatial distribution of actual clouds, aerosols, and haze in the sky to form actual atmospheric composition distribution characteristics; extracting anomalous polarization regions through difference analysis between theoretical and actual characteristics, and accurately classifying halo polarization anomalies and scattering polarization anomalies based on geometric and spectral characteristics; generating ultralocal weather change early warning signals based on the classification results; thus improving the accuracy and real-time performance of early warnings for ultralocal anomalous weather phenomena.

An integrated semiconductor photodetector and its fabrication method are disclosed, relating to the field of integrated photodetector technology. This invention addresses the problems of existing detectors, such as limited functionality, insufficient responsivity, low integration, and difficulty in achieving synergistic miniaturization and high performance improvement. The method involves depositing a Cr conductive layer on the front side of a superlens substrate, uniformly coating the conductive layer surface with photoresist, pre-baking to prepare a dot-matrix mask pattern, developing and fixing followed by Ni deposition, and then stripping to obtain the Ni dot-matrix mask. ICP etching is used to remove the surface conductive layer, and a nanopillar array superlens structure is then fabricated. A photodetector layer is transferred to the back side of the substrate. Positive photoresist is spin-coated onto the substrate surface, pre-baking to prepare a source / drain electrode pattern, developing and fixing followed by sequential deposition of an adhesion layer metal and an electrode body metal layer. The metal layer in the non-electrode region is then stripped to obtain the metal electrode in contact with the photodetector layer, completing the detector fabrication. This invention can be applied to polarization imaging, space remote sensing, airborne detection, and optical communication.

This invention belongs to the field of industrial inspection technology. It discloses a defect detection method and system based on honeycomb catalyst stacking. By acquiring omnidirectional image data of the honeycomb catalyst stacking through multi-angle polarization imaging technology, and calculating pixel-level polarization parameters to construct a global polarization feature map, it achieves accurate differentiation between intrinsic porous structures and suspected defects. A blind zone identification and virtual view reconstruction mechanism is introduced to solve the problem of blind zone detection at stacked edges. A layered reflectivity compensation function is used to eliminate optical interference in overlapping areas between layers. Texture features are extracted through multi-scale morphological filtering, and multi-dimensional feature fusion is performed by combining polarization features, edge continuity indicators, and corrected reflection intensity to establish a high-precision defect discrimination model. For low-confidence areas, detection parameters are dynamically adjusted and iteratively optimized to form an adaptive detection closed loop. This invention improves detection accuracy and reliability, and can accurately output the location, type, and severity of defects.

The invention belongs to the technical field of intelligent perception and agricultural informatization, and particularly relates to a non-contact soil humidity detection system and method based on polarization imaging. The problems that an existing traditional soil humidity detection method is poor in real-time performance and strong in dependence on illumination, and a contact type detection system and method cannot represent a real physical state in a characterization mode and cannot conduct mobile measurement are solved. The system specifically comprises an illumination control unit, a polarization imaging unit connected with the illumination control unit through a data bus, an attitude measurement unit connected with the polarization imaging unit through a data bus, and a data processing and analysis unit connected with the polarization imaging unit and the attitude measurement unit through data buses. And the display and output module is connected with the data processing and analyzing unit through a data bus.

The invention belongs to the technical field of polarization imaging, and particularly relates to a polarization dimension reduction imaging method, system and device based on principal component analysis, and the method comprises the steps: splitting a 0-degree polarization image from an original polarization image as a parallel polarization state image, and splitting a 90-degree polarization image as a vertical polarization state image; respectively extracting a background area and a target area; estimating the background intensity at the infinite distance in the original polarization image and the polarization degree of back scattering light; performing principal component analysis on the data matrix, and performing PCA dimension reduction and feature extraction to obtain a first feature value and a second feature value; and taking the first characteristic value as the total light intensity of the original polarization image, taking the second characteristic value as the light intensity difference between the parallel polarization state image and the vertical polarization state image, calculating the transmissivity of the original polarization image, and correcting the inherent radiation of the target area to obtain a final imaging image. According to the invention, the problem of light intensity estimation deviation at infinity in polarization imaging and the problem of transmissivity distortion caused by data redundancy are solved.

The application discloses a global polarization image recovery method without background prior, comprising the following steps: S1, different angle polarization images are obtained by rotating a polarization mirror in front of a camera; S2, a polarization imaging proxy model is established by fusing backscattering polarization degree and a polarization angle; S3, under the constraint of EME, an original image is segmented and sampled, and local parameter optimal values of the model are solved; S4, a robust polynomial curved surface fitting method is designed based on a least square method, and global parameters of variables in the model are obtained; S5, according to the relationship between model parameters and backscattering, a method for solving an infinite far atmospheric light value based on model parameters is proposed; and S6, based on the polarization imaging proxy model, the global model parameters obtained in S4 and the infinite far atmospheric light value solved in S6 are substituted into the proxy model in S2, and a clear non-fog image is inversed.

An end-to-end polarization hyperspectral image classification method and system, belonging to the field of deep learning and optical imaging technology, solves the technical problems of slow reconstruction speed, limited accuracy, low information utilization, and weak feature extraction capabilities in existing technologies. The method involves capturing images of a target using a snapshot-style spatially coded hyperspectral polarization imaging system. The captured images are then encoded by the system to obtain two-dimensional aliased data. A trained reconstruction network is used to reconstruct the two-dimensional aliased data into a polarization hyperspectral data cube. The classification network is then trained based on the polarization hyperspectral data cube to obtain a trained classification network. The trained reconstruction network and the trained classification network are jointly fine-tuned to obtain a polarization hyperspectral image classification model, which is then used to classify polarization hyperspectral images. This invention achieves high-quality reconstruction and accurate classification of polarization hyperspectral images.

This invention provides an underwater long-range ultra-high-definition polarization imaging system and method, relating to the field of data processing technology. The system comprises, from bottom to top, an optical sensing layer, a physical computing layer, an AI inference enhancement layer, an adaptive control layer, and a decision optimization layer, forming a hierarchical control structure where data flows upward and strategies are passed downward. The optical sensing layer employs a non-uniform polarization sampling array, a spectral polarization joint coding unit, and a dual-modal illumination unit to simultaneously acquire intensity, multi-directional polarization, multispectral images, and depth information. The physical computing layer calculates polarization parameters in real time and inverts water turbidity within the image signal processor hardware pipeline. The AI ​​inference enhancement layer embeds the physical model as a differentiable module into a neural network to achieve scattering suppression and detail enhancement. The adaptive control layer adopts a time-scale separated dual-closed-loop architecture. This invention can adapt to different water turbidity levels, output ultra-high-definition images in real time, and significantly improve the quality of long-range underwater imaging.

The invention relates to the technical field of industrial vision, in particular to an industrial robot grabbing recognition method and system based on deep learning, and the method comprises the steps: firstly, collecting an initial depth map of a target high-reflection gear based on structured light polarization imaging; generating an initial point cloud based on the initial depth map; acquiring a three-dimensional contact point to obtain a corrected point cloud; fusing the corrected point cloud and the multi-view initial point cloud data to construct a high-fidelity gear point cloud; inputting the high-fidelity gear point cloud into a point cloud derived feature generation network, and extracting a mass center position probability graph and a surface friction coefficient distribution graph; calculating a stability margin, and constructing an action value function in combination with the geometric score; constructing a grabbing evaluation network through U-Net, projecting the high-fidelity gear point cloud into a two-dimensional image, generating a pixel-level grabbing and pushing action value map, and forming a joint strategy space; and selecting an optimal grabbing action and an optimal pushing action from the joint strategy space for execution. According to the high-reflective metal gear grabbing device, high-reliability grabbing of high-reflective metal gears in a tightly stacked material box can be achieved.

The invention discloses a large-view-field polarization detection super lens based on an instrument matrix, and relates to the technical field of micro-nano optics and polarization detection. Comprising four sub-lens structures. A secondary phase distribution and global optimal phase screening strategy is adopted in the structural design, and a calibration method based on an instrument matrix is provided. According to the structure, different polarization components are spatially separated and focused, and polarization response of a system at different incident angles is accurately calibrated by using an instrument matrix, so that polarization detection in a large view field range of 120 degrees is realized. The invention has the characteristics of super compactness and high precision, and can be used for designing polarization detection, polarization imaging and the like in a large view field range.

This application relates to the field of tunnel construction safety monitoring technology, and discloses an intelligent identification method for water seepage risk at the tunnel face in water-rich clay layers. This method controls an active infrared excitation module to project radiation in an oblique incidence manner, uses polarization imaging to acquire sequences and calculates Stokes vectors; calculates the degree of linear polarization based on the Stokes vectors and performs threshold segmentation to generate a water film distribution mask; uses the Stokes vectors to construct a polarization angle field reflecting local normal changes as texture features, solves the optical flow constraint equation to obtain the instantaneous velocity field; performs frequency domain transformation on the velocity field, extracts the proportion of low-frequency creep energy through power spectral density analysis, and determines the soil rheological softening factor; finally, combines the water film distribution, rheological softening factor, and instantaneous velocity field to construct a mudslide risk index. This invention utilizes polarization texture reconstruction to solve the problem of weak texture tracking and eliminates elastic vibration interference through frequency domain energy decoupling, effectively improving the accuracy of mudslide risk identification.

The application discloses an underwater polarization imaging method based on Stokes vector anisotropic diffusion, and has the characteristics that the method comprises the following steps: collecting multiple underwater polarization images with different polarization directions; performing brightness normalization processing on the collected underwater polarization images; constructing a Stokes vector based on the brightness normalized underwater polarization images; approximating the diffusion process of the Stokes vector in different regions by calculating the gradient difference of four directions, and estimating the gradient required in different regions according to the diffusion process; introducing a Perona-Malik anisotropic diffusion model, constructing a Stokes vector diffusion model by Euler discrete approximation based on the gradient, and obtaining the diffused Stokes vector; calculating the polarization angle and the degree of polarization according to the diffused Stokes vector; performing depolarization compensation correction on the degree of polarization to obtain the corrected degree of polarization; estimating the backscattering light based on the polarization angle and the corrected degree of polarization; and obtaining the final imaging result based on the estimated backscattering light.