684results about How to "Improve clarity" patented technology

The invention relates to the field of image processing, in particular to a welding quality comprehensive evaluation method and system, and the method comprises the steps: obtaining an ultrasonic image of a welding joint, carrying out the contour detection, and extracting a defect region of the welding joint; gradient features and gray features of pixel points in the defect area are calculated, feature vectors of target pixel points in the defect area are constructed, and the feature vectors serve as the expression degree of the defect area; edge information of the defect area is obtained so as to determine defect types, the defect types comprise cracks and bubbles, the shape and size of a window are set according to the defect types, bilateral filtering processing is carried out, defect information is extracted, and comprehensive evaluation is carried out on the welding quality. According to the method, the appropriate window shape and size are selected according to the defect type, the detail information of the defect is effectively reserved, the definition of the ultrasonic image is remarkably enhanced through the self-adaptive filtering strategy, and therefore the welding quality is better evaluated.

The invention relates to multi-mode signal processing, and provides a signal processing method of a computer system. The method comprises the following steps: acquiring different modal signal data by using a plurality of sensors and performing timestamp synchronization; a depth time sequence alignment network and a multi-layer dynamic time warping algorithm are utilized to ensure signal time consistency; multi-modal signal features are extracted and fused through a multi-stream depth feature extraction network and a cross-modal attention mechanism; the system adopts a dynamic convolution kernel generation and meta-learning technology to carry out adaptive signal processing; noise suppression and signal optimization are carried out by using a multi-mode noise reduction auto-encoder and a generative adversarial network, and parameters are optimized and processed through closed-loop feedback control and reinforcement learning strategies. According to the method, synchronization, fusion, adaptive processing, noise suppression and real-time feedback control of multi-mode signals are realized, and the method has the advantages of high precision, high robustness and real-time performance.

The invention relates to the technical field of information, and discloses a parking lot panoramic safety monitoring method and system, and the method comprises the steps: obtaining a real-time video stream of a parking lot, and carrying out the denoising preprocessing of each frame of image, and obtaining a clear image; adjusting the brightness and the contrast ratio of the clear image by adopting a self-adaptive illumination compensation algorithm to obtain an illumination compensation image; according to the angle and distance change of the vehicle, correcting the vehicle image visual angle in the enhanced image by adopting a perspective transformation algorithm to obtain a perspective transformation image; identifying a vehicle position and a bounding box in the perspective transformation image by adopting a vehicle detection model based on deep learning, and determining a specific position of the vehicle in the image; and in the vehicle dense area, a multi-target tracking algorithm is combined with the moving tracks and the feature information of the vehicles, the shielding relation between the vehicles is judged, and the positions of the shielded vehicles are determined. The method can realize panoramic safety monitoring of the parking lot.

The invention relates to the technical field of geological surveying and mapping, in particular to a multi-source data fusion geological surveying and mapping system which comprises a multi-dimensional data collection module, a data calibration and synchronization module, a geological image enhancement module, a data fusion and model generation module and a visualization and interaction module. The data calibration and synchronization module carries out timestamp calibration and geographic position processing on data, the geological image enhancement module enhances geological images, the data fusion and model generation module fuses the processed data and constructs a geological model by using a modeling technology, and the visualization and interaction module provides a multi-dimensional data interaction platform. Through the geological texture recovery sub-module, visual quality of images is improved, reliability and accuracy during geological data and resource evaluation are enhanced, polarized light data are collected through the polarized image processing sub-module, key information such as surface material roughness is revealed, and surface and underground geological processes can be better understood and explained.

The invention relates to the technical field of RFID chip manufacturing, in particular to an RFID chip image anomaly detection method and system based on intelligent learning. The method comprises the following steps: acquiring a real-time RFID chip sampling image set, performing feature alignment based on a registration template, generating an average grey-scale map by using an alignment image set, and obtaining a chip edge mask image set by using an edge search algorithm; segmenting the edge mask image set of the RFID chip to obtain an effective edge mask variance image set of the RFID chip; integrating the variance image set to obtain a pre-generated variance image; acquiring field environment data of the RFID chip and performing sensitivity coefficient estimation to obtain an edge sensitivity coefficient; and performing convolution filtering smoothing processing on the pre-generated variance image to obtain a chip edge variance image, and constructing an RFID chip image anomaly detection model based on the edge variance image and the average grey-scale image. According to the invention, the detection accuracy and stability are improved.

The invention discloses an image enhancement method based on high-frequency and low-frequency adaptive fusion. The method comprises the following steps: respectively carrying out contrast enhancement of color channels on original images to obtain intermediate feature maps; calculating pixel mean values of the intermediate feature maps under the color channels; performing color compensation on the intermediate feature map according to the pixel mean value to obtain a color reconstruction image; decomposing the color reconstruction image into high-frequency image features and low-frequency image features; performing normalization processing and gamma correction on the high-frequency image features to obtain a high-frequency corrected image; sharpening the high-frequency correction image to obtain a high-frequency recovery image; carrying out defogging processing on the low-frequency image feature weight to obtain a low-frequency recovery image, and carrying out adaptive fusion on the high-frequency recovery image and the low-frequency recovery image to obtain an output image; rich detail and edge information is provided by the high-frequency recovery image, and optimized background and color information is provided by the low-frequency recovery image, so that the visualization effect of the output image is remarkably improved.

The invention discloses a mobile terminal multi-size webpage adaptive display method based on a browser viewport, which belongs to the field of browsers and comprises the following steps of: firstly, selecting iPhoneX (375px width) as a reference viewport size; and then, calculating a zoom factor according to the proportion of the viewport width of the current equipment to the reference viewport width, and dynamically adjusting the webpage content. And then, zooming the page root element by using the zooming factor. In order to adapt to screen widths and resolutions of different devices, breakpoints are set, a compact layout is applied to a narrower device, and more blanks and spaces are reserved on a wider device. And finally, recalculating the scaling in real time and updating the page. The method has the beneficial effects that a finer self-adaptive design is realized by dynamically calculating the zoom factor, adjusting the page layout in real time and optimizing display according to the breakpoint of the equipment.

The utility model discloses an out-of-focus protection desktop far-vision read-write device which comprises a plane spectroscope, a first concave reflector and a second concave reflector. Wherein the light reflected by the table top and an object placed on the table top is emitted to the plane spectroscope from the lower part and then is split; a first light ray formed by reflection of the plane spectroscope is reflected by the first concave reflector, then penetrates through the plane spectroscope and is emitted to an exit pupil position; a second light ray formed after penetrating through the plane spectroscope is reflected by the second concave reflector, is reflected by the plane spectroscope again and then is emitted to an exit pupil position; one of the first light and the second light emitted to the exit pupil position is imaged at a virtual image position not less than 3 meters in front of the exit pupil position, and the other one is imaged at a real image behind the exit pupil position to form positive defocus. According to the out-of-focus protection desktop far-vision read-write device, the coaxial optical system is used, an object placed on a desktop is amplified, projected and imaged, positive out-of-focus stimulation is provided, and myopia prevention and control are achieved.

The invention relates to the technical field of attendance management, and discloses an electronic class board attendance system for palm vein recognition, which comprises a palm vein data acquisition module for irradiating the palm of a user through a near-infrared light source and acquiring a palm vein image; the image optimization processing module is used for performing optical modeling optimization, noise removal and quality enhancement on the acquired palm vein image; the deep learning recognition module is used for extracting palm vein features and performing identity matching; the attendance record storage module is used for storing and managing user attendance data; and the control and communication module is used for managing cooperative work of all the modules and performing data interaction with an external attendance database. Through the technical scheme of combining near-infrared illumination and the light transmission equation, the palm vein feature image is accurately extracted, the effect of optimizing palm vein contrast and detail definition is achieved, the problems of palm vein feature edge blurring and detail loss are solved, and the image processing quality is improved.

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 textile texture defect detection system and detection method based on machine vision. The detection method comprises the steps that a matrix light source is located on one side of a textile detection area; acquiring an image of a detection area, detecting texture change of the textile in the detection area, and marking defect candidate areas; adjusting the angle change of the matrix light source and the brightness change of the defect candidate area, and generating an image; extracting multiple groups of image information, comparing the multiple groups of image information, and judging the defect position of the textile; the detected defects are marked in the image, and defect feature information is output; according to the method, the defect candidate area is preliminarily determined, and the matrix light source is matched to correspond to the defect area at different irradiation angles, so that the texture light and shade contrast of the surface of the textile in different directions is enhanced, the tiny offset of the weft skew defect is more clearly displayed in the image, the position of the defect is further determined, and the detection accuracy is improved. And the accuracy of defect detection can be improved.

The invention provides an automatic SRAF exposure development suppression method and device, a medium, a program product and a terminal, and aims to optimize an SRAF placement area when an interference pattern is detected in an initial exposure pattern through a multi-round iterative optimization technical means, and when a new exposure pattern meets a preset termination condition, the iterative optimization operation is ended, so that the SRAF exposure development is suppressed. Therefore, the problems of time waste and flow complexity caused by redesigning the SRAF placement rule in the traditional method are solved. According to the method, the original spatial position of the SRAF is kept, the enhancement effect on the resolution of the main graph is guaranteed to the maximum extent, and the definition and precision of the graph are improved, so that the production efficiency is improved, the manufacturing process is simplified, and a more convenient solution is provided for practical application.

The invention provides a sound processing method, system and device based on a hearing aid and a storage medium, and the method comprises the steps: collecting sound signals, including background noise and voice signals, of a hearing aid use environment in a large scale; the sound signals are preprocessed, abnormal values and missing values are processed, the abnormal values and the missing values are scaled into a unified scale, and the sound signals are divided into a training set and a test set; adjusting and optimizing parameters of the standard adaptive filtering model by using an improved gradient descent optimization algorithm, wherein the parameters comprise a filter order, a maximum number of iterations, an initial step length and a regularization coefficient; and inputting the test set into the optimized algorithm to realize optimization processing of the sound signal. According to the invention, the voice definition and the background noise suppression capability can be improved, and the use effect and the user experience of the hearing aid in a complex environment are improved.

The invention discloses a characteristic value calculation method based on electrocardio data, belongs to the field of medical data analysis, and provides an electrocardio data analysis and processing method. Comprising the following steps: decomposing different frequency components of an electrocardiosignal by using wavelet transform, and analyzing on different time scales. Wavelet transformation is relatively suitable for detecting QRS wave groups, and due to the fact that the wavelet transformation can highlight transient characteristics of signals on different frequency bands, complex electrocardiosignal processing is more efficient. In addition, wavelet transform is not only suitable for wave crest detection, but also can help to remove signal noise and improve signal definition. The deep learning technology is used for automatically extracting the features from the electrocardiosignals, a feature extractor does not need to be manually designed, related medical professional knowledge and the artificial intelligence technology are combined, wave crests in the electrocardiosignals can be more accurately detected, and meanwhile anomaly detection can be carried out, such as early signs of heart diseases such as sinus arrhythmia and the like.

The invention discloses a heating structure used in a scanning electron microscope and a use method of the heating structure, and relates to the technical field of scanning electron microscope high-temperature performance tests.The heating structure comprises a laser, a shell module, a laser clamping module and an electron back scattering diffraction scanning fixing module, and the upper surface of the electron back scattering diffraction scanning fixing module is obliquely arranged; the laser clamping module is installed on the electron back scattering diffraction scanning fixing module, the shell module is installed above the laser clamping module, a heating dodging plate used for placing a sample to be tested is arranged in the shell module, and the laser clamping module is used for clamping a laser lens of a laser. Laser beams output by the laser lens are irradiated on the heating dodging plate, the two ends of the to-be-tested sample extend out of the shell module and are connected with the stretching table, and an observation hole for observing the sample is formed in the upper end of the shell module. According to the invention, the imaging definition of the scanning electron microscope can be improved, and the in-situ high-temperature mechanical property test and the EBSD high-temperature analysis experiment in the scanning electron microscope can be smoothly carried out.

The invention provides a three-dimensional seismic fault identification method based on deep learning, and the method comprises the following steps: (1), carrying out the automatic modeling, generating three-dimensional seismic data and an accurate fault label, obtaining a three-dimensional seismic image data set, dividing the data set into a training set and a test set, and carrying out the recognition of the three-dimensional seismic fault; (2) training an improved 3D-Unet convolutional neural network of a two-channel structure or an AG-3D-Unet network fused with an attention mechanism and residual learning by taking the three-dimensional synthetic seismic data of the training set and a label of the three-dimensional synthetic seismic data as input, and verifying by using a test set to obtain a trained seismic fault recognition model, (3) inputting real seismic data, and obtaining a seismic fault recognition result. And outputting a fault identification result for the input to-be-predicted image through the identification network. According to the method, the definition, the fault continuity and the accuracy of fault identification are obviously improved, and the method has high practical value in actual seismic data fault interpretation.

The embodiment of the invention provides a rockburst dynamic early warning method and system based on micro-seismic information, and can solve the problem that in engineering scenes such as mining and tunneling, continuous vibration of equipment such as a drilling machine and a heading machine can generate a large amount of background noise, so that the difficulty of effectively extracting a micro-seismic fracture event in a rock mass is increased. The method comprises the following steps: acquiring operation disturbance information of vibration of an operation machine in a typical strong rockburst hole section rock mass construction process on surrounding rock masses, so as to extract operation disturbance characteristics; determining a continuous influence period of operation disturbance in an acquisition point range during operation and after the operation is stopped according to the acquisition point range of micro-seismic monitoring; and based on the operation disturbance characteristics, the continuous influence period and the working state information of the operation equipment, performing dynamic denoising on the acquired micro-seismic information so as to perform rockburst early warning through the denoised micro-seismic information.

The invention relates to an exterior mirror system and method for fluorescence imaging, and relates to the technical field of image processing, the system can detect fluorescence under a white light operation condition in a tumor excision process, and imaging in a dark or dim operating room is not needed; white light in the fluorescent light is separated, so that a clear fluorescent light image is presented; in addition, fluorescence images can be displayed in a 3D mode, and development is more visual. According to the invention, the technical problems of dark fluorescence image, poor definition and complicated operation in the developing process in the prior art are solved.

The invention discloses an operation monitoring system and method for waste gas treatment equipment. The method comprises the following steps: acquiring a shot image of an aluminum foil defect test chart; aluminum foil defects are analyzed to obtain image similarity values; comparing the image similarity value with an image similarity threshold value, judging whether the test image is qualified or not according to a comparison result, and generating a test non-passing signal and a test passing signal; analyzing association between the unqualified test image and equipment illumination based on the test failing signal to obtain an illumination association value; comparing the illumination association value with an illumination association threshold value, judging whether the test image is unqualified and is associated with an illumination condition according to a comparison result, and if so, generating an illumination association signal; based on the test passing signal, the aluminum foil defect points are analyzed, and a detection defect value is obtained; comparing the detection defect value with a detection defect threshold value, and judging whether the produced aluminum foil is qualified or not according to a comparison result; the aluminum foil surface defect identification precision and detection accuracy of the system can be improved.

The invention proposes a small sample image generation method based on semantic mask reconstruction and category semantic learning, and relates to the technical field of image processing, and the method comprises the steps: S1, constructing a network model comprising a generator and a discriminator based on a LoFGAN network; s2, extracting depth features of the input image by using a generator, and fusing the depth features of the input image into depth semantic features through local feature fusion; s3, semantic mask reconstruction is carried out on the depth semantic features, and a corresponding image is generated through a decoder; s4, sending a real image and a corresponding image generated by the decoder into a discriminator to train the network model; s5, inputting the small sample image into the trained network model to obtain a generated image similar to the input image; according to the method, the extraction precision of the semantic information can be effectively enhanced, the understanding ability of the generation model for a complex semantic scene is improved, and the fidelity and definition of the image are improved.

The invention discloses a fingerprint identification method and system based on an optical identification induction technology, and relates to the technical field of fingerprint identification, and the method comprises the steps: collecting a fingerprint surface texture image through the total reflection principle of light; converting the collected fingerprint surface texture image into a fingerprint surface texture grayscale image; determining the main direction and frequency of the fingerprint streaklines, and separating the adhered fingerprint streaklines; smoothing stray point information in the fingerprint image by using an image filtering algorithm; the fluctuation characteristics of the fingerprints are further highlighted by using morphological expansion and corrosion operation; extracting morphological features of fingerprints and local features of streaklines from the preprocessed fingerprint image; and outputting the user information corresponding to the fingerprints in the database higher than the preset similarity value. By arranging the image acquisition module, the image processing module, the feature extraction module and the similarity comparison module, the interference of environmental factors is reduced, the fingerprint image quality is ensured, the fingerprint identification time is shortened, and the real-time performance of the system is improved.

The invention discloses a self-adaptive contrast enhancement method and system for a medical image, and relates to the technical field of medical image processing, and the system comprises the following components: S1, image input and preprocessing, S2, quantum heuristic parameter search initialization, S3, a search process based on quantum state superposition and entanglement characteristics, S4, optimal parameter combination determination, and S5, optimal contrast enhancement. And enhancing the image contrast. According to the method, noise in the medical image is effectively removed through self-adaptive weighted median filtering and linear normalization preprocessing operation, the gray value range of the image is normalized, a high-quality input image is provided for subsequent contrast enhancement operation, meanwhile, a quantum heuristic parameter search algorithm is utilized, and the image quality is improved. According to the system, the optimal contrast enhancement parameter combination can be automatically searched and determined, the self-adaptive enhancement of the contrast of the medical image is realized, the definition and detail expressive force of the image are improved, and a doctor can more accurately diagnose diseases.

The invention relates to the technical field of image processing, and relates to an inspection fuzzy image recognition method and device based on a region target detection algorithm. The method comprises the following steps: acquiring an original image shot by a to-be-identified device; carrying out image preprocessing, and inputting the image into the trained region target detection network for processing; and carrying out deblurring processing by adopting a multi-Wiener filtering algorithm to obtain a reconstructed clear image, and finally carrying out image recognition. According to the invention, a region target detection network based on YOLO network modification is adopted, so that the to-be-deblurred target region in the equipment image can be accurately identified while the model is lightened; according to the method, a multi-Wiener filtering algorithm is adopted, so that the to-be-deblurred target area in the equipment image can be effectively deblurred; according to the method, the clear image can still be generated under the condition that the inspection robot moves, the definition and detail performance of the image are greatly improved, the recognition accuracy is further improved, and misjudgment caused by blurring is reduced.

The invention relates to the technical field of image recognition, and particularly provides a road target monitoring method, system and device and a storage medium, and the method comprises the steps: obtaining a road monitoring video stream, converting the road monitoring video stream into an image frame sequence, and carrying out the preprocessing of image frames; identifying a road target in each image frame by using a target detection algorithm, and generating a track of the road target in the image frame sequence by using a multi-target tracking algorithm; performing anomaly analysis on the trajectory based on a preset rule, and identifying an abnormal trajectory; and obtaining a confidence score of an abnormal road target to which the abnormal track belongs, screening out a target detection frame with the highest score, and extracting feature information of the road target from the target detection frame. According to the invention, the speed and precision of road target identification in a complex environment are improved, an abnormal analysis and subsequent processing method of the moving track is provided, the quality of road target monitoring is improved, and a data basis is provided for road management.

The invention relates to the technical field of gate channel target image recognition, and particularly discloses a gate channel target recognition detection method and system, and the method comprises the steps: collecting image data, carrying out the image preprocessing steps, such as illumination compensation and noise removal, extracting the brightness features and texture features in an image, and calculating the visibility coefficient of a target region. According to the method, high identifiability of the target is kept under various illumination conditions, the final identification accuracy of the target in different environments is evaluated by comprehensively analyzing the visibility coefficient and the identification confidence of the target, so that the image preprocessing and target identification algorithms are dynamically adjusted, the target can be rapidly and accurately identified, and the identification accuracy of the target is improved. The target identification efficiency in the gate channel is improved, the high efficiency and robustness of the system under the complex illumination condition are ensured, and the method has practical value and application prospect.

The invention relates to the technical field of computer vision, and discloses a visual saliency target detection method based on an RGB-D image, and the method comprises the following steps: S1, carrying out the channel alignment and spatial calibration of a mutual relation between corresponding features of different modals through an MI module, and obtaining a cross-modal representation; s2, then, an IGF module is added to the tail end of the Swin Transform encoder, and the IGF module is added to the tail end of the Swin Transform encoder; s3, the cross-modal fusion feature # imgabs0 # is subjected to channel cross fusion through a CCT module, and relevant information about significance is further obtained; and S4, finally, carrying out modeling on the remote dependency of the fusion features from a global perspective by using a decoding block based on Swin-Transform. According to the visual saliency target detection method based on the RGB-D image, by designing a modal interaction module and a channel cross fusion mechanism, the fusion effect of multi-modal features and the fine modeling capability of multi-scale features are fundamentally improved, and therefore the problems of saliency target detection precision and robustness which are difficult to overcome in the prior art are effectively solved.

The invention discloses a thyroid postoperative bleeding monitoring method and system, and relates to the technical field of medical monitoring. According to the method, the YOLO-Seg network model is adopted, and the SRCNN layer and the Blind Dconv layer are combined, so that the drainage tube area and the dressing area can be identified with high precision, the accuracy of area identification is improved, and the definition and the deblurring effect of the image are improved; the color, the category and the proportion information of the drainage liquid are accurately detected, so that powerful support is provided for judging whether the patient bleeding or not; by comprehensively analyzing the physiological index data, the overall health condition of the patient is more comprehensively evaluated, the health condition of the patient is accurately reflected through the physiological index score, the potential bleeding risk is found in time, a comprehensive, accurate and timely solution is provided for monitoring the thyroid postoperative patient, and safety and rehabilitation of the patient are effectively guaranteed.

The invention relates to an adaptive weighted bilateral filtering deconvolution OCT image processing method and system, and the method comprises the steps: carrying out the preprocessing of an original OCT signal, including background noise removal, wave number calibration, spectrum shaping and dispersion compensation, so as to obtain an OCT reconstructed image; enhancing the contrast ratio through histogram stretching, adjusting the space weight and the intensity weight of the OCT stretching image, and performing adaptive denoising on the image; and further optimizing the image by using a blind deconvolution algorithm, and iteratively updating the estimated image and a point spread function (PSF) to obtain a high-definition OCT reconstructed image. According to the method, efficient denoising and detail reservation of the OCT image are realized through adaptive weighted bilateral filtering and blind deconvolution technologies, so that a high-definition reconstructed image is obtained.