665 results about "Image contrast" patented technology

The invention discloses an oil field power distribution network operation safety violation behavior deep learning recognition system. The system comprises a video acquisition module, a data preprocessing module, an improved YOLO model module, a violation behavior judgment module and a result output module. The video acquisition equipment is distributed in a key area of an oil field site and transmits video data to the server through a wireless network; the data preprocessing module adopts histogram equalization and sharpening operation to enhance the image contrast. The improved YOLO algorithm model is subjected to customized training, and behavior actions of operators and equipment can be accurately recognized. And the illegal behavior judgment module performs matching judgment on the result output by the model in combination with the safety specification of the oil field operation. According to the method, the difficult case mining technology and the multi-stage training strategy are adopted, the judgment capability of the model on illegal behaviors under complex conditions is improved, the OpenCV technology is combined, the capability of the system for coping with light changes and severe weather conditions is enhanced, and the recognition accuracy under different illumination intensities is ensured.

The invention relates to a financial bill management system and an implementation method thereof. The financial bill management system comprises a data acquisition module, an AI identification module, an auditing module, a cost reimbursement module, a report generation module and a data storage and backup module. According to the financial bill management system and the implementation method thereof, by introducing the AI recognition module, automatic recognition and classification of bill types are achieved, the requirement for manual operation is reduced, the working efficiency and accuracy are improved, the AI recognition module can automatically process bill information, the whole process from image preprocessing to final key information extraction is highly automatic, and the efficiency is high. The system is not limited to bill identification and classification, but also covers a plurality of functional modules such as data acquisition, auditing, expense reimbursement processing, report generation, data storage and backup and the like, and provides a one-stop solution for users. The image preprocessing unit can effectively remove image noise, correct inclined images, cut redundant parts and enhance image contrast and brightness.

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 microscopy method includes a trained deep neural network that is executed by software using one or more processors of a computing device, the trained deep neural network trained with a training set of images comprising co-registered pairs of high-resolution microscopy images or image patches of a sample and their corresponding low-resolution microscopy images or image patches of the same sample. A microscopy input image of a sample to be imaged is input to the trained deep neural network which rapidly outputs an output image of the sample, the output image having improved one or more of spatial resolution, depth-of-field, signal-to-noise ratio, and / or image contrast.

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 cancer image contrast enhancement method and system based on deep learning, belongs to the technical field of medical image processing, and adopts an adaptive histogram equalization algorithm for preprocessing to enhance the contrast of a local region. Texture, shape and edge features are extracted through multi-scale analysis, and feature descriptors are constructed. And based on the feature descriptor, segmenting the image by using a region growing segmentation algorithm to obtain a focus region boundary. A self-adaptive contrast enhancement technology is adopted for the segmented focus area, and the focus display effect is highlighted. In consideration of noise interference, a denoising method based on wavelet transform is introduced, noise is suppressed in a high-frequency sub-band, and details are reserved in a low-frequency sub-band. Through the organic combination of the above technical schemes, the contrast and definition of the cancer image can be effectively enhanced, the focus area is highlighted, and noise interference is inhibited at the same time.

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 discloses a pelvic floor rehabilitation effect image contrast enhancement method, and belongs to the field of image processing. The objective of the invention is to improve the quality of pelvic floor X-ray images and the efficiency and accuracy of clinical diagnosis. According to the method, through collection compression and enhancement recovery modular design, whole-process image quality optimization is realized; the acquisition compression module retains low-contrast area information through dynamic resolution adjustment and area sensing compression, and the transmission bandwidth requirement is reduced; the enhancement and recovery module recovers the image resolution by using the mixed super-resolution and enhances the local and global contrast; in addition, the method further comprises a degradation modeling module, a loss function and training strategy module and a system integration module, and the enhancement effect of the generation network on the low-contrast image is optimized through multi-task learning training. According to the method, the defects of image degradation and noise processing are overcome, the balance between the image enhancement effect and diagnosis correlation is optimized by introducing a multi-task loss function, and powerful support is provided for clinical diagnosis and treatment.

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 relates to the technical field of medical monitoring, in particular to an intelligent self-adaptive arteriopuncture decompression monitoring system. The image acquisition module is used for acquiring image information of a punctured limb in real time; the image processing module is used for preprocessing the acquired image and enhancing the image contrast; segmenting the blood vessel; the deviation condition of the blood vessel center curve is detected, and the deviation amount or the deviation direction is output; the judgment module is used for establishing a prediction model, generating a decompression demand according to a blood vessel image analysis result, and generating a control command to adjust the position and pressure of the compressor according to the decompression demand; and the pressure reduction control module is used for adjusting the pressure of the compressor or sending a monitoring prompt according to the control command. According to the technical scheme, the arterial puncture decompression monitoring effect and safety can be improved.

The invention relates to the technical field of image shooting and processing, and discloses an anti-overexposure image processing method and system under strong light based on a high dynamic range, and the method comprises the steps: collecting a plurality of frames with different exposures for the same scene through an extremely weak light full-color night vision device, carrying out the multi-frame fusion, and generating a high dynamic range image; the method comprises the following steps: mapping an image with a high dynamic range to display equipment with a low dynamic range, and readjusting the brightness and contrast of the image to realize tone mapping; different areas in the image are independently processed, an overexposure area is identified, the brightness of the overexposure area is independently reduced, and the original brightness and contrast of other areas are kept; histogram equalization parameters are dynamically adjusted according to the local brightness distribution of the image, the image contrast and details are improved, and image color correction is achieved. The problem of overexposure of the ultra-weak light full-color night vision device in a strong light environment is solved through a high dynamic range algorithm, and the image quality of the night vision device under different illumination conditions is remarkably improved.

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 pupil center positioning method and device and electronic equipment, and relates to the technical field of image processing, and the method comprises the steps: obtaining a target pupil image; performing gradient calculation on the target pupil image according to a pre-configured gradient direction to obtain a gradient filtering image; taking the MASK image of the target pupil image as a mask to execute mask operation to obtain a pupil position candidate image, filtering each circular arc contour in the pupil position candidate image to obtain a candidate contour, fitting the circle center of the candidate contour to obtain a circle center-containing set, filtering the circle centers in the circle center set to obtain a target circle center, and obtaining the target circle center. And determining the target circle center as the pupil center. According to the pupil center positioning method and device and the electronic equipment, multiple filtering modes are adopted in the whole positioning process, the pupil center can be accurately obtained, and meanwhile high accuracy can still be achieved under the conditions of poor image contrast, shielding, deformation and the like.

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 relates to the technical field of real-time monitoring, in particular to a real-time monitoring method and system for a particle medicine processing process, and the method comprises the following steps: setting a fixed time interval based on photoacoustic signal data in the particle medicine processing process, capturing photoacoustic signals of a particle medicine under the conditions of thermotropic photoacoustic excitation and pressure-induced photoacoustic excitation, and determining the photoacoustic signals of the particle medicine according to the photoacoustic signals; and recording the amplitude of the photoacoustic signal to obtain a photoacoustic signal feature data set. According to the method, continuous monitoring of the particle medicine forming process is achieved through photoacoustic signal capturing at a fixed time interval, and thermal-induced and pressure-induced photoacoustic excitation is combined, so that the signal collection coverage degree is improved, the photoacoustic holographic image contrast ratio is adjusted, the trace component recognition precision is improved, the peak offset rate is analyzed, and noise baseline fluctuation is compared; abnormal time points are accurately screened, the accuracy of interface change detection is improved, particle trajectory data are obtained in combination with high-frame-rate imaging, flow field disturbance distribution is analyzed, accurate control over the machining process is achieved, and stable particle quality is ensured.

The invention provides a spinal endoscopic image deblurring method and system, and the method comprises the steps: carrying out the multi-scale convolution operation of a spinal endoscopic image, obtaining the difference of image contrast among different scales, segmenting the spinal endoscopic image into a plurality of local structure images, and obtaining a plurality of local structure images; determining the brightness uniformity of each local structure image through the distribution gradient of the pixels in each local structure image and the difference quantity of the contrast; determining the dependency relationship of the textures between the adjacent local structure images through the texture difference between the adjacent local structure images on the spine tissue structure; performing contrast enhancement on the brightness of the local structure image according to the dependency relationship and the brightness uniformity to obtain a local enhanced image; and fusing and reconstructing the fuzzy region through all the local enhanced images to obtain a deblurred spinal endoscope image. By adopting the scheme provided by the invention, local adaptive equalization enhancement of the image brightness of the spinal endoscope can be realized, so that detail blurring caused by non-uniform illumination is reduced.

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 discloses a digital image target detection method and system based on medical microbiological examination, and relates to the technical field of digital image processing, and the method comprises the following steps: shooting a microbiological sample to obtain multispectral images with different wavelengths, and shooting the same microbiological sample by adjusting the angle of incident light to obtain a polarization image; carrying out denoising and artifact removal processing on the acquired multispectral image and polarization image, and adjusting the image contrast; fusing the processed multispectral image and polarization image by using principal component analysis (PCA) to obtain a multidimensional data set; extracting type features of different microorganisms from the multi-dimensional data set by using an image processing algorithm; constructing a microorganism classification model by using a deep learning algorithm, inputting the microorganism classification model by using the type features, distinguishing different microorganism species, and outputting the microorganism species; and comparing the output microbial species with a known microbial sample, verifying the accuracy, and adjusting the parameters of the microbial classification model according to the verification result.

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 utility model discloses an oil cylinder digital twinning platform integration system which comprises a sensor assembly and an upper computer based on a digital twinning simulation model, the upper computer is connected with the sensor assembly, data, collected by the sensor assembly, of key parts of an oil cylinder are obtained through the upper computer, and the collected data are integrated and displayed on the upper computer. The integration of functional requirements of digital twinning of the oil cylinder is realized, state information of a pile driving barge, the hydraulic oil cylinder and an operation environment in the construction process is obtained, visual real-time monitoring of the motion state of the super-large and super-long oil cylinder is realized, a camera structure with image acquisition and recognition functions is introduced, and data and image contrast analysis is combined, so that the visual real-time monitoring of the motion state of the super-large and super-long oil cylinder is realized. The accuracy of evaluating the working state and performance of the oil cylinder is improved, and the service life of key components of the oil cylinder can be predicted in an auxiliary mode.