96 results about "Poor Quality Image" patented technology

The invention discloses an unmanned aerial vehicle multi-modal feature fusion target tracking method and system based on natural language description, belongs to the technical field of computer vision and image processing, and solves the problem that in the prior art, when the quality of an image collected by an unmanned aerial vehicle is poor or image features are not obvious, the target tracking capability and the long-time tracking capability are poor. Natural language description is carried out on a traffic accident scene in an image of an unmanned aerial vehicle visual angle, and a language prompt is obtained; constructing a scene-context feature pyramid network to perform context information enhancement processing on the image of the view angle of the unmanned aerial vehicle to obtain a feature-enhanced image; respectively carrying out visual coding and language coding on the enhanced image and language prompt to obtain visual features and language feature vectors, and carrying out visual-language bimodal feature local alignment; and fully fusing the obtained aligned new language features with the visual features to obtain multi-modal features for target tracking. The method is used for multi-modal feature fusion target tracking of the unmanned aerial vehicle.

The invention relates to the technical field of medical image processing, in particular to an artificial intelligence assisted intraoperative imaging method, which comprises the following steps: S1, preprocessing a multi-modal medical image, segmenting and recognizing an anatomical structure by a deep learning model according to the preprocessed image, measuring anatomical parameters based on a segmentation and recognition result, and generating an operation planning path by artificial intelligence according to the anatomical parameters; s2, collecting a C-shaped arm perspective image stream in real time, dynamically tracking space coordinates of a surgical instrument, comparing the position of the instrument with a surgical planned path, calculating offset, and when the offset is greater than an offset threshold, outputting correction guidance through an AR superposition layer; and S3, monitoring an image quality index in real time, dynamically adjusting exposure parameters through a reinforcement learning model, and when a metal implant is detected, switching a dual-energy-spectrum mode and executing an artifact suppression algorithm. According to the method, preoperative precise planning and intraoperative assistance are realized through artificial intelligence, the problems of poor image quality and high radiation risk are solved through technical optimization, and the method has important clinical application value.

The invention discloses a device and a method for intelligently investigating types and quantity of fishes. The device comprises a self-adaptive sonar detection module, a three-dimensional image acquisition module, an image preprocessing module, a fish detection module, a main body segmentation module and an identification and classification module. The sonar module is integrated with a three-frequency-band transducer, can dynamically switch frequency according to fish school depth, and realizes target tracking counting and quantity estimation in combination with an algorithm; the image acquisition module constructs a fish school three-dimensional image model by combining a depth camera with a light attenuation compensation algorithm; the preprocessing module fuses acoustic and optical features and expands a data set; the detection and segmentation module is used for accurately positioning fishes and removing impurities; the identification and classification module realizes type identification based on transfer learning and supports incremental learning of new fingerlings. According to the method, through cooperation of multiple modules, the problems of insufficient detection precision, poor image quality and the like in traditional investigation are solved, efficient and accurate investigation of fish species and quantity is realized, and technical support is provided for fishery resource management and ecological protection.

The invention aims to provide a glue surface defect analysis method and system based on machine vision. The method comprises the following steps: acquiring a glue surface image; performing feature recognition on the glue surface image, and judging whether defects exist or not; and if the defect exists, judging the type of the defect. According to the method, enhanced segmentation is carried out on the glue surface image, so that the problem of low machine vision detection accuracy caused by poor image quality due to illumination condition and shooting condition factors is solved, and meanwhile, a glue surface defect identification model is designed; the glue surface defect identification model is trained for different types of glue and different types of defects, so that the glue surface defect identification model has good generalization ability, and multiple defects of multiple types of glue can be detected.

The invention discloses a railway pantograph carbon slide plate wear detection method based on machine vision, and the method comprises the steps: firstly carrying out the illumination enhancement of a pantograph image through employing an SCI model which introduces a channel attention mechanism, and carrying out the super-resolution reconstruction of the pantograph image after the illumination enhancement through employing a Real-ESRGAN model; secondly, an improved M3Net model is adopted to carry out pantograph carbon slide plate area positioning, an improved M3Net model encoder adopts a residual error post-standardization structure and logarithm interval continuous relative position deviation method, a decoder adopts an MIB module and an MAB module, and then a DexiNed model introducing a CA block and a space attention mechanism is adopted to carry out carbon slide plate abrasion edge detection; and finally, judging whether uneven wear exists or not according to a wear edge detection result. According to the railway pantograph carbon slide plate wear detection method disclosed by the invention, the problem of large wear detection error of the carbon slide plate under the condition of poor image quality or complex background in the existing method is solved.

The invention discloses a low-illumination image quality improvement method based on self-adaptive stochastic resonance. The method is specifically implemented according to the following steps: step 1, synchronously acquiring a grayscale image and a color original image corresponding to the same image through two channels; step 2, preprocessing the grayscale image and the color original image acquired in the step 1; 3, self-adaptive stochastic resonance parameter calculation is carried out; step 4, performing dual-channel adaptive stochastic resonance processing on the preprocessed grayscale image and the preprocessed color original image according to adaptive stochastic resonance parameters to obtain an enhanced image; and 5, carrying out brightness component fusion on the enhanced image, and then carrying out image reconstruction and post-processing. According to the invention, the problem of poor image quality after low-illumination image enhancement in the prior art is solved.

The invention provides a synchronous positioning and mapping method and system suitable for a low light condition, electronic equipment and a storage medium, and aims to solve the problem that the existing SLAM technology is difficult to meet the actual operation requirements of real-time monitoring and quick response under a dark light condition. The method comprises the following steps: introducing an LSKAttention big kernel attention mechanism into an IAT-based image enhancement module, performing image enhancement processing to obtain an output result of image enhancement; and carrying out comprehensive extraction and clustering of image features on an output result through a preset closed-loop detection module. The problems of poor tracking image quality, poor mapping loopback effect and the like in the dark light environment can be solved, and accurate synchronous positioning and mapping of the environment in the dark light environment are realized.

The invention discloses a highway service area safety prevention and control method and system based on artificial intelligence, and relates to the technical field of highway service area safety prevention and control. Through an environment adaptive dynamic compensation mechanism, the problems of poor image quality, detail loss, high noise and the like under different light conditions are effectively solved, the details of the image are supplemented, and the image quality is improved; an image recognition detection algorithm is utilized to accurately record information such as pedestrian flow and vehicle types, and a reliable basis is provided for safety risk prevention and control; the vehicle circulation rate, the population circulation rate and the dangerous chemical risk degree are integrated to calculate the actual risk degree of the danger, and the potential risk of the service area can be comprehensively reflected, so that a scientific, reasonable and targeted safety risk prevention and control scheme is formulated. The speed reduction condition of the vehicle is monitored and reported in real time, the safety management efficiency of the service area is effectively improved, the accident rate is reduced, and safe operation of the expressway service area is guaranteed.

The invention discloses an eye movement tracking stability enhancing method, system and device and a storage medium, and belongs to the field of human-computer interaction and computer vision. The method comprises the following steps: acquiring an eye image and an original sight fixation point coordinate; performing quantitative evaluation on the image from five dimensions of definition, contrast and the like to generate an eye image quality evaluation grade; calculating the moving speed and acceleration of the current point based on the historical sight fixation point sequence; fusing, and judging and correcting the original coordinates. Through multi-modal information fusion and hierarchical adaptive correction, noise jump caused by poor image quality can be accurately distinguished and filtered out, meanwhile, the real eye movement dynamic state is effectively reserved, and the output stability and reliability of an eye movement tracking system are remarkably improved.

The invention belongs to the technical field of image processing, and particularly provides an automobile data recorder image optimization method, system and device and a storage medium. The method comprises the following steps: acquiring image data and environmental parameter data of a target scene in real time through an automobile data recorder and an acquisition module deployed on a mining vehicle; outputting a current environment state type through an environment state classification model based on the environment parameter data; according to the environmental parameter deviation degree of each sub-state in the environmental state type, calculating a weight score through a preset severity scoring model, and determining a priority order of an image optimization algorithm according to a scoring result; and sequentially calling the image optimization algorithms associated with the sub-states according to the priority order to process the image data, and generating optimized image data. Through the scheme, the problems of poor image quality and monitoring failure of the mining automobile data recorder in a severe environment are solved, and the visibility of image data of key driving information is improved.

The invention discloses a zero-sample low-light image enhancement method based on joint frequency domain prior guide diffusion, which relates to the field of optics and comprises the following steps: decomposing the frequency domain of a low-light image; performing forward diffusion and reverse sampling processing on the low-light image by using a Markov chain structure model, and supervising an optimization processing result in combination with a multi-modal text to obtain a sampling result; decomposing the frequency domain of the sampling result to obtain a sampling low-frequency domain, and performing conversion processing on the sampling low-frequency domain and the low-light low-frequency information by using Fourier transform to respectively obtain amplitude and phase information of the sampling low-frequency domain and the low-light low-frequency information; and in combination with the amplitude and phase information of the sampling low-frequency domain and the low-light low-frequency information and the low-light high-frequency information, on the basis of inverse fast Fourier transform and inverse discrete wavelet transform, an image enhancement output is obtained by updating a sampling result. According to the method, the problem of poor image quality of the low-light image is solved by combining the frequency domain prior guide diffusion method.

The invention discloses a multi-modal feature tracking method based on a traffic scene, and relates to the technical field of traffic scene feature tracking, and the method comprises the steps: collecting traffic scene RGB image data and event camera data, and constructing a multi-modal time series data set; constructing a multi-modal feature tracking model; extracting a time sequence motion feature of the event camera data, and generating a displacement prediction vector; state prediction updating is carried out, and a tracking prediction result is generated; and continuously tracking the traffic target to generate motion tracking trajectory data. According to the invention, the technical problems of poor image quality, low tracking accuracy, weak single-mode tracking anti-interference capability and difficulty in adapting to high-frequency tracking requirements caused by the influence of factors such as illumination, weather, exposure and the like during traffic scene feature tracking in the prior art are solved; the technical effects of realizing collaborative feature tracking of the RGB image and the event camera multi-modal data in the traffic scene and improving the frequency, accuracy and anti-interference capability of feature tracking are achieved.

The invention provides a circular curve burr detection method and system based on multi-scale phase consistency and a contour model, and a medium, and the method comprises the steps: constructing a multi-scale Log-Gabor filter group, and carrying out the convolution calculation of an input image, and obtaining a response vector; calculating phase consistency measurement of each pixel point, extracting an initial edge point set, fitting an initial circular contour, performing energy minimization evolution on the initial circular contour based on an active contour model, and fitting a real contour of burrs; constructing a two-dimensional feature vector; carrying out fusion discrimination on the two-dimensional feature vectors based on a linear weighted score function, and outputting a burr detection result; according to the method, phase consistency is used as a core image feature, so that the detection algorithm has natural invariance for uneven illumination, brightness change and contrast difference, and under the scene of failure caused by poor image quality in the prior art, the contour edge can still be stably and completely extracted, and the omission ratio is remarkably reduced.

The invention discloses a four-dimensional cone beam CT (Computed Tomography) reconstruction method based on an anatomical prior constraint diffusion model, which mainly solves the problem of poor quality of a reconstructed image in the prior art, and adopts the implementation scheme that the method comprises the following steps: acquiring CT original full-projection data of a batch of patients, and carrying out sparse sampling on the CT original full-projection data; performing data set division and reconstruction on the original full projection data and the sparse projection data to obtain a reference true value and a low-quality CT image containing a large number of artifacts; preliminarily enhancing the CT image containing a large number of artifacts by using the pre-trained ResViT network to obtain a CT image without artifacts; segmenting the CT image without artifacts by using a body segmentation network to obtain anatomical information of each organ; constructing a four-dimensional cone beam CT motion compensation reconstruction model based on anatomical prior constraints, and training and verifying the four-dimensional cone beam CT motion compensation reconstruction model; and inputting the tested data into the trained reconstruction diffusion model to obtain a reconstruction result. According to the method, artifacts of sparse cone beam CT in a specific organ area can be thoroughly eliminated, the reconstructed image is clear in details, and the method can be used for tracking and positioning tumor movement caused by respiration.

The invention relates to a macular fovea centralis multipoint accurate positioning method and system based on multi-modal fusion, relates to the technical field of medical image processing, and solves the problem of how to overcome lesion interference and image quality difference in diabetic retinopathy patients to realize accurate positioning of macular fovea centralis. The method comprises the following steps: carrying out data preprocessing on multi-classification equal-circle pattern spots, and excluding the types of abnormal pixel area and distance interval in an eye fundus image and abnormal perfusion characteristics in a fluorescein eye fundus angiography image by utilizing a preset equal-circle and equal-distance multipoint constraint condition; and calculating the position, distance and angle of each equal circle according to an equal-circle and equal-distance multi-point constraint condition, and calculating the position of the center point of the macular according to a preset positioning method. The method has the following effect that the positioning accuracy of the macular fovea centralis is improved.

The invention discloses an abnormal weather identification method and device based on deep learning, electronic equipment and a program product. According to the recognition method, a C2fPRFF module is arranged in a backbone network through a weather recognition model, multi-scale receptive field features are extracted through progressive fusion of stacked PRFFBlocks, and the understanding of the model on context information is enhanced. The neck network is provided with an EGMA module, channel attention and space attention sub-modules can be combined, channel shuffling operation is added, the feature expression ability is effectively improved, and the model is helped to better deal with the problems that the image quality is poor and the foreground and the background are difficult to distinguish. Besides, the positioning precision of target detection and the convergence speed of the model can be optimized by introducing the newly designed focus fusion intersection-union ratio loss, and the robustness and accuracy of the model are improved especially under the complex background and interference conditions. The detection performance and the reliability of the weather identification model in a severe environment can be remarkably improved by integrating the improvement in the three aspects.

Illuminated contact lenses and systems for improving ocular diagnostics, disease management and procedures are aimed at increasing the amount of light reaching the retina. The edge of the contact lens is beveled to redirect and increase light reaching the retina. The light source surrounds and contacts the straight or curved beveled edge. In addition, a reflective cylinder and its top wall surround the lens to block any light loss. The edge of this distal end of the contact lens may be rounded to increase the angle at which the retina is visible. A new eye imaging camera has a low light camera assembly with a server, a light sensor alongside the low light camera, a short cylindrical housing, a space between the camera assembly and the housing, an internal procedure in the server for detecting good or bad image quality, and an alarm of poor image quality in which an operator recaptures the image.

This invention relates to the technical field of combining artificial intelligence with traditional Chinese medicine (TCM), and discloses an AI-based intelligent image analysis system for TCM tongue diagnosis. The system includes: an image acquisition and preprocessing module, which receives raw tongue image data from a tongue image acquisition device via a hardware interface and performs multimodal correction processing on the raw tongue image data to generate standardized images; and an environment adaptation module, which is connected to the image acquisition and preprocessing module via a data interface. This invention reduces image errors caused by the shooting environment and equipment through multimodal correction in the image acquisition and preprocessing module, and combines this with a data expansion and enhancement module to generate diverse case images to improve the dataset. This effectively solves the problems of poor image quality and insufficient case coverage, reduces reliance on doctors' experience and subjective judgment, reduces differences in judgment among different diagnostic subjects regarding the same tongue image, and improves the accuracy and stability of diagnostic results.

The invention provides a cross-modal magnetic resonance image synthesis method and system, relates to the technical field of magnetic resonance imaging, and aims to solve the problems of data missing, low calculation efficiency and poor image quality in the existing image synthesis technology. The method comprises the following steps: encoding a multi-modal image through a multi-layer encoder to obtain multi-level encoding features; performing feature extraction on the multi-level coding features through a plurality of residual error mixing Mama modules in sequence to obtain corresponding optimization features respectively; inputting each optimized feature into a multi-feature extraction module for feature aggregation to obtain a multi-scale aggregation feature; performing multi-level feature fusion on the multi-scale aggregation features based on a plurality of modulation fusion modules; and through an implicit neural representation module, mapping the fusion features into pixel values by using a multi-layer perceptron, and combining the pixel values to generate a target modal image. According to the invention, the problems in the prior art are solved, and the image synthesis quality is improved.

The present invention relates to an image processing apparatus, method, and program that reduce the difference in image quality between high-absorbent regions and adjacent regions in a tomographic image. [Solution] The system includes a processor, which identifies high-absorbent regions in a projection image obtained by imaging a subject containing high-absorbent materials using a CT scanner, and derives a corrected projection image by performing a correction on the high-absorbent regions in the projection image to suppress the image quality difference between the high-absorbent regions and other regions other than the high-absorbent regions.

The invention discloses an optical crystal Vickers hardness and transmittance measurement system based on image processing, particularly relates to the field of optical crystal measurement, and comprises an image collaborative acquisition module, an image preprocessing module, a feature extraction calculation module, a self-adaptive feedback module and a data output module. According to the optical crystal Vickers hardness and transmittance measuring system based on image processing, physical separation and synchronous acquisition of an indentation image and a transmission light field image are realized through an image collaborative acquisition module, the conflict between exposure parameters and focal length requirements of the same sensor is eliminated from a hardware architecture, and parameter adjustment caused by mode switching of a single sensor is avoided; repeated parameter adjustment caused by poor image quality is reduced through the image preprocessing module, and the measurement consistency is enhanced; an accurate optical crystal feature set is obtained through the feature extraction and calculation module, and invalid parameter adjustment is reduced.

The present invention discloses a distribution line inspection image enhancement method and system, comprising: extracting color information and grayscale information from an acquired distribution line inspection image, and obtaining an image quality score based on the color information and grayscale information using a trained image quality evaluation network; screening the distribution line inspection image according to the image quality score, performing color space conversion on the screening distribution line inspection image, and extracting a brightness component; performing brightness enhancement on the brightness component based on a pre-built enhancement model based on an attention residual module, and performing color space conversion on the enhanced brightness component, the original hue component, and the original saturation component to obtain an enhanced distribution line inspection image. First, image quality evaluation is performed to filter low-quality images, and then the brightness information of the low-illuminance inspection image is enhanced, and at the same time, detail information is enhanced through bilateral filtering to solve the problem of poor image quality in a low-illuminance environment of a distribution network scene.

The present invention provides a method, device, equipment and storage medium for identifying the readings of an instrument dial. First, an image to be recognized collected by an image acquisition device is obtained, and a pre-trained YOLOv5 model is called to recognize the image to be recognized to locate the dial area image. Then, a pre-trained DeepLabV3+ model is called to extract features from the dial area image to generate deep features and shallow features. Finally, the deep features and the shallow features are fused to generate fused features, and semantic segmentation is performed on the fused features to identify and output the reading result of the dial. The problem of insufficient recognition accuracy and reliability caused by poor image quality of the captured image is solved.

The application relates to a visual SLAM system under low-illumination conditions, which comprises an image preprocessing module and a SLAM module. The image preprocessing module comprises an image contrast enhancement submodule based on histogram equalization and an image denoising submodule based on domain transfer. The image contrast enhancement submodule based on histogram equalization is used for performing contrast enhancement on a low-illumination image to obtain a contrast-enhanced image, and the image denoising submodule based on domain transfer is used for performing denoising on the contrast-enhanced image. The visual SLAM system under low-illumination conditions has the advantages that the image contrast is improved and the noise is processed in the image preprocessing module, the low-illumination image detail expression capability is improved, the problem of poor SLAM input image quality under low-illumination conditions is solved from the input level, and the system has high precision and good stability.

This invention relates to the field of machine recognition technology, and particularly to a multi-task-based method and device for recognizing multi-pointer instruments in substations. This invention proposes a highly efficient multi-task instrument recognition network that can simultaneously handle three key tasks: target detection, instrument centering, and pointer segmentation of substation instruments. This significantly improves the detection speed while greatly enhancing the accuracy of instrument pointer detection. The algorithm proposed in this invention has higher universality, enabling the recognition of multiple instruments and multiple pointers, effectively solving the problem of using a different recognition algorithm for each instrument. The dataset for this invention is entirely derived from actual substations. Even in outdoor scenes and under conditions of poor image quality, it achieves accurate recognition of single and double-needle instruments in substations, including instruments in switchgear, discharge counters, main transformer temperature instruments, current transformers, voltage transformers, and surge arresters, with an accuracy approaching 100%. The algorithm is flexible in configuration, easy to set up, and reliable in operation.

The invention discloses a time-varying information decoupling method based on time feature guidance, and aims to solve the problem that the quality of a reconstructed image is poor due to the fact that an existing method cannot suppress negative effects of time-varying shadow information on geometric constraints. Decoupling of time-varying shadow information and time-varying geometric information in a scene is realized, a time-space field function learning rule is optimized, and the time-space field function fitting difficulty is reduced. According to the invention, based on a time-varying geometric information decoupling strategy guided by time sequence features, a space-time field function is driven to understand an imaging rule before and after geometric change, so that the space-time field function can realize dynamic fitting for different geometric forms at different times; a time-varying shadow information decoupling strategy based on illumination time-varying rule mining is proposed, a time-space field function is enhanced to understand an imaging rule under a shadow change condition, the negative influence of time-varying shadow information on geometric constraints is effectively inhibited, and the quality of a reconstructed image is further improved.

The utility model relates to a light shield and railway inspection equipment, and relates to the field of railway detection. The light shield comprises a light shielding frame and a connecting bending piece. An accommodating area is defined by the shading frame, and an optical filter is mounted on the lower edge of the shading frame; the connecting bending piece comprises a first connecting section, a second connecting section and a third connecting section, the two ends of the second connecting section are perpendicularly connected with the first connecting section and the third connecting section respectively, and the first connecting section and the third connecting section are located on the same side of the second connecting section; the first connecting section and the third connecting section are connected with the upper edges of the two opposite side walls of the shading frame respectively. The light shield provided by the utility model solves the technical problem that a camera equipped on a railway inspection robot in the prior art is poor in image quality or easy to damage.

This invention discloses a signal enhancement method and apparatus for time-slice compressed sensing 3D imaging radar. First, an M×N dimensional random binary matrix is ​​generated as the measurement matrix for compressed sampling. The target area is illuminated using a laser source, and repeated sampling and modulation are performed to obtain a sampling matrix. The j-th column of the sampling matrix is ​​extracted to generate a compressed sampling vector for the corresponding time slice. The covariance between the compressed sampling vector and each column of the measurement matrix is ​​calculated to obtain the compressed sampling covariance vector. The covariance between each column of the measurement matrix and all columns is calculated to obtain the covariance measurement matrix. Based on a compressed sensing 2D reconstruction algorithm, the 2D image of each time slice is reconstructed using the covariance measurement matrix. The reconstructed 2D images of all time slices are stitched together to form a complete 3D image. This method and apparatus can solve the problems of low reconstruction accuracy and poor image quality in existing compressed sensing 3D imaging technologies under noisy environments.

The present invention discloses a Fourier ptychographic microscopy color reconstruction method, apparatus and device, relating to the field of microscopic imaging, and is used to solve the problems of poor reconstruction efficiency and poor image quality in the existing Fourier ptychographic microscopy imaging. It includes: obtaining a high-resolution data set; constructing a Fourier ptychographic microscopy color reconstruction network based on a self-supervised generative adversarial network; setting a loss function for training the Fourier ptychographic microscopy color reconstruction network; using the data set and the loss function to train the Fourier ptychographic microscopy color reconstruction network to obtain a trained Fourier ptychographic microscopy color reconstruction network; inputting the data to be tested into the trained Fourier ptychographic microscopy color reconstruction network to obtain a target FPM color image. The present invention can improve the quality of the FPM color image, reduce the FPM acquisition time, and improve the reconstruction efficiency.

The invention discloses an image quality enhancement and target detection method and system for a high-speed sorting mechanism, and the method and system improve the image quality of to-be-processed first image data through an image quality enhancement model in an application scene of the high-speed sorting mechanism. Therefore, the precision of subsequent target detection is improved, and the feature extraction capability is enhanced. And carrying out first identification operation through the first detection model and carrying out second identification operation through the second detection model by utilizing a multi-model combination mode. The industrial and agricultural raw materials are identified by the two models from different angles, accurate material positioning and multi-dimensional material identification are realized, the identification results of the two models can be optimized, and the accuracy and reliability of detection can be enhanced. In addition, the multi-model combination mode can effectively solve the problem of inaccurate detection caused by poor image quality and limitation of a single model in a high-speed sorting mechanism, so that the sorting speed and precision of industrial and agricultural raw materials are improved.