2708 results about "Imaging Signal" patented technology

The invention provides an intelligent image signal processing method and system based on multi-modal fusion. According to the invention, a multi-mode input signal is received, is divided into a spatial distribution feature extraction region and a dynamic change trajectory capture region, and is decomposed into a penetrability feature layer and a substance reflection feature layer; carrying out association mapping on missing pixel information in the dynamic change trajectory capture region and energy distribution of the penetrability feature layer to generate enhanced dynamic trajectory data, identifying a determined reflection mode in the substance reflection feature layer, and carrying out frequency domain superposition on corresponding frequency band response and the spatial distribution feature extraction region; generating a composite spatial feature, then constructing a multi-modal joint optimization model, then adjusting the contribution ratio of the two data, and generating a fused image signal; the technical scheme provided by the invention not only solves the problems of detail loss, artifact generation and poor dynamic adaptability caused by single-mode limitation, but also improves the spatial resolution and tracking precision of the image signal.

The invention discloses a super-resolution image enhancement system and method based on a variational mode decomposition algorithm. The system comprises an adaptive decomposition module, an enhancement processing module, a fusion module and an optimization module. The adaptive decomposition module receives low-resolution image signals, generates modal component signals containing different frequency band characteristics, and outputs modal quantity parameter signals according to image frequency domain energy distribution. The enhancement processing module comprises a high-frequency enhancement unit and a low-frequency reconstruction unit, and generates a high-frequency enhancement signal and a low-frequency reconstruction signal. And the fusion module receives the modal quantity parameter signal, the high-frequency enhanced signal and the low-frequency reconstructed signal, and performs spatial adaptive weighted fusion on the high-frequency signal and the low-frequency signal through a dynamic weight coefficient to generate an initial high-resolution signal. And the optimization module carries out adaptive nonlinear filtering processing on the initial high-resolution signal. The super-resolution image enhancement system based on the variational mode decomposition algorithm can solve the problem that the prior art is difficult to adapt to a complex image structure.

The invention relates to an underwater robot navigation positioning method and system. The method comprises the following steps: S1, acquiring angular velocity and acceleration signals through an inertial measurement unit; s2, resolving a three-dimensional velocity observation value according to the beam radial velocity vector signal in combination with the angular velocity signal, and extracting environment feature point cloud data according to the acoustic image signal; s3, multi-source data time synchronization is carried out, and a fusion input signal with time-space alignment is generated; s4, constructing an adaptive factor graph optimization model, and dynamically adjusting an inertial navigation solution node based on a real-time weight coefficient; inputting the environment feature point cloud data into a closed-loop detection module to generate a loopback factor node, and adaptively correcting the weight of the node according to the feature matching degree; and S5, solving the adaptive factor graph optimization model through a nonlinear optimization algorithm. According to the underwater robot navigation positioning method and system, the problem that the fusion positioning precision of a multi-source heterogeneous sensor is insufficient in an underwater GPS-free environment can be solved.

The present invention relates to the technical field of cross-modal image signal reconstruction. Disclosed is a cross-modal joint source-channel coding and decoding method adaptable to changeable scenarios. The method comprises: first designing a Transformer encoder-based cross-modal channel coding and decoding optimization solution, so as to achieve the performance improvement and robustness of a channel encoder and a channel decoder; then designing a cross-modal source coding and decoding optimization solution for haptic-to-image generation based on a latent diffusion model, so that under an image signal loss scenario, haptic information is used to guide image generation; and finally, incorporating transfer learning technology, so as to reduce additional training costs caused by a system facing changeable cross-modal communication scenarios such as a changeable channel signal-to-noise ratio and different transmission tasks. Under cross-modal changeable communication scenarios, the joint source-channel coding and decoding method provided in the present invention can solve the problems of the inability of a receiving end to well complete image reconstruction, and additional model training costs caused by changeable channel environments and scenarios.

The invention belongs to the field of machining, and relates to a machining process quality online monitoring and feedback adjusting system and method. The system comprises a multi-modal data acquisition module used for acquiring a mechanical vibration signal and a high-frequency micro-damage signal; the feature preprocessing and cross-modal fusion module is used for carrying out time domain, frequency domain and time-frequency domain feature extraction on the collected mechanical vibration signals and high-frequency micro-damage signals, carrying out edge, texture and shape feature extraction on workpiece machining surface image signals, and introducing a cross-modal attention mechanism to achieve weighted fusion of heterogeneous signals. And finally generating a fusion feature vector with high discrimination. The machine tool / cutter state and workpiece quality intelligent analysis module is used for constructing an intelligent analysis model for the equipment operation state and the workpiece machining quality; inputting the fusion feature vector into an intelligent analysis model to obtain a quality evaluation result; and the quality judgment and feedback adjustment module is used for adopting a dynamic threshold adjustment mechanism to realize adaptive comparison with a process standard according to a quality evaluation result.

The invention relates to the technical field of digital surveying and mapping of constructional engineering and construction quality control, and discloses a house surveying and mapping system for constructional engineering, which comprises a data acquisition module for synchronously generating a panoramic image signal and an inertial measurement unit signal; the preprocessing module receives a panoramic image signal and an inertial measurement unit signal, and fuses the panoramic image signal and the inertial measurement unit signal through a visual inertial odometer to generate a pose calibration signal; the fusion modeling module receives the purification point cloud signal and the pose calibration signal, and generates building component parameterized data based on a deep learning component recognition algorithm; the result output module receives the building component parameterized data and generates a construction deviation analysis report based on a preset tolerance threshold value; the intelligent control module generates an acquisition quality feedback signal through a real-time quality evaluation algorithm to the data acquisition module so as to adjust a scanning path, and sends a dynamic sampling rate control signal to the preprocessing module so as to optimize a calculation load. The problems of low house surveying and mapping efficiency, poor modeling precision and weak field real-time feedback are solved.

The invention discloses a multi-parameter monitoring and intelligent early warning system and method for a mine car dry disc braking device. The system comprises a data processing module, and a high-temperature fiber grating sensor, a laser displacement sensor, a piezoelectric vibration sensor, an industrial camera and an early warning execution module which are in signal connection with the data processing module. The data processing module corrects and processes the acquired temperature signal, thickness signal, vibration frequency signal and surface image signal, judges the wear state of the current brake device according to the signal processing result, and judges whether to send an early warning signal to the early warning execution module or not according to the wear state; and the early warning execution module implements corresponding early warning action according to the early warning signal. According to the method, the problem of misjudgment caused by single parameter monitoring is effectively solved, and the fault recognition accuracy is remarkably improved through multi-dimensional data cross validation. Seamless connection from state monitoring to safety protection is achieved through a closed-loop control system. And the multi-sensor cooperative working mode can effectively ensure the detection precision.

The invention relates to the field of image data processing, and discloses a texture preserving type image denoising and enhancing method based on a generative adversarial network, which comprises the following steps: acquiring an original image signal, and calculating low-frequency sub-band data and high-frequency sub-band data by using discrete wavelet transform; calculating the gradient magnitude of the low-frequency sub-band data to generate a structural significance gradient map; establishing a reverse mapping relation based on the structure saliency gradient map, and generating a spatial self-adaptive dynamic gating threshold; performing statistical gating on the high-frequency sub-band data by using the dynamic gating threshold to generate a high-pass gain coefficient and a low-pass suppression coefficient; according to the method, cross-band modulation logic of the structure flow to the texture flow is established, so that the problem that weak texture signals are easy to lose under non-uniform illumination is solved, and non-structured noise filtering and structured micro texture restoration are realized on the premise of not depending on semantic tags.

A system and method are disclosed for low-light image enhancement using hierarchical adaptive wavelet decomposition with cross-scale feature fusion. The system analyzes a raw input image to determine image characteristics and preprocessing parameters. A hierarchical adaptive wavelet decomposition process creates a variable-depth decomposition tree comprising frequency domain nodes, with decomposition depth determined by local image complexity. Cross-scale feature fusion implements attention mechanisms between nodes at different decomposition levels, enabling bidirectional information flow across scales. A dynamic network pool allocates specialized neural networks to process nodes based on their frequency characteristics, with weight sharing between similar nodes for efficiency. An adaptive reconstruction engine traverses the decomposition tree using learned filters and multi-scale residual learning to produce an enhanced image. The hierarchical approach enables superior low-light image enhancement by allocating computational resources based on content complexity, achieving better quality than fixed decomposition methods while maintaining compatibility with existing image signal processing pipelines.

The invention relates to the technical field of image enhancement, and discloses an image enhancement method based on an ISP image signal processing visual sensor, and the method comprises the steps: carrying out the collection of multiple frames of images of the same scene, carrying out the analog-to-digital conversion of the collected multiple frames of images, generating an RGB three-channel digital matrix, and obtaining N frames of original image sequences; dark current correction and lens shadow correction are executed, an affine transformation matrix from the N-1 frame to the first frame of image is calculated based on SIFT feature point extraction and matching, and an image sequence after space alignment is obtained; performing spatial feature extraction to obtain a multi-scale feature map sequence, and performing residual feature connection operation to obtain a time sequence correlation feature map sequence; according to the method, attention calculation of the channel dimension and the space dimension is executed, meanwhile, structural layer edge enhancement and texture layer denoising processing are executed, a target enhanced image is obtained, various kinds of distortion and noise interference in the image acquisition process are reduced, and the definition and the detail fidelity of the enhanced image are ensured.

The invention provides an offshore wind turbine generator fault diagnosis method and system based on multi-source sensor data fusion. The method comprises the steps that a vibration signal from at least one component of a wind turbine generator is acquired through a vibration sensor; performing time-frequency conversion on the vibration signal by applying synchronous compression wavelet transform to obtain time-frequency representation of the vibration signal; when the reconstruction error exceeds a preset threshold value, it is judged that an abnormal event exists in the vibration signal; obtaining the position of a part corresponding to the abnormal event; starting an image sensor and an acoustic sensor according to the position of the component, and acquiring an image signal and a sound signal of the component according to the image sensor and the acoustic sensor; according to the DS evidence theory, the vibration signal, the image signal and the sound signal, obtaining the confidence of the fault type; the fault type of the component is judged according to the maximum confidence allocation principle, high-resolution time-frequency analysis can be achieved through synchronous compression wavelet transform (SST), and the fault feature identification degree is improved in combination with the self-encoding neural network and the D-S evidence theory.

The invention relates to the field of image signal processing, in particular to a household camera imaging quality detection method and system. Comprising the following steps: acquiring a to-be-detected image sequence shot by a camera in a preset scene; carrying out regional preprocessing on the to-be-detected image sequence; extracting motion feature parameters of a dynamic target area and static feature parameters of a static background area in the preprocessed to-be-detected image sequence; inputting the motion characteristic parameters and the static characteristic parameters into a preset imaging quality evaluation model, and outputting a comprehensive quality score; and obtaining an imaging quality grade of the camera based on a comparison result of the comprehensive quality score and a preset threshold value. According to the invention, by constructing the preset scene including the dynamic target area and the static background area, the composite environment of the household scene is accurately simulated; meanwhile, illumination gradient change control is introduced, a typical household illumination range is covered, and the problem of detection distortion caused by a single scene in an existing method is solved.

A system of USB communicating camera comprising an integrated USB camera further comprising a USB communicating camera with a sensor, an image signal processor and integrated with a microcontroller having an image processing module, an inference unit, a first USB interface, a flash memory; a host system with a processor, a user interface application and a second USB interface to communicate to and from the USB communicable camera, the system is user configurable for different application specific pre-trained machine learning models. The user interface application is adapted to receive an application specific configuration data. The inference unit is an artificial intelligence inference unit that draws inference on image frames based on an input and the corresponding pre-trained machine learning model. The system deploys edge computing. A firmware residing in the flash memory remains unchanged when different application specific pre-trained machine learning models are loaded in the flash memory.

An imaging device includes an imaging element unit that is exposed to incident light and that outputs a captured image signal, a notification unit, and a control unit that performs control in such a way as to cause the notification unit to perform notification corresponding to still image capture exposure, which is performed by the imaging element unit to generate a still image in response to a still image capture instruction, in a period different from an exposure period of the still image capture exposure.

A doorbell device includes a housing, an optical module, a circuit board, and a button assembly disposed in an opening of the housing. The optical module is disposed in the housing and is controllable to power up to photograph an environmental image and outputs an image signal; the circuit board is disposed in the housing and has several electronic components, a thermally conductive circuit, and a sensing switch on an outer surface of the circuit board; the electronic components includes an auxiliary function component and a primary function component electrically connected to the sensing switch, the optical module, and the auxiliary function component; the thermally conductive circuit is laid out on the outer surface, is electrically connected to the electronic components, and has an extending segment located in a sensing area of the circuit board; a pressing board corresponds to the sensing area and the sensing switch.

The invention discloses an image signal processing system and method, and relates to the technical field of image signal processing, and the method comprises the steps: extracting structural features of a collected original image signal, and generating a region classification mask; predicting a processing path of each region according to the region classification mask; according to the processing path, performing corresponding compression operation on each area, and embedding corresponding structure identification information in compressed data; and during decoding, enhancement processing is performed on the structure key area according to the structure identification information, so that the image restoration quality is improved. The continuous adjustment of the channel reservation number and the quantization step size is realized, and the fidelity control capability of a complex structure region is enhanced. And the image restoration quality and the detail retention effect are improved, and a closed-loop optimization process between compression and enhancement is constructed.

The invention discloses a composite insulator defect detection method and system based on a multi-source signal and VMD decomposition, and the method is characterized in that the method belongs to the technical field of power system and equipment monitoring, and comprises the steps: synchronously collecting an ultrasonic echo signal, a mechanical vibration signal and an infrared thermal image signal of a composite insulator, and carrying out the timestamp alignment; performing CEEMDAN denoising and VMD decomposition on the ultrasonic echo signal in sequence to extract time-frequency domain features, and generating an ultrasonic feature vector; performing frequency domain transformation on the mechanical vibration signal to extract fundamental frequency and harmonic characteristics, and generating a vibration characteristic vector; performing surface temperature abnormal feature extraction on the infrared thermal image signal to generate an infrared auxiliary feature vector; the ultrasonic feature vector, the vibration feature vector and the infrared auxiliary feature vector are spliced into a fusion feature vector, and after dynamic weight distribution, the fusion feature vector is input into a multi-layer perceptron model for defect classification; and analyzing and positioning the internal defect position of the composite insulator by combining the ultrasonic signal propagation path time delay and the vibration mode, and generating a three-dimensional visual report.

The invention belongs to the field of computer vision image signal processing, and relates to a multi-robot collaborative indoor scene semantic segmentation method and system based on multivariate interactive learning. The method comprises the steps of adopting an image feature encoder based on a double attention integration module to realize cross-modal feature fusion on an RGB image and a depth image; a dimension anisotropy attention enhancement module is adopted, and feature optimization is carried out through a multi-dimensional direction attention mechanism; a cross-task collaborative interaction module is adopted, and information is shared among different tasks by utilizing an interaction mechanism based on a feature level; in a multi-task heterogeneous decoding stage, each robot uses a decoder with a specific task to execute a specific task, and semantic segmentation, instance segmentation and scene classification are realized; and a joint optimization loss function is adopted to perform end-to-end joint training on multiple tasks, so that overall optimization and collaborative awareness performance improvement are realized. According to the method, the precision and robustness can be improved when complex indoor scene semantic segmentation and other visual tasks are executed.

The invention relates to an unmanned aerial vehicle inspection method and system based on an overhead transmission line, and the method comprises the steps: S1, generating an unmanned aerial vehicle three-dimensional space coordinate signal, and outputting a position correction signal based on a tower topology database; s2, in response to the position correction signal, controlling a multispectral sensing module to synchronously acquire a visible light image signal, an infrared temperature signal and a laser radar point cloud signal of the line; s3, inputting the visible light image signal into a defect identification model to generate a first defect feature signal; s4, fusing the first defect characteristic signal and the second defect characteristic signal to form a cross-modal diagnosis signal, and triggering a defect classification decision signal; and S5, classifying decision signals and cross-modal diagnosis signals according to defects. According to the air quality intelligent monitoring method and system based on sensing data feedback, the problems of inaccuracy in routing inspection positioning, defect missing detection and insufficient endurance in a complex environment can be solved.

The present application provides a battery thermal runaway detection method and apparatus. The method comprises: acquiring a time series signal generated when a gas sensor identifies a gas component released by a battery, and converting the time series signal into an image signal, so as to strength key features in the signal; processing the image signal by means of a target detection model, so as to predict a gas type and a gas concentration; and then determining a thermal runaway risk of the battery on the basis of a prediction result. Therefore, by introducing a machine learning algorithm, simple gas identification requirements are met, and complex data analysis and prediction can be carried out, thereby accurately predicting the thermal runaway risk of lithium batteries, and effectively improving the early warning precision of thermal runaway.

The invention provides a self-adaptive beam spot calibration method and system based on small-angle X-ray scattering equipment, and the method comprises the steps: simulating and optimizing the thickness of a Ni filter and a pinhole combination through a Monte Carlo principle, and obtaining an optimized ray light source; irradiating the detector through a ray light source to complete the initial calibration operation of the detector; collecting an image signal representing the scattering characteristic of the sample; outputting beam spot morphological characteristic data; and finally, determining that calibration is completed through multi-index statistical test. The method comprises the following steps: checking the stability of a light source, finishing initial calibration operation, controlling a water cooling mechanism, executing power grading loading, ensuring stable operation of a light source module, providing stable light source calibration conditions, and finally, quickly positioning a position with better beam spot quality in a coarse adjustment stage through a spiral search algorithm. And then fine adjustment is carried out in a fine adjustment stage by using the self-adaptive PID controller to judge that calibration is completed, so that the stability and the sensitivity of the system are ensured.

The invention relates to the technical field of power transmission line detection, and provides a high-voltage overhead power transmission line insulator string detection device and a control method thereof. Comprising an unmanned aerial vehicle body, and an airborne data transmission module, an airborne insulator detection module and an image acquisition and analysis module which are carried on the unmanned aerial vehicle body, the image acquisition and analysis module is used for acquiring an image signal of the insulator string and analyzing the image signal so as to output a position control signal of the unmanned aerial vehicle and an angle control signal of the airborne insulator detection module; the unmanned aerial vehicle body flies to a specified position based on the position control signal; the airborne insulator detection module is used for rotating to a specified angle based on the angle control signal and detecting an insulator string; and the airborne data transmission module is used for receiving the detection data sent by the airborne insulator detection module and determining the health state of the insulator chain based on the detection data. Zero-value insulator live-line detection of a full-voltage-class full-string type insulator string of a power transmission line can be realized.

According to the multi-mode neural learning network model, method, device and medium, after electroencephalogram signals and original images are coded, electroencephalogram features and image features are obtained, then the similarity of the electroencephalogram features and the image features is calculated, comparative learning of the electroencephalogram signals and the original image signals is completed, and the accuracy of the electroencephalogram signals and the original image signals is improved. The encoding of the original image comprises the steps of obtaining a first primary feature of an embedded representation level based on the original image, obtaining a second primary feature of the embedded representation level based on the filtered image, and fusing and reasoning the primary features, so that the embedded representation level primary features of the original image and the filtered image are respectively extracted through a double-branch image path; and an attention gating mechanism is adopted to carry out embedded representation level fusion, so that self-adaptive adjustment of an image modal structure is realized, the problem of a single path of traditional image coding is solved, and the alignment capability of a visual modal is effectively enhanced.

An image sensor for color imaging using arbitrary-color-filter-array event data is provided. The image sensor comprises: a plurality of color imaging pixels and a plurality of color event pixels. An image signal of a first color imaging pixel included in the plurality of color imaging pixels is determined based on a first color signal of the first color image pixel and at least a color event data of one or more color event pixels included in the plurality of color event pixels. The color event data is generated in a temporal relation to the generation of the first color signal.

A signal processing method includes obtaining an image signal and a first event signal that are of a target scene, where the first event signal is an event signal in a frame format or a stream format; performing time-dimension and / or space-dimension format conversion on the first event signal, to obtain a second event signal, where the second event signal is an event signal in a frame format; and fusing the second event signal and the image signal, to obtain a fused signal.

The invention discloses an underwater image de-scattering method, system and device for scattering field decoupling in the technical field of underwater optical imaging and computer vision, and the method comprises the steps: calculating an intensity graph and a scene linear polarization degree graph according to an obtained multi-scale underwater image; according to the intensity image, performing image signal decomposition by using a dynamically constrained background light separation model to obtain a background scattering component and a target feature component; according to the scene linear polarization degree map and the background scattering component, calculating fusion transmissivity according to a non-linear optical transmission equation; carrying out noise suppression processing on the fusion transmissivity by adopting a double-constraint optimization architecture to obtain the fusion transmissivity subjected to noise suppression processing; and performing image reconstruction through a reverse optical propagation model according to the fusion transmissivity and the target feature component subjected to noise suppression processing to obtain a scattering-removed underwater image. The method effectively breaks through the dependence of a traditional method on the uniform hypothesis of a scattered field, and provides high-precision visual restoration support for an underwater detection task.

The invention discloses an LED liquid crystal display screen brightness and color difference detection and correction method and system, and belongs to the technical field of liquid crystal display. Initial brightness values and color values of all display units of a display screen are obtained; constructing a brightness and color difference reference model based on the convolutional neural network, and calculating a brightness deviation delta L and a color deviation delta C between the brightness and color difference reference model and a standard value; constructing a brightness-color difference error matrix W; according to the error matrix and in combination with a self-adaptive threshold algorithm, identifying a brightness-color difference abnormal region and corresponding coordinates of the brightness-color difference abnormal region; based on a deviation scoring result, introducing a gradient regression model to generate a brightness gain coefficient and a color adjustment coefficient, and constructing a correction parameter set P; the parameter set is issued to a control chip, dynamic image signal modulation and local frame level refreshing are executed, and point-by-point brightness and color difference correction is achieved; the method has the advantages of high precision, high automation, high adaptive capacity and the like, and is suitable for improving the brightness and color difference uniformity in various LED liquid crystal display screen scenes.

According to embodiments, a system for imaging a blood flow in a region of interest of a patient includes: a display configured to display an image; a processor configured to execute the instructions to: obtain ultrasound imaging data based on an imaging signal; select the region of interest in the ultrasound image data; determine at least one characteristic of a blood flow in the region of interest; generate an animation indicating the at least one characteristic of the blood flow in the region of interest, wherein the animation indicates the blood flow during a single time slot; and control the display to display a static image including the region of interest and the animation within the region of interest.

A camera module includes an imaging lens having an optical axis, a variable through hole mechanism, an image sensor, an image sensor actuating module and a flexible printed circuit. The variable through hole mechanism is disposed corresponding to the imaging lens to adjust an amount of light into the imaging lens. The image sensor is disposed on the optical axis and is configured to convert imaging light passing through the imaging lens into an image signal. The image sensor actuating module is configured to move the image sensor. The variable through hole mechanism and the image sensor actuating module each have a coil disposed on the flexible printed circuit. The flexible printed circuit is electrically connected to the variable through hole mechanism and the image sensor actuating module.

The invention relates to a naked eye 3D binocular image acquisition and real-time processing system based on hardware synchronous triggering, and belongs to the technical field of image processing and three-dimensional display. According to the system, a hardware synchronous triggering mechanism is adopted, a time sequence synchronous control unit is used for sending a synchronous pulse signal to a binocular image sensor, and strict synchronization of left and right viewpoint image acquisition is ensured. The image signal processing unit processes collected original data, the stereo parallax correction module performs epipolar correction, and the sub-pixel interleaving module generates a composite view frame according to grating parameters of the display terminal. The system realizes high-speed data transmission through a double-buffer direct memory access DMA mechanism. The problems of visual tearing and weak stereoscopic impression caused by asynchronous binocular image acquisition in the prior art are solved, nanosecond-level synchronization precision is realized, optical crosstalk is reduced, and smoothness and comfort of stereoscopic display are ensured.