529 results about "Image contrast" patented technology

The invention relates to the technical field of optic nerve detection, in particular to a plastic particle quality detection method and system based on optic nerves, and the method comprises the following steps: capturing a plastic particle image through an industrial camera, carrying out the balance processing of the image contrast, extracting the brightness and color features of the image, and removing the noise interference; and adjusting the image brightness and optimizing the particle edge to obtain a particle optimization image. According to the invention, the accuracy and automation level of plastic particle quality detection are greatly enhanced by using the visual neural network and the image processing technology, and the edge and shape features of the particles can be accurately extracted from a complex background by automatically adjusting the image contrast and brightness and applying an advanced edge detection algorithm. According to the method, the visual quality of the image is optimized, tiny flaws such as cracks and bubbles of the particles can be effectively recognized and analyzed, and more detailed data support can be provided compared with a traditional method by accurately calculating the surface roughness and texture uniformity of the particles.

A data-centric system for analyzing agricultural crops, consisting of: a data acquisition module configured to capture images of agricultural fields using cameras, unmanned aerial vehicles (UAVs) or sensors, with the sensors collecting data on soil moisture, temperature, light, humidity and pH; a data preprocessing module configured to: resize the acquired images to a standardized dimension suitable for input to a deep learning model; apply noise reduction using a Gaussian filter; improve image contrast through histogram equalization; and perform image magnification through rotation, reflection, and scaling transformations; a feature engineering module configured to extract the following features: color features, which include color histograms, mean, and standard deviation of color channels; texture features using Gray-Level Co-occurrence Matrix (GLCM) properties, which include contrast, dissimilarity, homogeneity, energy, angular moment (ASM), and correlation; shape features, which include contour area, perimeter, aspect ratio, and roundness; and other features, which include the green pixel ratio and edge density; a classification module configured to: implement deep learning-based classification models selected from the group consisting of Support Vector Machine (SVM), Artificial Neural Network (ANN), Convolutional Neural Network (CNN), ResNet18, Random Forest (RF), SegNet, VGGNet, Naive Bayes (NBG), Decision Tree (DT), K-Nearest Neighbors (KNN), and DeepLab; detecting and classifying weed species in the images of agricultural fields; and diagnosing plant diseases based on the features extracted from the images of agricultural fields; an output module comprises a user interface configured to display the classification and recognition results; and a recommendation module configured to suggest treatment solutions for diagnosed plant diseases through the output module's user interface.

The invention discloses a single image super-resolution reconstruction method and system based on wavelet transform and cross-domain feature fusion. According to the method, firstly, a low-resolution RGB image is mapped to a high-dimensional feature space through a shallow feature extraction module; performing up-sampling and discrete wavelet decomposition on the features by using a wavelet feature mixing module to obtain multi-band features; low-frequency and high-frequency depth features are respectively extracted through a double-branch structure, cross-domain fusion is realized by means of a deformable cross attention mechanism, and the feature expression ability is enhanced in combination with residual connection; and finally, reconstructing a high-resolution image through convolution, up-sampling and regularization processing. In the training process, a pixel-level loss function is adopted to optimize network parameters, the multi-frequency-domain feature sensitivity is effectively improved, texture and structure information is balanced, the image contrast, definition and structural integrity are improved, and high-quality real-time super-resolution reconstruction can be achieved.

The invention discloses a cross-modal retrieval model optimization method and system based on illusion enhancement. The method comprises the steps that a similar illusion text generation module generates a similar illusion text based on an original training data set, a language model and a prompt template; the data set construction module combines the illusion-like text and the original image-text pair into an enhanced training data set; the fine tuning optimization module encodes image, original text and illusion-like text features through an image or text encoder, calculates text-to-image contrast loss and image-to-text illusion enhancement contrast loss, and optimizes model parameters of the encoding adapter and the text encoder in combination with an improved contrast loss function. According to the method, the problems of low precision and poor efficiency caused by illusion interference of a multi-modal model in intelligent driving scene retrieval are solved, and the accuracy of cross-modal retrieval is remarkably improved by utilizing illusion to generate data enhancement and dynamic loss constraint.

The invention provides an infrared image frequency division enhancement method based on a diffusion model, and the method comprises the steps: firstly carrying out the multi-scale frequency domain decomposition of an infrared image through discrete wavelet transform, and dividing the image into a low-frequency component and a high-frequency component; in the low-frequency component processing process, a diffusion equation is constructed in combination with a thermal radiation physical model, and discretization solution is performed by using a finite difference method. The high-frequency component is converted from a space domain to a frequency domain through Fourier transform, energy distribution of the high-frequency component is analyzed, and a diffusion probability function is adjusted. In the fusion stage, according to the signal-to-noise ratio of the local area of the image, the fusion weight of the low-frequency component and the high-frequency component is dynamically adjusted, the processing results of all the local areas are integrated in a weighted fusion mode, and finally the high-quality enhanced infrared image is generated. The method has excellent performance in improving image contrast, definition and detail performance, is suitable for the fields of security monitoring, military reconnaissance, industrial detection and the like, and can effectively improve monitoring, analysis and detection effects.

The invention discloses a sapphire substrate automatic defect detection and classification method based on visual inspection, and relates to the technical field of visual inspection and image processing, and the method comprises the steps: carrying out the adaptive threshold segmentation and phase consistency detection of a multi-modal image data set, and carrying out the fusion, and obtaining a joint defect probability graph; extracting defect candidate areas from the joint defect probability graph, and generating unified feature representation; inputting the unified feature representation into a double-branch neural network, performing fusion and classification through a cross attention mechanism, outputting a defect classification result, performing reconstruction by using a NeRF algorithm, outputting a defect three-dimensional shape, and generating a three-dimensional shape map; and generating a defect thermodynamic diagram based on the three-dimensional morphology map, performing space-time correlation analysis in combination with equipment process parameters, and outputting a sapphire substrate defect process analysis report. According to the method, the image contrast and the defect characterization capability are improved, so that the comprehensiveness and the sensitivity of detection are remarkably enhanced.

The invention discloses a method for measuring the particle size of metal powder by using a scanning electron microscope, which comprises the following steps: step 1, sample treatment: adhering a small conductive adhesive tape on a sample table, sprinkling metal powder on the adhered conductive adhesive tape, and purging to obtain the conductive adhesive tape adhered with the metal powder; step 2, parameter setting: setting acceleration voltage, working distance, diaphragm, image contrast, brightness, magnification times and scanning speed of a scanning electron microscope to observe the conductive adhesive tape adhered with the metal powder obtained in the step 1 to obtain an electronic image of metal powder particles; and processing and calculating the electronic image of the metal powder particles by using software to obtain the average particle size of the metal powder particles. According to the method disclosed by the invention, a solvent is not needed to disperse the sample, pollution of the solvent to the sample is effectively avoided, and the result is more accurate than that of directly measuring the particle size by using length software.

The invention discloses an underwater image enhancement method based on color channel unit compensation amount, and is applied to the technical field of underwater image processing. Comprising the following steps: respectively compensating and correcting color cast of attenuated red, green and blue channels of an underwater image by using a color channel compensation method of unit compensation dosage to obtain corrected images of the three channels; carrying out adaptive platform histogram equalization on the corrected image to realize gray scale extension and redistribution of pixel values; for a brightness channel, using a CLAHE algorithm to improve image contrast, using a GUM algorithm to enhance details, and using a Gamma correction algorithm to improve local brightness; and performing multi-scale fusion to obtain a final output enhanced image. According to the method, the quality of the underwater image is remarkably improved, the method has important practical significance in the application fields of underwater target tracking, recognition, positioning and the like, and more reliable visual support can be provided for ocean resource development and scientific research.

The invention relates to the technical field of vision assistance, and discloses an anesthesia puncture positioning method and system based on vision assistance, and the method comprises the steps: accurately positioning an anesthesia puncture point through multi-view image fusion, hyperspectral image processing, image preprocessing, illumination equalization, edge enhancement, depth feature extraction and the like. The method comprises the following steps: firstly, constructing an image coordinate system, collecting a plurality of camera images, and obtaining a fused clear image through a designed multi-view fusion algorithm and distance calculation; and in combination with a hyperspectral image fusion algorithm, the image quality is further improved. Then, residual mapping filtering denoising and local histogram enhancement are used for illumination equalization, and the image contrast and edge details are enhanced; a convolutional neural network is adopted, interest point detection is carried out, a Hessian matrix is utilized to describe image second-order changes, and local depth features are extracted. And accurate positioning of an anesthesia puncture point is realized through a weighted soft voting classifier and a dynamic threshold method.

The invention relates to the field of cultural relic image recognition, in particular to a text image enhancement method based on multi-scale feature fusion and a residual attention mechanism, and the method comprises the steps: obtaining a historical material data set, marking a text region in the historical material data set, and constructing a real text image data set; generating an image construction synthesis data set with the same format as the historical material text region; introducing a plurality of noise types into the synthesized data set to simulate possible problems of an actual old text image; an improved U-Net network is provided for text image enhancement so as to better learn a mapping relation between a real image and a degraded text image; a multi-scale feature perception and extraction module is adopted to extract feature information in the image so as to improve the image contrast and solve the noise problem; the extracted features are further processed through a residual attention module, and important areas in the image are effectively concerned; the image features are further optimized through a feature enhancement module, and image details and contrast are enhanced; the image denoising effect of the text extraction model for the historical materials is better than that of an existing model.

The invention discloses an image edge feature enhancement correction fusion method based on a guide filter, and aims to solve the problems that detail features of a low-illumination visible light image and an infrared image are not obvious, focusing edge information is not clear, and registration of a multi-focus image is wrong. The invention provides an edge feature enhancement correction fusion method based on a guide filter. In the illumination enhancement stage, a visible light image is divided into a base layer and a detail layer through a guide filter, and the image contrast and detail information are enhanced. For an infrared image, an infrared background is reconstructed by using a quadtree decomposition and Bezier interpolation method, unclear focusing edge feature information is extracted, and the visibility of image details is enhanced. And finally, reconstructing the two processed images by using a multi-scale weighted gradient method, and performing fusion by calculating large-scale and small-scale weight scales.

The invention discloses a gasket structure capable of eliminating real-shot stray light, which comprises the following steps of: S1, designing a chamfering structure, namely, accurately setting the thickness of an ultra-thin gasket to be 0.022 / 0.033 mm, manufacturing the chamfering structure in an inner hole, and performing rigorous optical simulation and experimental optimization; s2, material selection and surface treatment are coordinated, material screening focuses on the characteristics of low reflectivity and high optical stability, and optical-grade polycarbonate has good optical transparency, low refractive index and excellent mechanical performance. The light propagation path is changed through unique design, and the influence of stray light on imaging is reduced to the maximum extent. By effectively eliminating the stray light, the image contrast of the imaging system is improved, the definition is obviously improved, more image details can be clearly presented, and the application scene requirement harsh for the imaging quality is met. The reduction of stray light interference helps to reduce the noise level of an optical system, improves the stability and reliability of the system, reduces the equipment fault and maintenance frequency caused by the stray light problem, and reduces the use cost.

The invention discloses an image enhancement and visual SLAM (Simultaneous Localization and Mapping) method and system aiming at a low-illumination scene. The method comprises the following steps: firstly, judging by utilizing an image brightness detection algorithm, and carrying out image brightness adjustment on an image with abnormal current brightness; then carrying out image brightness adjustment based on a mean value adaptive Gamma value, calculating Gamma correction indexes corresponding to different low-illumination images with abnormal brightness, and carrying out brightness correction on the different low-illumination images according to the Gamma correction indexes; and finally, carrying out image contrast adjustment based on a CLAHE algorithm, wherein the CLAHE algorithm is adopted to enhance the contrast of the image after the mean value self-adaptive Gamma correction. According to the method, the problems of poor positioning precision, low robustness, poor map quality and the like of a visual SLAM system in a low-light environment in the prior art are solved. According to the method, deep learning and a traditional visual SLAM framework are combined, image performance is changed through an image enhancement method to improve image quality, and a feature point extraction mode and matching precision in a low-light environment are improved.

The invention provides a multi-scale pyramid weighted fusion underwater image enhancement method based on double prior, and the method comprises the steps: obtaining a degraded underwater image, and carrying out the global and local cooperation body color calibration of the degraded underwater image; decomposing the color correction image into a base layer, a detail layer and a noise layer by adopting a variational decomposition algorithm; performing spectral prior and transmissivity loss constraint on the base layer image to obtain a defogged image; fusing the detail layer image and the noise layer image to obtain a filtered image; performing enhancement processing on the filtered image by adopting a nonlinear mapping and contrast enhancement strategy to obtain an enhanced image; performing multi-level feature integration and reconstruction on the defogged image and the enhanced image by adopting a multi-scale pyramid adaptive weighted fusion method to obtain an underwater image with natural color and high visual definition; according to the method, the traditional image processing and variational optimization thought is combined to effectively correct the color deviation of the degraded underwater image, the image contrast and the detail definition are improved, and the visualization effect of the underwater image is improved.

The invention discloses an overexposure and underexposure image enhancement method, device, equipment and medium, and the method comprises the steps: enabling an image decomposition network to respectively receive a first illumination image and a second illumination image through employing two encoder-decoder networks sharing the weight, and extracting multi-scale illumination distribution information through multi-scale connection; the image reconstruction module outputs a corresponding reflectivity component and a brightness component, the image enhancement network adopts an image enhancement sub-network to adjust illumination distribution and suppress noise based on the reflectivity component and the brightness component, and the image reconstruction module multiplies the adjusted reflectivity component with an illumination image element by element and outputs an enhanced image. According to the method, the technical effects of effectively recovering image details, reducing noise interference and improving real-time performance under the overexposure or underexposure condition are reflected, the method is particularly suitable for complex environments such as power equipment monitoring, the contrast ratio of the enhanced image is better, artifacts are fewer, the calculation burden is reduced, and efficient batch processing prediction is supported.

The invention provides an embedded image enhancement method and system for a large-target-surface image sensor. According to the method, temperature gradient distribution and environment humidity data of a large-target-surface image sensor are collected, and an environment parameter matrix and a temperature noise mapping model are constructed in an associated mode; and correcting optical distortion of the incident light signal by using the liquid crystal modulator array, and outputting an original image. Then through a double-path module, a main path extracts noise features, and an auxiliary path separates color shift features caused by humidity; and verifying the physical consistency of the characteristics based on a thermodynamic equation and screening effective data. And finally, dynamically optimizing the image contrast, the dynamic range and the target details by the embedded processor, and outputting an enhanced image. Through environmental parameter modeling, dual-path feature separation and physical verification, temperature and humidity interference is effectively suppressed, and detail enhancement and color fidelity of the large-target-surface image sensor are improved.

The invention discloses a road target detection method and device based on bimodal feature fusion and weak light enhancement, and relates to the field of target detection. And the weak light enhancement target detection model performs enhancement and target detection on the road image under the weak light condition. And the bimodal target detection model performs target detection on the road image under the normal light condition. The weak light enhancement target detection model carries out feature extraction through a multi-scale feature attention module, and carries out image feature brightness enhancement through a brightness enhancement module. The image quality is improved, the image contrast is kept, and the target detection accuracy of the weak light image is greatly improved. The bimodal target detection model adopts a double-branch training strategy, the rich capture capability of visible light on target color and texture features under a normal illumination condition is fully exerted, the advantage of a target contour is highlighted in combination with the penetration characteristic of infrared light, and the target recognition capability under the conditions of complex background, shielding and long-distance blurring is remarkably improved.

The invention provides a weak light environment image contrast improvement method for a robot welding path, and relates to the field of image processing, and the method comprises the steps: firstly improving an original compression mode based on a brightness single factor through a contrast perception compression factor; secondly, a low-light image enhancement color space based on a polar coordinate structure is introduced, and a contrast perception compression factor is combined to construct a low-light image enhancement color space with a space decoupling capability; a welding path structure response diagram is constructed by fusing the direction consistency entropy and the direction responsivity, the sensitivity to the structural characteristics with directivity and coherence such as the welding path is enhanced, and the accuracy and robustness of structure extraction are improved; and finally, through combination of the robot welding path image after color enhancement and the welding path structure response diagram, background noise interference is effectively inhibited while welding edge details are highlighted, accurate enhancement of the welding path image is realized, the welding edge details are enhanced, and the definition and the structure continuity of the image are improved.

The invention provides a long-depth-of-field integrated imaging display, a long-depth-of-field integrated imaging method and a long-depth-of-field integrated imaging system. The long-depth-of-field integrated imaging display comprises an image processor, a display, a microlens array and a quartic phase mask, the pupil position of each micro lens of the micro lens array is correspondingly integrated with a quartic phase mask; the image processor is used for acquiring a to-be-displayed image and constructing an element image array; performing inverse convolution processing on each element image in the element image array based on a preset phase mask function, and constructing a preprocessed element image array matrix; performing negative value processing on the preprocessed element image array matrix to obtain a preprocessed element image array; and the display is used for displaying the preprocessed element image array, so that the preprocessed element image array generates a long depth-of-field image through the micro-lens array and the quartic phase mask. According to the invention, defocus blurring is effectively suppressed without introducing an extra complex optical structure, a long depth-of-field image is generated on the premise of ensuring the image contrast, and the integrated imaging display quality is improved.

A multi-beam charged particle system and a method of operating a multi-beam charged particle system can provide improved image contrast. The multi-beam charged particle system comprises a filter element or an active array element in a detection system, which can provide improved, anisotropic image contrast. The disclosure can be applied for applications of multi-beam charged particle system, where higher desired beam uniformity and throughput may be relevant.

This disclosure provides microscope-based systems and methods that significantly improve image contrast and have better vertical / horizontal resolution. This disclosure uses confocal microscopy in replacement of conventional fluorescent microscopy, for example, adding a spinning disk or a confocal microscopy scanning unit on the prior invention. Using a spinning disk vastly improves the speed of image acquisition (allowing for imaging of fast dynamic processes and live specimens), and considerably reduces photo damage. Disclosed herein are some embodiments to demonstrate how to set up the spinning disk microscopy system for image-guided illumination and photolabeling.

The invention relates to an automatic blood vessel segmentation method and system for a CT image. The method comprises the following steps: acquiring an enhanced CT image and a plain-scan CT image at the same position; inputting the enhanced CT image to a pre-trained first blood vessel automatic segmentation model to obtain a first blood vessel segmentation result; and registering the enhanced CT image and the plain-scan CT image to mark and map the first blood vessel segmentation result to the plain-scan CT image so as to obtain a second blood vessel segmentation result. The method has the advantages that a deep learning model (such as a registration network based on U-Net or Transform) is utilized to calculate a deformation field, and in combination with multi-scale feature enhancement and regularization strategies, the precision and stability of enhanced CT and plain-scan CT image registration are improved, and artifacts and local distortion in the deformation field are reduced; in order to solve the problem that the contrast difference of a plain scanning CT image and an enhanced CT image is significant, a compensation strategy of multi-modal texture and intensity distribution is introduced, such as adversarial loss and structural similarity index (SSIM) optimization, so that registration is more robust among different modals.

The invention discloses an AI algorithm-based apparent defect detection method, system and device, and relates to the technical field of apparent defect detection. The appearance defect detection method based on the AI algorithm comprises the following steps: detecting defects of a high-reflection area; shadow area defect detection; and detecting defects of the sunken area. According to the method, the appearance defect area of the metal product to be detected is obtained through the AI visual sensor, the identification accuracy of the defect area in the high-reflection area is quantified, image contrast optimization judgment is carried out, then the area identification accuracy in the shadow area is quantified, and angle light source optimization judgment is carried out; and finally, the identification accuracy of the defect area in the concave area is quantified, and camera parameter optimization judgment is carried out, so that the identification accuracy of the specified appearance defect feature in the concave area corresponding to the high-transmittance and high-reflection metal is realized. The problem that in the prior art, high-transmittance and high-reflection metal is low in appearance defect feature recognition accuracy in a corresponding concave area in the appearance defect detection process is solved.

The invention relates to the technical field of medical robots, in particular to a three-dimensional reconstruction method and system for a nasal jejunum catheterization robot. The method comprises the following steps: acquiring endoscope image data in real time through an endoscope camera and a data transmission channel, and performing preliminary denoising and enhancement; a parallel processing architecture is adopted, and the endoscope image data is divided, subjected to image denoising and enhancement again, improved in image contrast and subjected to distortion correction processing in sequence; carrying out dynamic modeling on the newly collected image data in the nasal jejunum dynamic three-dimensional model; performing dynamic path planning and attitude adjustment, and detecting errors in real time; the system comprises a data acquisition module, a data parallel processing module, a dynamic modeling module and an adjusting module. In this way, the real-time performance, the soft tissue adaptability and the catheter positioning information precision can be met, and therefore the reliability of nasal jejunum catheterization operation is improved.

The invention relates to the technical field of artificial intelligence and electronic manufacturing detection, and discloses an ultra-thin printed circuit board defect detection method and system based on AI vision. The objective of the invention is to solve the technical problems of poor imaging quality, high missed detection and false detection rate, weak generalization ability and difficulty in consideration of efficiency and precision caused by buckling deformation, surface reflection and tiny and diverse defects of an ultrathin printed circuit board in the existing detection technology. The method comprises the following steps: performing multi-angle image acquisition through a composite illumination module and a high-resolution linear array camera; geometric correction is carried out on the image by using an SIFT algorithm and affine transformation, and the image contrast is enhanced by using a CLAHE algorithm; generating a defect candidate region through the lightweight full convolutional network; and carrying out accurate classification and identification on the candidate region by using a deep residual network. According to the technical scheme, high-precision, high-efficiency and low-false-alarm detection of the tiny defects of the ultrathin printed circuit board can be achieved, and the automation level and adaptability of detection are remarkably improved.

The invention relates to the technical field of defect detection, in particular to a defect detection method and system based on a microwave frequency crystal resonator, and the method comprises the steps: forming a microwave probe through a microwave frequency crystal resonator array, placing a to-be-detected material below the microwave probe, and setting a scanning area and scanning step lengths in two directions on the surface of the material; controlling the probe to perform point-by-point scanning according to step length, and collecting a plurality of reflection coefficients of each sampling point to form an echo signal matrix; performing singular value decomposition on the matrix, applying an adjustable coefficient to a first singular value and a second singular value, and adaptively selecting an optimal coefficient according to imaging quality to reconstruct an echo signal; performing two-dimensional Fourier transform on the reconstructed matrix, constructing a spatial filter in combination with probe spacing and defect depth to perform phase compensation, performing two-dimensional Fourier transform to obtain a defect image, and performing threshold segmentation on the defect image to obtain a binary image only containing defects; and analyzing the connected regions to obtain the gravity center position of each defect and the defect area determined by the pixel number and the single pixel area. The problems of serious clutter interference, insufficient defect imaging contrast ratio, difficulty in quantitative evaluation of defect size and the like in the existing microwave detection can be solved.

The invention discloses an imaging system for studying improvement of sensitivity essence of a gas sensor by acoustic tweezers, which is provided with a sound source module and an optical imaging module, the gas sensor is placed at an antinode position of a sound field generated by the acoustic tweezers, the sensor is irradiated by a sheet-shaped light source, and the sheet-shaped light is scattered by gas, so that the sensitivity essence of the gas sensor is improved. Scattered light is shot and collected by a camera through gas on the gas sensor and a lens assembly and then processed through a data processing module, the gas concentration gradient on the gas sensor is obtained by moving the position of the sheet-shaped light source, analysis is conducted in combination with relevant parameters of the sound tweezers, and the research on the effect of the sound tweezers on improving the sensitivity of the gas sensor is facilitated. The problem that no related analysis system exists in the prior art is solved. Furthermore, a first convex lens and a second convex lens are adopted in the lens assembly, a double-telecentric light path is formed, the double-telecentric light path can receive parallel light more easily, and due to the characteristic, the contrast ratio of images shot by the camera is obvious, and recognition and detection of a follow-up algorithm are facilitated.

The application discloses a method for rapidly and nondestructively detecting the quality of silicon carbide crystals, which comprises the following steps: placing the silicon carbide crystals on a computer tomography (CT) carrier table, and rapidly and nondestructively detecting the typical defects in the silicon carbide crystals by means of computer tomography under specific parameters, image contrast and the identification of the topographic features, so as to directly obtain the defect topography, density distribution and the evolution of the defects with the growth process of the measured ingot at different sections, and significantly improve the visualization degree of the defects in the silicon carbide crystals.

The invention relates to the technical field of optical imaging and three-dimensional measurement, in particular to a multispectral structured light path system which comprises a light source module used for generating multi-wavelength light; the collimation system module is composed of any one of one or more lenses, and an aperture diaphragm is arranged on the first face. When the system is used, a multispectral light source design is adopted, the system can meet the three-dimensional measurement requirements of various complex material surfaces, the system adaptability is conveniently and remarkably improved, the image contrast is enhanced through multi-wavelength structured light projection, and the measurement precision is improved. The method effectively reduces the mismatching rate, improves the precision and reliability of three-dimensional reconstruction, achieves the simultaneous obtaining of multispectral information and structured light three-dimensional shape information, expands the application capability of the system in the high-end fields of material recognition, surface defect detection and the like, employs an integrated optical path design, is compact in structure, is high in reliability, and is suitable for large-scale popularization and application. And miniaturization and engineering application of the system are facilitated, and the portability and practicability of the equipment are improved.

The invention relates to the technical field of bar code identification, and discloses a rapid image matching incomplete bar code accurate identification method, which comprises the following steps: carrying out image contrast enhancement processing based on bar code candidate area response on an incomplete bar code image; positioning a bar code area of the incomplete bar code image after contrast enhancement, and detecting and marking an incomplete area in the bar code area; carrying out inference and reconstruction processing on the incomplete area based on a bar code standard coding rule and a statistical redundancy mode; and carrying out identification decoding and redundancy check on the reconstructed bar code image. According to the method, through a multi-step processing mode of image contrast enhancement, incomplete area positioning and labeling, reconstruction based on coding rules and redundancy modes and identification verification, rapid and accurate identification of the incomplete bar code is realized, and under the condition that partial information of the bar code is missing, fuzzy and interfered, the bar code structure can still be effectively recovered, and the identification accuracy of the bar code is improved. And the accuracy, the stability and the robustness of bar code identification are obviously improved.