3364 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 relates to an automatic data labeling method of an ISP image signal processing visual sensor. The method comprises the following steps: carrying out noise reduction and signal enhancement on an original image data stream acquired by an ISP image signal processing visual sensor to obtain a standardized image sequence; performing time sequence feature extraction to obtain a time sequence feature tensor; performing spatial feature extraction to obtain spatial-temporal feature representation; inputting a double-fold teacher-student network for semi-supervised training to obtain an initial image annotation model; performing quality evaluation based on the prediction probability distribution and the time sequence consistency to obtain a labeling result with a quality score; and storing a high-quality sample in the labeling result with the quality score into a sample buffer pool, carrying out sample screening based on entropy and distance measurement, and carrying out parameter updating on the initial image labeling model through an incremental learning strategy to obtain a target image labeling model. According to the invention, the reliability evaluation of the labeling result is realized, and the accuracy of automatic labeling is ensured.

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 discloses an image super-resolution reconstruction method based on deep learning, and belongs to the technical field of image signal processing. Comprising the following steps: S1, realizing multi-dimensional feature representation of a low-resolution input image by using a deep learning network; s2, realizing accurate alignment and lossless mapping of cross-scale features, and effectively solving the problems of feature dimension inconsistency and information distortion; s3, explicitly learning an increment mapping relation between low-resolution input and a reconstructed high-resolution image, and relieving the gradient disappearance problem of a deep learning network structure; s4, enhancing the naturalness and detail fidelity of the reconstructed image through adversarial training; s5, integrating pixel-level reconstruction loss, perception loss and structural similarity loss, and effectively balancing objective indexes and subjective visual quality of the reconstructed image; and S6, realizing joint optimization of deep learning network parameters through a unified gradient back propagation mechanism, and finally outputting a high-quality super-resolution reconstructed image.

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 invention belongs to the technical field of image processing, and particularly relates to a cord fabric production defect identification and analysis system based on machine vision, which comprises a multispectral imaging module, an image preprocessing module, a target detection module, a result verification module, a weight correction module and a control response module, the method comprises the following steps: acquiring a multispectral image sequence of each partition of a cord fabric forming surface, combining motion compensation, geometric calibration and noise filtering to improve preprocessing precision, and generating a composite spectral image by adopting adaptive weight fusion on the basis of considering an image signal-to-noise ratio and texture definition; and detecting defects based on frequency division domain texture and morphological gradient parameter bimodal matching, further verifying detection credibility through defect sensitive spectral image feature enhancement degree, dynamically correcting a fusion weight and performing iterative optimization, and finally triggering a production control instruction according to a detection result. And closed-loop management of high-precision defect detection and production control linkage of the cord fabric is realized.

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.

A system and method include a first device and a vehicle in communication with the first device. The vehicle includes a front trunk, a camera disposed within the front trunk; and a vehicle controller controlling the camera to capture an image for the front trunk and communicating the image in an image signal to the first device.

Imaging with reduced noise is disclosed. In one example, an imaging element includes a pixel block including a first pixel and a second pixel that performs photoelectric conversion of incident light of a wavelength different from that of the first pixel. An electric charge holding unit holds electric charges of the first and second pixels, and a signal generation unit generates an image signal based on the held electric charges. A pixel block controller controls transfer of the electric charges of the first pixel to generate a first image signal and of the second pixel to generate an added image signal. A signal processing unit switches a first mode for outputting the first image signal and the added image signal and a second mode for outputting the first image signal and a second image signal obtained by subtracting the first image signal from the added image signal.

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 a detection system and method of a display control panel, equipment, a medium and a program product, and relates to the technical field of equipment detection, and the system comprises a test host, and a mainboard to be detected and a detection device which are connected with the test host; the mainboard to be tested comprises a display screen driving module; the test host is used for respectively outputting a test image signal to the mainboard to be tested and the detection device; the to-be-tested mainboard is used for performing signal conversion on the test image signal based on the display screen driving module to obtain a display screen driving signal and outputting the display screen driving signal to the detection device; and the detection device is used for carrying out pixel comparison on image data in the test image signal and the display screen driving signal, and determining a detection result of the mainboard to be detected based on a pixel comparison result. Based on the device, the test precision, efficiency and consistency of the display control panel can be effectively improved, the uncertainty caused by manual intervention is reduced, the test cost is reduced, and the device has high universality and adaptability.

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.

The invention belongs to the field of electroencephalogram signal analysis, and particularly relates to an asynchronous EEG-fNIRS-oriented brain image signal causal analysis method and device, and the method comprises the steps: creating a psychological experiment normal form for guiding a patient to carry out a cognitive ability test; collecting and preprocessing asynchronous EEG and fNIRS signals generated by the patient in the process of executing the psychological experiment normal form; extracting spatial features from the fNIRS signals to process EEG signals, so that the two signals are spatially aligned; time sequence features are extracted from the EEG signals to process the fNIRS signals, so that the time sequence features and the fNIRS signals are aligned; and analyzing the causal relationship between the EEG and the fNIRS signal after space and time alignment. According to the method, the spatial-temporal information of various modal signals collected by asynchronous non-joint equipment is fully utilized, the flexibility and accuracy of the method can support deep analysis of brain region states, and an effective tool is provided for neurovascular coupling analysis and clinical application of EEG and fNIRS.

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 belongs to the technical field of backlight module regulation and control, and particularly discloses a partition dynamic dimming control system and a partition dimming parameter self-learning method. A backlight control module; a sensor module; a power management module; through cooperation of the image signal processing module, the backlight control module, the sensor module and the power supply management module, backlight brightness of different areas can be accurately adjusted according to image content, and when a black or dark hue image is displayed, the backlight brightness of the corresponding area is reduced and even can be completely closed, so that the light leakage phenomenon is reduced, and the display quality is improved. According to the backlight module, the brightness of the image is adjusted, the purity of black is improved, the contrast ratio of the image is remarkably improved, specific brightness adjustment can be conducted on the bright part and dark part details in the image through partition dynamic dimming, the phenomenon of uneven brightness or flickering in different areas is reduced, and the dimming cost of the backlight module is reduced.

The invention provides a calcium imaging neural signal extraction method and product based on self-supervised pre-training, and relates to the technical field of artificial intelligence. Based on a pre-training data set containing a large amount of unlabeled two-photon calcium fluorescence imaging data, a pre-training decoding network and a corresponding pre-training task are self-supervised, and a pre-trained space-time coding network is trained by taking minimization of a pre-training loss function as a target; the method comprises the following steps of: firstly, establishing an encoder and decoder network, establishing fine-tuning data with accurate space-time marked neuron calcium signal positions through simulation, simultaneously performing fine-tuning on the encoder and decoder network by taking minimization of a fine-tuning loss function as a target, and checking the algorithm extraction precision based on various actual imaging data to obtain a calcium imaging signal extraction algorithm network with strong generalization performance. Therefore, a calcium signal extraction model which can be generalized in various imaging conditions and model biological brain regions can be obtained through training without manual labeling, a high-accuracy neural signal extraction result is obtained, the application difficulty that a deep learning model lacks data generalization ability is overcome, and the accuracy of a neural signal extraction result is improved. Calcium imaging neural activity analysis based on self-supervised pre-training and wide in application is achieved.

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.

A method of processing streamed image data is provided. A stream of image data is obtained along with information about the location of one or more region of interest in the image data. The method performs a first spatial processing to change a spatial resolution of at least a portion of the streamed image data in dependence upon the location of the region of interest to generate a first processed stream. Image signal processing is performed on the first processed stream to generate a stream of processed image data. A second spatial processing is then performed on the stream of processed image data to generate a second processed stream of image data.

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.

Disclosed is a system comprising data repository(ies) and server(s) configured to: access, from data repository(ies), a first three-dimensional (3D) model of a virtual environment and a second 3D model of a real-world environment; combine the first 3D model and the second 3D model to generate a combined 3D model of an extended-reality (XR) environment; perform occlusion culling using the combined 3D model, to identify a set of virtual objects that occlude real object(s) in the XR environment from a perspective of a viewpoint; and send, to display apparatus(es), a virtual-reality (VR) image representing the set of virtual objects and information indicative of portion(s) of a field of view that corresponds to the real object(s) that is being occluded by the set of virtual objects from the perspective of the viewpoint.