50 results about "Poor Quality Image" patented technology

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 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.

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 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.

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 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 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.

The invention provides an unmanned aerial vehicle electric power grid inspection device and method based on multi-modal vision, belongs to the technical field of electric power equipment inspection, and aims to solve the technical problems of poor image quality, low defect detection accuracy and low manual marking efficiency in traditional unmanned aerial vehicle inspection. The device comprises an unmanned aerial vehicle, a multi-modal image acquisition device and a computer. The method comprises the following steps of: performing contrast enhancement, deblurring and image feature enhancement on an image by using a PGI-Enhance framework; carrying out feature extraction and feature fusion on the preprocessed image by adopting a DTCFuse model; and carrying out equipment defect detection on the fused thermal imaging data by using a thermal imaging defect detector based on deep learning, and when a defect is found, automatically carrying out real-time circling and labeling on an image so as to realize detection on the power equipment.

This invention provides an image acquisition method and system, an in vitro diagnostic device, a medium, and an electronic device. The image acquisition method includes: obtaining the current distance between a lens and a target object; adjusting the focal length of the lens according to the current image distance of the lens and the current distance between the lens and the target object to focus the lens on the target object; acquiring a first image of the target object captured by the focused lens; obtaining the original coordinates of each pixel in the first image; obtaining a scaling matrix according to the current distance between the lens and the target object and a reference distance; correcting each original coordinate using a distortion mapping table and the scaling matrix to obtain multiple target correction coordinates; and outputting a corrected image based on the multiple target correction coordinates. This improves problems such as image blurring and poor image quality caused by insufficient lens focusing accuracy, changes in the height between the lens and the target object, and optical distortion of the lens.

The invention discloses an invoice intelligent identification method and system based on an AI technology, and relates to the technical field of artificial intelligence identification. Carrying out 1 * 1 convolution on the target invoice image and then substituting the target invoice image into a ReLU activation function to obtain a first feature; substituting the first feature into a shadow detection layer to obtain a shadow feature, performing 3 * 3 convolution operation, and fusing the shadow feature with the first feature to obtain a second feature; substituting the second feature into the illumination correction layer to obtain a third feature; correcting the third feature by using the shadow feature, fusing the corrected third feature with the target invoice image, and enhancing to obtain an enhanced invoice image; performing text intelligent recognition on the enhanced invoice image through an OCR technology; a shadow area is positioned through the shadow detection layer, insufficient exposure is improved through the illumination correction layer, original features and processed features are fused for multiple times to reserve text details, the problems of low recognition accuracy and even incapability of recognition caused by poor image quality of a traditional OCR are solved from the source, and the invoice recognition accuracy is improved.

The invention relates to the field of mode recognition, in particular to a mobile terminal face recognition and photo quality detection method and system based on deep learning, and the method comprises the steps: collecting an entrance video image of an examinee in an examination room in real time, and obtaining a face image I; inputting the image I into an improved RetinaFace network for face detection to obtain a face positioning frame and face region feature point coordinates; performing multi-dimensional quality detection on the face image; carrying out image quality enhancement on the face image passing the quality detection and then carrying out alignment processing; inputting the aligned face image into an improved MobileNetV3 network to extract a depth feature, and obtaining a face feature vector; calculating the cosine similarity between the face feature vector and a feature vector in a preset face feature library; and judging whether the person is the same person. The problems that the recognition failure rate is high and frequent recollection is needed due to poor image quality are effectively solved, and the smoothness and efficiency of examinee entrance verification are improved.

The application discloses an invoice intelligent identification method and system based on AI technology, and relates to the field of artificial intelligence identification technology; a target invoice image is subjected to 1*1 convolution, and then substituted into a ReLU activation function to obtain first features; the first features are substituted into a shadow detection layer to obtain shadow features, and then subjected to 3*3 convolution operation and fused with the first features to obtain second features; the second features are substituted into an illumination correction layer to obtain third features; the third features are corrected by the shadow features, fused with the target invoice image, and then enhanced to obtain an enhanced invoice image; the enhanced invoice image is subjected to text intelligent identification through an OCR technology; the shadow detection layer is used to locate a shadow area, the illumination correction layer is used to improve insufficient exposure, and the original features and the processed features are fused repeatedly to retain text details, so that the problem of low recognition accuracy or even unrecognizability caused by poor image quality of traditional OCR is avoided from the source, and the invoice recognition accuracy is improved.

The invention discloses a target detection method based on adaptive ISP, and relates to the related field of target detection, and the method comprises the steps: obtaining RAW image data which is a target detection image data set in a traffic scene; inputting the RAW image data into an RAW parameter encoder to generate a shared encoding feature vector; inputting the coding feature vector into an RAW parameter decoding module, and outputting a plurality of functional parameters; according to the multiple functional parameters, ISP sub-function transformation is applied to the RAW image data, and multiple ISP sub-images are determined; and fusing the plurality of ISP sub-images to generate an ISP image, and inputting the ISP image to the target detection model to obtain a target detection result. The technical problem that the target detection effect is affected due to poor image quality in existing target detection is solved, and the technical effects of improving the image quality and further improving the target detection accuracy are achieved.

The invention provides an illumination perception lightweight defect detection method, system and equipment in a low-light environment, and belongs to the field of intelligent inspection and automatic defect identification. According to the method, an illumination perception lightweight YOLO framework is constructed, and the following operations are executed in sequence: obtaining a low-light environment image; low-light enhancement and lightweight feature extraction are realized on a feature level by using an illumination adaptive enhancement feature extraction network; performing multi-scale feature fusion through a hybrid path aggregation network; and outputting a detection result based on a dynamic space attention mechanism by adopting a dynamic feature fusion detection head. By adopting the illumination perception lightweight defect detection method, system and equipment in the low-light environment, the problems of poor image quality, large model calculation amount, small target leak detection and strong background interference in the low-light environment are solved, and the method, system and equipment have the advantages of high detection precision, few model parameters and high reasoning speed; the system is suitable for intelligent inspection of infrastructures such as sewers and pipe galleries.

To provide an image processing device, a method, and a program that can rotate an image and output it without causing a difference in image quality compared with an image output without rotation.SOLUTION: An image processing device includes a storage serving as a memory unit for storing a plurality of dither matrices, an acquisition unit for acquiring an image, and a control unit for selecting one of the plurality of dither matrices on the basis of information regarding dot arrangement, performing halftone processing, and generating an output image when performing a process for rotating the image acquired by the acquisition unit.SELECTED DRAWING: Figure 3

The application discloses a coagulation floc image acquisition system for experiments, which comprises a workbench and a subsystem, a pipeline pull rod (44) is fixedly connected with the workbench, a circulating water pump (5) is arranged in a bottom water tank (22), the bottom water tank (22) is arranged on a lower plate surface (2) of the workbench, a medicament water tank (11) is arranged on an upper plate surface (1) of the workbench and is provided with a first stirrer (12) at the top, a mixed water tank (14) is arranged on the upper plate surface of the workbench and is provided with a second stirrer (13) at the top, the mixed water tank (14) is fixedly connected with a water passage corridor (15), an image acquisition water tank (16) is arranged on the upper plate surface (1) of the workbench and is fixedly connected with the water passage corridor (15), and the problems that, in the prior art, a floc image is greatly influenced by environmental light, cannot be used due to large-area overexposure, floc evaluation dimension is reduced, reliability is reduced, image acquisition efficiency is low, reliability is reduced, image quality is poor, a decision error of a machine is large and intermittent work can only be carried out are solved.

The application discloses a synchronous rotation encoding single-pixel imaging method for a high-speed rotating object and a medium, and the method comprises the following steps: generating synchronous rotation encoding patterns in sequence; burning the synchronous rotation encoding patterns into a high-speed DMD; when the rotating object rotates counterclockwise by one degree, the high-speed DMD projects a synchronous rotation encoding pattern to the center position of the surface of the rotating object to be measured; then, the light intensity signals reflected by the surface of the rotating object are collected until the synchronous rotation encoding pattern is projected completely; finally, all the collected light intensity signal values are input into a computer, and the image is reconstructed through image recovery program processing. The application can solve the problem of poor image quality of the reconstructed rotating object by using synchronous rotation encoding, and the synchronous rotation encoding pattern suitable for the imaging of the rotating object is designed according to the relationship between the rotating angle of the object and the synchronous rotation encoding pattern by using four-step phase shift Fourier single-pixel imaging technology.

The invention belongs to the field of medical health data information processing, and particularly relates to an ostomy bag base information generation method and system based on an ostomy state acquisition result, and the system comprises a medical image acquisition unit, a medical data processing unit and a design unit. The medical image acquisition unit is used for acquiring multi-modal medical image data, at least including a color image and a depth image, of a stoma area of a patient, and the medical image acquisition unit comprises an annular light equalizing module for providing shadowless illumination and a user guide interface for guiding a user to complete standard pose acquisition; by adopting the annular light equalizing module and the image data acquisition unit, the problem of poor image quality caused by non-uniform illumination of an acquired environment is solved, meanwhile, the authenticity and shadowless of the color and texture of a two-dimensional color image are ensured by annular light equalizing, and the three-dimensional geometric information is directly acquired by the depth sensor, so that reliable input is provided for high-precision segmentation and reconstruction, and the image quality is improved. And the defect of insufficient information of a single sensor is avoided.