118 results about "Image separation" patented technology

The invention relates to a light-weight low-light image enhancement method based on illumination iterative adjustment, and the method comprises the steps: carrying out the HVI color space transformation of a to-be-processed image, obtaining an HVI image, separating an illumination intensity component, obtaining an illumination intensity feature map, and carrying out the convolution processing of the HVI image, and obtaining a shallow feature map; feature fusion and brightness adjustment are carried out on the illumination intensity feature map and the shallow layer feature map to obtain a depth feature map, up-sampling is carried out step by step through two layers of decoders to recover the size, channel adjustment is carried out on each layer of decoder by adopting convolution of a specified size, and up-sampling is realized through bilinear interpolation; a Mama-based structure refining module is introduced into the tail end of each layer of decoder to improve the structure restoration capability, the number of channels of HVI features output by the last layer of decoder is adjusted to a specified value through a convolution of a specified size, residual connection is carried out on the HVI features and an initial HVI image, an enhanced HVI image is obtained, and a restored image is obtained through inverse HVI transformation. Adaptive enhancement is carried out under various low light conditions, multi-directional spatial context aggregation is realized, and texture representation is improved.

The invention discloses a reinforcing steel bar surface binding point identification and positioning method based on point cloud and pixel mapping. The method comprises the following steps: obtaining original three-dimensional point cloud data to carry out outlier noise point elimination; performing plane fitting on the denoised point clouds, identifying template surface point clouds and removing the template surface point clouds; mapping the spatial physical coordinate attribute of the steel bar surface point cloud into a three-channel coding image; separating the three-channel coded image into three single-channel images: a length image, a width image and a depth image; processing the depth image to extract an initial reinforcing steel bar straight line set; screening out a parallel steel bar straight line set from the initial steel bar straight line set according to a set reference direction and a parallel angle tolerance threshold value; and calculating straight line intersection points in the parallel steel bar straight line set to obtain candidate binding point pixel coordinates, determining representative binding point pixel positions through clustering, querying coordinate information of the three single-channel images, and outputting three-dimensional space coordinates of the steel bar binding points. According to the invention, robust and accurate steel bar binding point identification and positioning are realized.

The invention discloses a method and a device for constructing a multilevel power field knowledge base, relates to the field of artificial intelligence, and mainly aims to solve the problem that the field knowledge base constructed based on an existing method is poor in effect of improving the application reliability of a large language model. Comprising the steps of performing text image separation on original data to obtain a domain knowledge original text and a domain knowledge original image; performing adaptive semantic text block segmentation on the original text to construct a semantic text block knowledge base; performing dual-channel collaborative knowledge extraction on the text blocks, fusing extraction results, and constructing a knowledge graph knowledge base; performing adaptive text description on the domain knowledge original image to generate a constructed image and a text description knowledge base; vector conversion and mapping alignment are carried out on the domain knowledge text blocks, the knowledge graph triple, the domain knowledge original image and a combination of corresponding text descriptions to construct a semantic vector knowledge base; and constructing a multi-level domain knowledge base based on the plurality of knowledge bases.

The invention relates to a wallpaper surface defect detection method, which comprises the following steps of: acquiring a wallpaper surface image by using a line-scan digital camera, correcting the image, establishing an average background model, and performing reflection compensation based on the average background model; s200, the image after reflection compensation is separated into R, G and B color channels, a gray level co-occurrence matrix (GLCM) of each channel is calculated to extract contrast features, the dynamic weight of each sub-block is calculated, a weighted RGB image is obtained, then the RGB image is converted into an LAB color space, an L channel is extracted, and # imgabs0 # is obtained; fusing the weighted RGB image and the L channel; carrying out three-layer decomposition by using a Daubechies8 wavelet basis, and carrying out reconstruction to obtain a background image; and carrying out dual-channel Ostu segmentation on the defect candidate image, carrying out decision fusion after segmenting the two channels, and carrying out noise removal and non-defect region filtering to obtain a defect region.

This invention discloses a laser image signal-to-noise ratio (SNR) enhancement method based on single-camera dual-channel imaging, relating to the fields of optical microscopy, digital image processing, and machine learning. The method includes: acquiring the original image from a single camera; separating the original image into a green channel image and a red channel image; constructing a denoising model based on the linear relationship between the green and red channel images, using the minimization of background noise in the green channel image as the objective loss function of the denoising model, and solving for an optimized parameter set; processing the green and red channel images according to the optimized parameter set to obtain a high SNR laser image after removing fluorescence background noise. This invention can remove fluorescence background noise from the original image acquired by a single camera without increasing hardware costs, effectively eliminating spectral crosstalk in the laser channel and significantly improving the laser image signal-to-noise ratio.

The application provides a low-light image enhancement method and system based on sequence multi-modal guidance, belonging to the technical field of image processing, wherein the method comprises: collecting a low-light RGB image and corresponding event data stream; encoding the event data stream into a three-dimensional event tensor; separating the low-light RGB image into low-frequency features and high-frequency features; inputting the high-frequency features and the three-dimensional event tensor into a pre-trained event feature extraction network to generate event features, and inputting the low-frequency features and the low-light RGB image into a pre-trained image feature extraction network to generate image features; and performing sequence enhancement on the image features and the event features to obtain an enhanced image. The application solves technical problems such as multi-modal feature fusion imbalance, texture fidelity decline caused by event data noise, and high model calculation complexity, and realizes high-quality enhancement of low-light images.

A method of processing a mammogram image, including receiving a digital mammography image of a breast of a patient, and separating the digital mammography image into three channels. The method includes removing noise and uneven illumination to form a first set of three uniform background images, and separating a luminance channel and a chrominance channel from the first set with a transfer function to form a second set of three uniform background images. The method includes applying principal component analysis to the second set; and assembling a single grayscale image and applying a first subspace projection. The method includes removing at least a portion of a pectoral muscle from the grayscale image, and normalizing the muscle removed image using a second order Gaussian Laplacian and an oriented diffusion filter to form a processed image. The method includes identifying cancerous and / or noncancerous regions in the processed image.

The application relates to the technical field of surface defect intelligent detection, and discloses a surface defect detection method and device based on multi-view synthetic imaging. The method comprises the following steps: acquiring a multi-view synthetic image without dead angles of a detected target after quality evaluation; performing highlight suppression and shadow enhancement on the multi-view synthetic image to obtain an enhanced multi-view synthetic image; separating the enhanced multi-view synthetic image into corresponding multiple single-view images, respectively performing defect detection on each single-view image to obtain corresponding single-view defect detection results, and generating a final overall detection result based on the single-view defect detection results. Through multi-view synthetic imaging, image quality control, high-reflective surface enhancement processing and multi-view joint defect detection, the application can effectively solve the problems of light interference and defect missing detection in high-reflective surface detection, and can realize high-precision surface defect detection with no dead angle coverage in a single detection.

This invention discloses an integrated broadband communication and stereo remote sensing method based on multi-satellite cooperation. For a ground target area, a central satellite and several neighboring auxiliary satellites are selected from a low-Earth orbit (LEO) satellite constellation. Each satellite collaboratively transmits radio frames towards the joint beam coverage area. Ground users receive the shared pilot signal embedded in the radio frames to obtain channel state information and recover data. Simultaneously, the central satellite receives and stores the ground echo signal corresponding to the shared signal and performs channel estimation. Combining the Doppler center differences between echoes from different cooperating satellites, multi-view range image separation is achieved. Furthermore, a stereo synthetic aperture radar (SAR) imaging method is used to calculate the three-dimensional geographic coordinates of reflectors in the target area. This method achieves integrated collaboration of communication and remote sensing functions within a LEO satellite constellation, contributing to improved constellation resource utilization efficiency and three-dimensional observation capabilities of the target area.

The application relates to the technical field of image processing, in particular to an intelligent detection method for aluminum alloy plate bending processing, which comprises the following steps: collecting a bending image sequence; separating each frame of image into a base image containing material texture information and a reflection image containing highlight information, to obtain a base image sequence; performing optical flow analysis and texture analysis on the base image sequence to identify processing defects generated in the bending process of the aluminum alloy plate; and generating a visual result image labeled with the positions of the processing defects and the positions of the highlights based on the processing defects and the reflection image, so as to realize detection of the bending defects of the aluminum alloy plate. According to the technical scheme, the light interference can be separated from the root cause, and finally, stable and reliable detection of the bending defects of the aluminum alloy plate is realized.

The invention relates to the technical field of PCB production detection, and discloses a method and a system for PCB multilayer board alignment deviation identification. According to the method, the multi-layer target theoretical projection image is generated through physical modeling, frequency domain analysis is carried out in combination with the X-ray detection image and the multi-layer target theoretical projection image, and quantitative evaluation of the reliability of different areas of the image is achieved through the obtained pixel-level credibility weight matrix. Further, through a method of deeply combining credibility evaluation and image separation, noise in the image is effectively suppressed, structural information of a high-credibility region is reserved, and accurate and reliable interlayer alignment deviation data can be provided for alignment deviation recognition through the obtained single-layer target separation image and target design parameters; the problem that the alignment recognition result is inaccurate due to aliasing distortion when an X-ray detection image is directly analyzed by a traditional method is solved, and the recognition accuracy of the alignment deviation condition of the PCB is remarkably improved.

The invention discloses a shooting signature processing method, computer equipment, a product and a storage medium, and the method comprises the steps: carrying out the preprocessing of a shot image in response to a triggering operation of the shot image containing the signature of a user, and obtaining a preprocessed shot image; performing shadow removal on the pre-processed shot image to obtain a shot image after shadow removal; performing edge detection on the shot image after shadow removal to obtain a candidate contour corresponding to the user signature and contour information of the candidate contour; and according to the candidate contour and the contour information of the candidate contour, cutting the shot image after shadow removal to obtain a signature image signed by the user. According to the technical scheme, the image definition and the user signature image separation precision are remarkably improved by processing the shot image containing the user signature.

In order to solve the technical problems that the existing droplet observation device cannot monitor the multi-nozzle printing process and the overall observation precision is low, the present application provides a multi-nozzle single droplet real-time observation device, an observation method and a 3D printing system.The observation device of the present application can make the observation camera capture the current working printing nozzle and automatically focus on it in real time through the cooperation of the first steering engine and the displacement platform, and complete the real-time monitoring of the printing state of the printing nozzle; through the high-frequency multiple flashing of the LED flash, the observation camera can capture the images of the same droplet at multiple times in one shot, at the same time, the high-frequency deflection of the piezoelectric deflection mirror and the telecentric lens are matched with each other, the overlapping images of the single droplet at adjacent moments are separated, so that a single photo can have enough droplet images to show the forming process, the overall observation precision is improved, the quality monitoring and fault diagnosis problems during additive manufacturing are improved, and the waste rate of electronic 3D printing is reduced.

The invention provides an image processing method and device, equipment and a medium, and relates to the technical field of artificial intelligence, in particular to the technical field of computer vision, deep learning and the like. The method comprises the steps of identifying a main body object corresponding to a target area on an original image in response to a detected input target operation for the original image; performing expansion operation on the main body object to obtain an adjacent area of the main body object; inputting a region identification graph for identifying the positions of the main body object and the adjacent region in the original image and the original image into an image matting model to obtain a foreground mask; separating the original image into a foreground image and a background image based on the foreground mask; performing blurring operation on the background image to obtain a blurred background image; combining the fuzzy background image and the foreground image into a preview image; and outputting the preview image.

The invention relates to the technical field of image processing, in particular to an intelligent detection method for aluminum alloy plate bending processing, and the method comprises the steps: collecting a bending image sequence; separating each frame of image into a substrate image containing material texture information and a reflection image containing highlight information to obtain a substrate image sequence; carrying out optical flow analysis and texture analysis on the substrate image sequence so as to identify processing defects generated in the bending process of the aluminum alloy plate; and based on the processing defect and the reflection image, generating a visual result graph marked with a processing defect position and a highlight position at the same time so as to realize detection of the bending defect of the aluminum alloy plate. By means of the technical scheme, illumination interference can be separated from the source, and finally stable and reliable detection of the bending defects of the aluminum alloy plate is achieved.

The application discloses a multimodal medical image heterogeneous signal synchronous fusion method and device and system, comprising the following steps: S1, acquiring a DSA video stream and an IVUS video stream; S2, performing cross-modal medical image registration based on a space-time Transformer according to the DSA video stream and the IVUS video stream, and realizing medical image heterogeneous signal synchronous fusion. The technical scheme of the application solves the problems of image separation, inaccurate positioning, system delay and the like in the prior art, and improves the precision and safety of interventional surgery.

Systems and methods are provided for implementing a Siamese neural network using improved “sub” neural networks and loss function. For example, the system can detect a granular change in images using a Siamese Neural Network with Convolutional Autoencoders as the twin sub networks (e.g., Siamese AutoEncoder or “SAE”). In some examples, the loss function may be an adaptive loss function to the SAE network rather than a contrastive loss function, which can help enable smooth control of granularity of change detection across the images. In some examples, an image separation distance value may be calculated to determine the value of change between the image pairs. The image separation distance value may be determined using an Euclidean distance associated with a latent space of an encoder portion of the autoencoder of the neural networks.

The invention relates to a structure-semantic double-graph collaborative SAR image quality evaluation method, which comprises the following steps of separating an SAR image, and constructing a double-channel quality measurement set for independent modeling; indexes are obtained, correlation calculation is carried out, an index map is constructed, and each node corresponds to one index; on the basis of the index map, the dependency relationship between indexes is calculated from the structure dimension and the semantic dimension through self-supervised learning, the dependency relationship of the two dimensions is integrated, and an image level score based on the integration result is output; and constructing a measurement index shared with system analysis and a measurement index special for information retrieval, and carrying out quality measurement evaluation: after extracting a structure sub-graph, integrating an IR special index which is not contained in SAR prior into a global graph. The method is superior to a reference model in the aspects of recognition accuracy, recall rate, precision and efficiency, and a solution with high universality and good interpretability is provided for cross-modal image quality evaluation.

The present application relates to an image acquisition device and a method. The device comprises: a first projector, configured to project a projection pattern of a first wavelength to an object to be acquired, such that said object presents a first wavelength-based projection image; a second projector, configured to project light of a second wavelength to said object, such that said object presents a second wavelength-based mark point image; and a light splitting component, provided between said object and a preset camera module, and configured to separate the light of the first wavelength and the second wavelength, so as to separate the projection image from the mark point image. The preset camera module comprises: a first camera module and a second camera module, the first camera module being configured to acquire the projection image, and the second camera module being configured to acquire the mark point image. The present disclosure solves the technical problem in the prior art that when projection images and mark point images are acquired at the same time, the mark point images are contaminated by the projection images.

The imaging system includes a multi-distance camera having an imager and a lens. The lens includes a plurality of regions, and each region is physically different from each other region. The controller is in communication with the multi-range camera. The controller includes a processor and a memory. The memory stores instructions configured to cause the processor to process an image received from the imager by separating the image into a plurality of different region images, processing each different region image using a processing program corresponding to a region of the region image being processed, the region images are recombined into a single processed image, and the single processed image is provided to at least one vision system.

The invention discloses a single-camera partitioned optical microscopic imaging device which comprises a bottom plate, a support is mounted at the top of the rear side of the bottom plate, and a mounting plate is mounted on the front side of the support; the sample table is mounted on one side of the top of the bottom plate and is used for clamping a sample; the transmission illumination assembly is mounted on the other side of the top of the bottom plate and is used for providing uniform illumination for the sample; and the microscopic imaging assembly is mounted at the bottom of the front side of the mounting plate. According to the invention, through the arrangement of the image separation assembly, the intermediate image of the sample can be separated into microscopic images of a plurality of channels, and the microscopic images can be imaged on the target surface of the camera in real time after being transmitted through different paths, so that a single camera can carry out multi-channel imaging without considering whether a plurality of cameras are aligned or not; according to the optical microscopic imaging device, the problem of difference of time resolutions of a plurality of cameras does not exist, the optical path of the optical microscopic imaging device is relatively simple in the propagation process, some very rapid dynamic events can be synchronously captured, and the use effect is good.

Methods and systems for setting up care areas (CAs) for inspection of a specimen are provided. One system includes a computer subsystem configured for acquiring an area of interest image for a specimen from images of the specimen generated by an imaging subsystem. The computer subsystem segments the area of interest image based on intensity of pixels in the image thereby separating the image into pixels corresponding to one or more specimen structures in the image having an intensity different than other pixels in the image. In addition, the computer subsystem detects edges of the specimen structure(s) in the image based on the pixels corresponding to the specimen structure(s) and determines characteristic(s) of one or more CAs in the images of the specimen based on the detected edges. The computer subsystem further stores information for the CA(s) for use in inspection of the specimen.

The invention provides a method and device for image steganography information detection, a twin neural network system and a method and device for training the neural network system. The detection method comprises the following steps: separating a to-be-identified image into at least three color channels, and respectively constructing image vectors based on each color channel based on the at least three color channels; combining the image vectors of the at least three color channels in pairs to form image vector pairs of two color channels in the plurality of color channels; respectively inputting the image vector of each color channel in the image vector pairs of the plurality of color channels into a trained twin neural network to generate the similarity between the image vector pairs of each color channel; and determining whether steganography information exists in the to-be-identified image based on the similarity between the generated image vector pairs of each color channel.

The application discloses an apple recognition and positioning method suitable for a complex open environment of an orchard, and comprises the following steps: high-light area detection and high-light compensation are performed on an image of an apple to be recognized; shadow area adaptive screening is performed on B and R channels separated from the image, and then standard logarithmic transformation is performed; after the image is enhanced by high shadow, A components in a LAB color space and improved light adaptive I2 components in an OHTA color space are extracted, information fusion is performed based on color features of the apple in the two space components, and an RGB three-channel image containing only all visible apple areas is obtained; binaryzation, morphological operation and median filtering are performed, and an apple area binary image is obtained; the apple area binary image is used to separate the adhered apples; and the image after the adhered apples are separated is used for apple recognition and positioning.

The display device includes: a display; a processor; a user separation layer; and left and right image separation layers. The processor acquires user position information including position information of a first user according to output of the sensor acquiring the position information of the real space. Generating a right eye image and a left eye image of the first user based on the position information of the first user; generating a second user image of a second user; and controlling the display to display a display image including the right-eye image and the left-eye image of the first user and the second user image. The user separation layer is fixed to a surface of the display and separates the display image into a viewpoint of a first user and a viewpoint of a second user. The left and right image separation layer is fixed to a surface of the display and separates a display image into a right-eye image and a left-eye image.

The invention relates to the field of crop phenotype monitoring, and discloses a field crop aging process intelligent monitoring method and system, and the method comprises the steps: respectively obtaining canopy RGB images at a booting stage and a specified time point after heading of a crop through an image collection system; performing color calibration on the acquired canopy RGB image, separating a canopy leaf area from the calibrated RGB image, and only retaining leaves in the image; lab color parameters are extracted from the segmented leaf areas, and SPAD values at the booting stage and SPAD values at specified time points after heading are predicted according to the extracted Lab color parameters; and according to the predicted SPAD value, calculating the aging degree to represent the aging process of the canopy leaf. According to the method, the aging process of the canopy leaves in the field can be identified losslessly and efficiently, and the requirements of modern precision agriculture can be met.

The application provides a multimodal fidelity hallucination detection method and system based on closed-loop multi-dimensional verification, belonging to the technical field of large visual language models, comprising: S1: decomposing the user instruction input into the LLM and the answer of the LLM into structured units; S2: through the collaborative mode of the LLM and the visual tool, the consistency score of the assertion is calculated from three dimensions of text-instruction consistency, text-image consistency and logical consistency, all verification results are bound to a unique evidence ID, and the assertion without evidence support is determined as unknown; S3: performing type-specific calibration on the consistency score, and then generating a unified hallucination score H through weighted fusion; S4: providing traceable evidence support for the detection result, and triggering the abstention mechanism when the evidence is insufficient, so as to ensure the credibility and practical value of the detection result. The method realizes accurate detection of three types of hallucinations, i.e., instruction inconsistency, text-image separation and logical inconsistency.

This application provides an image processing method, electronic device, and program product, relating to the field of astrophotography technology. The image processing method includes: performing background target removal processing on an acquired image containing a high-speed moving target using a depth model to obtain a background target image and a moving target image; overlaying multiple moving target images to obtain a first overlay image; overlaying multiple background target images to obtain a second overlay image; and fusing the first and second overlay images to obtain a target image. This application can separate an original acquired image containing a high-speed moving target into a background target image and a moving target image, perform overlay processing on each separately, and then fuse the two overlay images to obtain the final target image, effectively improving the quality of the target image and meeting the user's needs for capturing and imaging high-speed moving targets.

An image processing device according to one embodiment of the present invention comprises: an acquisition unit that acquires a captured image captured by an imaging device; a separation unit that separates, from the captured image, a reflection layer composed of a reflection component reflected by a transparent body; a detection unit that uses the reflection layer to detect a rectangular region and an imaging source region that includes reflection of an imaging source; and an assessment unit that, on the basis of the positional relationship between the rectangular region and the imaging source region, assesses whether the rectangular region is a transparent body region.