80 results about "Poor Quality Image" patented technology

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 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 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.

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 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 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.

This invention belongs to the field of magnetic particle imaging, specifically relating to a three-dimensional magnetic particle imaging system, method, and device that fuses imaging parameters for quantitative analysis. It aims to address the problem that existing technologies only focus on the quality analysis of two-dimensional MPI reconstructed images, using a single parameter and failing to comprehensively assess the combined influence of multiple factors on the reconstructed image, resulting in poor quality of the reconstructed MPI three-dimensional image. The system includes: an input module configured to acquire a phantom model of the object to be reconstructed, as well as the imaging parameters and interference parameters of the MPI imaging device; a signal calculation module configured to calculate the induced voltage signal; a reconstruction module configured to perform three-dimensional reconstruction of the object to be imaged based on the induced voltage signal using a three-dimensional image reconstruction method to obtain an MPI three-dimensional reconstructed image; and an output module configured to output the MPI three-dimensional reconstructed image. This invention improves the quality of MPI three-dimensional reconstructed images.

The application discloses a flexible self-adaptive endoscope device and method, and belongs to the field of nondestructive testing.The device comprises a flexible catheter and a flexible lens module at the front end of the flexible catheter, and the module is provided with a filling capsule made of transparent elastic material, a driving mechanism and a shape constraint layer.Through accurate injection or extraction of transparent fluid into the filling capsule, the radial expansion and curvature change of the filling capsule can be actively controlled, so that the lens diameter, focal length and concave-convex surface can be dynamically changed.The application solves the problems of the existing endoscope, such as many blind areas, poor image quality and difficulty in adapting to complex curved paths caused by fixed focal length and fixed structure, and is especially suitable for high-definition and blind area-free visual detection of key parts in narrow and complex structures, such as aero-engines.

The present invention improves image quality in an image sensor using a single-slope type ADC.　A differential amplifier compares an analog signal with a predetermined reference signal and outputs a first output signal indicating a comparison result. An amplification unit amplifies the first output signal and outputs the amplified first output signal as a second output signal. An output unit inverts and outputs the second output signal. A current suppression unit is composed from a predetermined number of metal-oxide-semiconductor (MOS) transistors connected in series to one of a power supply terminal and a ground terminal of the output unit. A quantization error correction unit controls the gate voltage of each of the MOS transistors.

The application discloses an image preprocessing method and system for laser light imaging, and relates to the technical field of image processing. The method comprises the following steps: under consistent laser light and shooting conditions, collecting multiple images of a target area; generating a reference background image according to the multiple images by using a preset algorithm; determining a corresponding difference image according to the reference background image, generating a guide image based on image gradient amplitudes, fusing the guide image into the difference image after normalization to obtain a target difference image; performing two-dimensional Fourier transform on the target difference image to obtain a frequency spectrum image; constructing a two-dimensional mask and performing inverse Fourier transform on the frequency spectrum image after the two-dimensional mask is applied to the frequency spectrum image to obtain a target image. The application solves the problem of poor image quality in the prior art.

The application relates to the technical field of battery manufacturing, and particularly discloses a winding core coating detection method and system. The winding core coating detection method comprises the following steps: obtaining a winding core image collected by an X-ray detection device, and obtaining an area maximum region image after segmentation and cutting; processing the area maximum region image, and extracting positive electrode edge center points and negative electrode edge center points; counting the number of the positive electrode edge center points and the negative electrode edge center points, and if any one is less than the number of winding core layers, it is determined that the winding core coating is unqualified; otherwise, the coating size of each layer is calculated, if the coating size of each layer is within a process detection standard range, it is determined that the winding core coating is qualified, otherwise, it is determined that the winding core coating is unqualified. The application can realize accurate detection of the winding core coating quality in an independent manner; meanwhile, by taking the number of edge points reaching the number of winding core layers as a pre-judgment condition, misjudgment caused by poor image quality or edge extraction failure can be effectively avoided, and the robustness and reliability of detection are improved.

The application discloses a device and method for intelligently investigating fish species and quantity. The device comprises an 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 integrates a three-frequency transducer, which can dynamically switch frequencies according to the depth of the fish school, and realize target tracking counting and quantity estimation in combination with an algorithm; the image acquisition module uses a depth camera in combination with a light attenuation compensation algorithm to construct a three-dimensional image model of the fish school; the preprocessing module fuses acoustic and optical features to expand the data set; the detection and segmentation module accurately locates the fish and removes impurities; and the identification and classification module realizes species identification based on transfer learning and supports incremental learning of new fish species. Through the cooperation of multiple modules, the method solves the problems of insufficient detection accuracy and poor image quality in traditional investigation, realizes efficient and accurate investigation of fish species and quantity, and provides technical support for fishery resource management and ecological protection.

A processor is provided, and the processor specifies a high-attenuation substance region in a projection image acquired by imaging a subject including a high-attenuation substance using a CT apparatus, and derives a corrected projection image by performing correction on the high-attenuation substance region in the projection image to suppress a difference in image quality between the high-attenuation substance region and other regions outside the high-attenuation substance region.

The invention discloses an optic disk and optic cup region segmentation method based on uncertain alignment of federal learning, and belongs to the technical field of medical image processing and artificial intelligence application. The objective of the invention is to solve the problems that the boundary segmentation of an optic disk and an optic cup is unstable and the structure index estimation error is large under the conditions of acquisition equipment difference, patient distribution difference, image quality difference and the like of a multi-center fundus image. And constructing a semantic segmentation model at each client, independently training by using local training data, and predicting output and verifying feedback on a local verification set. And the server side performs uncertainty modeling and alignment on output of the global model and the local model based on Dirichlet distribution, generates a dynamic aggregation weight, performs multi-step aggregation updating in combination with a historical weight, and adaptively adjusts a next local training round according to verification feedback of the client side, so that a final global segmentation model is obtained through iteration. According to the method, the estimation precision of the structure indexes can be improved under the multi-center heterogeneous data condition.