758 results about "Image conversion" patented technology

The invention discloses a deep learning-based multimodal image fusion method for soft tissue photoacoustic / ultrasound imaging. Steps: an ultrasound-photoacoustic imaging device acquires photoacoustic and ultrasound images of human soft tissue and performs size normalization processing; an input spatial transformation module converts the images to the YCbCr space; an input pre-convolution module modifies the number of data channels; an input multi-scale feature extraction module extracts salient features from the source images; an input filter prediction module derives multi-scale filters; and an input filter fusion and adaptive enhancement module combines the input source images to obtain the final fused result. The invention has superior fusion performance compared to several traditional fusion methods and deep learning-based fusion methods, and more importantly, it exhibits excellent real-time performance. Furthermore, various modes of photoacoustic / ultrasound fusion extension experiments have verified the effectiveness of the method proposed in the invention.

The invention discloses an unmanned aerial vehicle infrared thermal imaging method and device for cold leakage detection of a low-temperature storage tank, and relates to the field of automatic inspection and detection of storage tanks, and the method comprises the steps: determining the inspection range and detection distance of an unmanned aerial vehicle based on geometric parameters, a cold leakage frequency region and environment parameters of a site; calculating the distance between the shooting points and the vertical coverage height of single-circle flight, further dividing flight elevation layering, and calculating the number of the shooting points of each circle of flight; establishing a three-dimensional model; collecting a visible light image and an infrared thermal imaging image of each shooting point; the method comprises the following steps: performing multi-dimensional correction and temperature image conversion on an acquired infrared thermal imaging image, performing image registration on a temperature image with a temperature scale and a visible light image, performing cold leakage area identification to obtain a cold leakage area, performing quantitative calculation to obtain three-dimensional positioning of a cold leakage position and a cold leakage area, and performing cold leakage area identification on the cold leakage area. And generating a visual detection result, a detection report and a maintenance suggestion. According to the invention, automatic detection and accurate analysis of the cold leakage area of the low-temperature storage tank can be realized.

A processor-implemented method including converting an input image based on first sub-images of first color channels into a multispectral image based on second sub-images of second color channels, generating an illumination map representing an illumination configuration of the input image, based on the input image, generating a confidence score map of the illumination map, based on the multispectral image, and determining illuminant information of the input image by fusing the illumination map with the confidence score map, a second number of channels of the second color channels being greater than a first number of channels of the first color channels.

The invention discloses a progressive multi-modal medical image fusion method based on multiple scales. Relates to the technical field of multi-modal medical image fusion, in particular to a progressive multi-modal medical image fusion method based on multiple scales. Through the integrated design of multi-scale extraction, hierarchical coding, layer-by-layer fusion and adaptive modeling, efficient and accurate fusion of multi-modal medical images is realized, and reliable technical support is provided for clinical application. The method comprises the following steps: acquiring images of a mode A and a mode B, and preprocessing the images; performing multi-modal parallel coding on the preprocessed image, wherein the multi-modal parallel coding comprises four coding stages; the input of each coding stage passes through a self-adaptive visual RWKV module, an efficient cross-modal module and a progressive fusion module in sequence to obtain the output of multi-modal parallel coding; and carrying out image conversion to obtain an RGB fusion image.

The invention discloses an unsupervised image conversion imaging method based on the Schrodinger bridge theory, and the method comprises the following steps: 1, constructing an image domain-domain conversion frame based on the Schrodinger bridge theory, simulating a smooth evolution path from a source domain image to a target domain image through a time condition generator, and forming antagonism training in combination with a discriminator; 2, integrating a saliency content guide constraint, a global feature consistency constraint and a contrast learning constraint in the conversion framework so as to enhance semantic content retention and detail generation capability; 3, establishing a composite loss function, and carrying out weighted combination on Schrodinger bridge path loss, adversarial loss and each auxiliary constraint loss for guiding model optimization; and 4, training a time condition generator and a discriminator through an optimization algorithm, and carrying out stable and high-fidelity target domain conversion on the source domain image by utilizing the generator after training is completed. According to the method, the stability of model training and the quality and fidelity of the generated image are remarkably improved.

A technique for processing delivery aborts by a UAV delivery service includes: acquiring a delivery zone (DZ) image of a delivery destination including a DZ for a package being delivered to the delivery destination by a UAV; determining to abort a delivery mission for the package based upon the DZ image; converting the DZ image to a vector embedding; performing a similarity search on a vector database using the vector embedding, the vector database storing reference images of other delivery destinations indexed to reference vector embeddings and outcome attributes describing delivery outcomes associated with the reference images, and wherein the similarity search identifies a subset of the reference images deemed to have a threshold similarity to the DZ image; and prompting a vision language model with the DZ image and the subset of the reference images to provide an abort explanation or to determine a delivery disposition for the DZ.

An apparatus and method for adaptive slide imaging using a selected scanning profile is disclosed. The apparatus includes a scanner configured to capture a macro image of a slide and including a stage configured to hold the slide, an optical sensor configured to convert an image into one or more electrical signals, and an optical system configured to form the image of the slide on the optical sensor. The apparatus includes at least a processor and a memory containing instructions configuring the at least a processor to receive the macro image of the slide from the scanner, extract metadata from the macro image of the slide, determine a classification category of the slide, retrieve a scanning profile as a function of the classification category of the slide, and image, using the optical system and optical sensor of the scanner, the slide as a function of the scanning profile.

The embodiment of the invention discloses an image quality evaluation method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a to-be-processed image, and converting the to-be-processed image into a target color space to obtain a to-be-used image; wherein the to-be-processed image is an image acquired based on fusion of a plurality of sensors; determining a brightness mean value corresponding to the to-be-used image, and converting the to-be-used image into a frequency domain space to obtain frequency domain data corresponding to the to-be-used image; determining image texture attributes according to the frequency domain data; determining a target evaluation result of the to-be-processed image according to the image texture attribute and the brightness mean value; wherein the target evaluation result is used for representing the credibility of the image collected by the image sensor in the current environment. Through the technical scheme of the embodiment of the invention, image quality evaluation for non-single indexes can be realized, and the image quality evaluation efficiency and accuracy are improved.

The model training apparatus trains an image conversion model to generate, from an input image representing a scene in a first environment, an output image representing the scene in a second environment. The model training apparatus inputs a training image to the image conversion model to obtain a first feature map and an output image, input the output image to the image conversion model to obtain a second feature map, computes a patch-wise loss using the features corresponding to a positive example patch and a negative example patch extracted from the training image and a positive example patch extracted from the output image, and trains the image conversion model based on the patch-wise loss, which is extracted intensively from the region representing an object of a specific type.

The invention discloses a character recognition method and device for a handwritten image, terminal equipment and a storage medium, and the method comprises the steps: obtaining the handwritten image, and converting the handwritten image into a single-channel gray-scale map; wherein the single-channel grey-scale map is an image with a white background and black characters; performing reverse binarization processing on the single-channel grey-scale map to generate a reverse binarization image with black-matrix white characters; all contours in the reverse binarization image are recognized, and the contour with the largest area serves as a character core area; calculating an amplification coefficient according to the width and height of the reverse binarization image; performing boundary expansion on the character core region according to the amplification coefficient, and performing cutting based on the expanded boundary to obtain a cut image; and performing character recognition according to the clipped image. According to the invention, the character recognition accuracy can be improved.

A structure inspection system includes an image conversion device located in a far edge of the structure inspection system that is proximate to a physical structure, and a near edge processor located in a near edge of the structure inspection system that is remote from the physical structure. The image conversion device receives an image of the physical structure, and renders a real time 3D model of the physical structure based on the first images. The near edge processor is configured to provide a baseline 3D model of the physical structure to the image conversion device. The image conversion device further compares the first real time 3D model with the baseline 3D model, and provides an indication when the first real time 3D model deviates from the baseline 3D model.

The invention belongs to the technical field of image data processing, and particularly relates to a wood surface defect detection method based on multi-view coding and a feature memory bank. The method comprises the steps that an original wood data set is acquired, an original wood image in the original wood data set is preprocessed, and the original wood image is converted into a Lab image; a wood surface defect detection model is constructed to process the Lab image, and a normal feature core set is obtained; after an anomaly detection framework is constructed and a feature core set is obtained, inputting a to-be-detected wood surface image into a wood surface defect detection model to extract local features, and calculating a nearest neighbor distance between each local feature and features in the normal feature core set; and generating an abnormal heat map of the to-be-detected image based on the nearest neighbor distance so as to realize defect detection and pixel-level positioning. The problems that the wood surface defect types are diversified, abnormal samples are difficult to cover, and the marking cost is high are solved.

The invention discloses an image compression method and system based on edge gradient self-adaption, and the method comprises the steps: converting an input image into a YCbCr color space, calculating an edge gradient value of a brightness component, and employing iteration processing including a feedback mechanism: dividing an edge region and a non-edge region based on a current self-adaption edge gradient threshold value, the blocks are divided according to different sizes; dynamically updating an edge gradient threshold according to a block statistics result, and adaptively optimizing region division through multiple iterations; and finally, based on the optimized blocking result, processing the edge region and the non-edge region by adopting different quantization strategies, generating a quantization matrix, and then carrying out entropy coding to output a compressed code stream. According to the method, adaptive adjustment of compression parameters is realized through an iterative optimization mechanism, image edge and texture details are effectively reserved while a high compression rate is ensured, and the subjective visual quality of a compressed image is remarkably improved.

The application provides a method for camera color calibration applied to an electronic device, the electronic device comprising a camera array (including a first camera) and a high-resolution multispectral camera, the method comprising: synchronously collecting original images of a first scene by the first camera and the high-resolution multispectral camera, wherein the first camera collects a three-channel image (a first image), and the high-resolution multispectral camera synchronously collects an M-channel image (a second image), M being a positive integer greater than or equal to 3; converting the second image from the M-channel image to a three-channel image by a first conversion function; taking the converted three-channel image as a reference to perform color calibration on the first image to obtain first color parameters for color adjustment of the first camera. In the method, the color of the array camera is aligned with the color perceived by the high-resolution multispectral camera, and the authenticity and consistency of the color of the image captured by the camera in the camera array can be improved.

The invention relates to the technical field of image processing, and discloses an HVI color space-based shadow removal method and system, a terminal and a storage medium, and the method comprises the steps: obtaining an RGB input image, converting the RGB input image to an HVI color space, and carrying out the decoupling of the RGB input image to obtain HV branch information and I branch information; designing a double-branch network structure, extracting color features in HV branch information by using an HV branch in the double-branch network structure, and extracting brightness features in I branch information by using an I branch in the double-branch network structure; fusing the color features and the brightness features by using an attention mechanism to obtain fusion information; and performing brightness adjustment and color correction on the fusion information to obtain an HVI component, mapping the HVI component back to an HVI color space, converting the HVI component into an RGB image, and outputting a shadow-free image. According to the method, the HVI color space and the double-branch architecture are introduced, so that the robustness and the accuracy of image shadow removal can be remarkably improved.

The invention provides an image noise reduction method and system, computer equipment and a storage medium, and belongs to the field of image processing, and the method comprises the steps: firstly converting an original strip mine coal rock image into a gray level image, and carrying out the image processing of the gray level image for the problem of edge blurring caused by mixed noise in the strip mine coal rock image; the method comprises the following steps: firstly, extracting a noise model, analyzing the noise type and distribution characteristics of the noise model to implement preliminary noise reduction, filtering out small-scale noise of an image subjected to preliminary noise reduction through Gaussian-Laplacian joint transformation to obtain an enhanced image, then calculating a gradient magnitude image of the enhanced image, dynamically generating a parameter matrix in combination with a normalized gradient value, and constructing an adaptive generalized overall variation noise reduction model; and taking the gradient amplitude image and the self-adaptive parameter matrix as input, updating the optimal estimation image through iterative optimization, and obtaining an optimal noise reduction image when the iteration energy variation is smaller than a threshold value. According to the method, while mixed noise is removed, coal rock texture details are remarkably reserved, and high-quality data support is provided for mining decisions of strip mines.

The invention discloses a multi-label interference identification method and device based on dual-domain asymmetric feature fusion, and the method comprises the steps: enabling a received interference signal to generate two types of time-frequency images through short-time Fourier transform and continuous wavelet transform, converting the two types of time-frequency images into RGB images, and inputting the RGB images into a dual-branch AsymResNet18FPN network to extract multi-scale features; cross-domain feature adaptive fusion is realized through channel splicing and attention weight generated by MLP; and finally, outputting an interference type combination by the multi-label classifier. According to the method, the complementarity of double-domain features is fully utilized, the direction perception and multi-scale modeling capability is enhanced, the problem of feature submerging under the low interference-to-signal ratio is effectively relieved, synchronous recognition of multiple composite interferences is supported, and the recognition precision and robustness in a complex electromagnetic environment are remarkably improved.

The invention discloses a method and a system for automatically testing a virtual cabin of a whole vehicle based on digital twinning, and relates to the technical field of vehicle testing, a three-dimensional working condition space containing an eye point coordinate set and a polarization angle set is constructed by simulating the height and head micro motion of a driver, and a head-up display image in the three-dimensional working condition space is acquired through a camera; constructing a correction coordinate system and a gray brightness mapping table by combining a standard calibration board, and converting the image into a physical brightness matrix; marking elements in the physical brightness matrix through a brightness threshold value, extracting two parallel straight lines of a main track and a ghosting track through Hough transform and ODR sub-pixel refitting, and calculating a visual angle difference and a ghosting brightness ratio; and generating a test result table containing the three-dimensional working condition index based on a double-threshold joint judgment rule. The system comprises an image acquisition module, a feature extraction module, a data calculation module and a test judgment module, realizes automatic and accurate quantitative automatic test based on the system, and improves the virtual cabin test efficiency and reliability.

The application discloses a liver US-CT medical image conversion method based on a CycleGAN network. The method comprises the following steps: inputting a liver US image x as a training sample into a generator G; the generator G generates a liver pseudo-CT image y' according to the liver US image x; inputting the liver pseudo-CT image y' as a training sample into a generator F; the generator F generates a loopback liver US image x'' according to the liver pseudo-CT image y'; training the generator G; training a discriminator Dy; inputting a liver CT image y as a training sample into the generator F; the generator G generates a liver pseudo-US image x' according to the liver CT image y; inputting the generated liver pseudo-US image x' as a training sample into the generator G; the generator G generates a loopback liver CT image y'' according to the liver pseudo-US image x'; training the generator F; and the discriminator Dx.

The invention provides an image conversion model establishment method and device, an infrared image labeling method and device and a medium, and relates to the field of automatic driving, and the method comprises the steps: obtaining a visible light image and an original infrared image which are aligned in time and space; inputting the visible light image into a material attribute estimation branch to output a material semantic graph, and inputting the original infrared image into an image noise adding branch to output a noise-added infrared image; the two and the visible light image are jointly input into a diffusion model to output predicted noise; the predicted noise, the noise-added infrared image and the original infrared image are input into a multi-task loss module to output weighted summary loss, and diffusion model parameters are iteratively updated according to the weighted summary loss so as to construct an image conversion model; and inputting the visible light image into the image conversion model to output a target infrared image, and mapping and labeling to obtain infrared labeling information. According to the method, the physical consistency of the infrared generated image and the visible light image is remarkably improved, the task of the infrared generated image is friendly, and a large-scale high-quality infrared data set can be quickly constructed.

A processor acquires two radiation images with contrast based on a first characteristic corresponding to a first imaging condition. It derives a body thickness distribution of a subject from at least one of the images and removes scattered ray components from both images using the first characteristic. A soft part image and a bone part image representing respective regions of the subject are derived from the two images. These images, based on the first characteristic, are converted into soft and bone part images with contrast based on a second characteristic corresponding to a second imaging condition, using the body thickness distribution and both characteristics. Finally, the processor adds the converted soft and bone part images to derive a processed radiation image with contrast based on the second characteristic.

The invention discloses a dam anti-seepage body integrity detection method based on an artificial power supply. Comprising the following steps: injecting a broadband excitation current signal into a dam anti-seepage body through a multi-frequency program control constant current source; a modular distributed electrode array is adopted to collect and obtain voltage data of the surface of the dam anti-seepage body in real time; high signal-to-noise ratio voltage data is obtained by adopting filtering processing and a digital phase-locked amplification technology; carrying out three-dimensional inversion calculation by adopting an improved electrical impedance tomography algorithm, and reconstructing an internal conductivity distribution image of the dam anti-seepage body; analyzing, identifying and positioning defects through a deep learning model; and constructing an electro-seepage coupling inversion model, converting the internal conductivity distribution image based on the dam anti-seepage body into permeability coefficient distribution, and generating an integrity evaluation report of the dam anti-seepage body. Compared with a traditional detection method, the method is high in detection precision, wide in application range and high in detection efficiency, and a brand new technical means is provided for evaluating the health state of the dam seepage-proofing body.

An image highlight removal method and system, a storage medium and a program product relate to the field of general image data processing or production, the method comprising: receiving an input image, converting the input image to a brightness and chrominance separated color space, identifying a highlight pixel region, and generating a highlight mask; expanding outwards by taking the boundary of the highlight mask as a reference, and constructing an annular analysis area; calculating a pixel gradient amplitude and a pixel gradient direction, and generating a linear structure feature data set; calculating a pixel color mean value and a pixel color standard deviation, and generating an abnormal region feature data set; performing defect distribution reasoning based on direction information in the linear structure feature data set and the abnormal region feature data set to generate a structure prior guidance graph; and carrying out source patch constraint according to the structure prior guidance graph, and processing the highlight pixel region to obtain an output image. According to the invention, the highlight removal process can be optimized, and the integrity of equipment defect features in the image is ensured.

This invention provides a parking space detection method, system, and electronic device based on Inverse Perspective Mapping (IPM). The method includes acquiring an initial surround view image of an autonomous vehicle and converting it into an initial IPM image using inverse perspective mapping (IPM). The initial IPM image is then optimized to generate an optimized IPM feature map. A pre-trained parking space detection model is used to process the optimized IPM feature map to determine the coordinate regions of the four corner points of the parking space, the coordinate region of the center point of the parking space, and the horizontal and vertical coordinate offsets of each pixel within the foreground region of the parking space relative to the four corner points. A neighborhood region of the center point of the parking space is determined from the foreground region. Furthermore, based on the horizontal and vertical coordinate offsets of each pixel within the neighborhood region of the center point of the parking space relative to the four corner points, the true coordinate positions of the four corner points are determined from their respective coordinate regions. This invention addresses the problem of low accuracy in existing parking space detection schemes.

The invention provides an image generation method and device, a storage medium and a processor. Relates to the technical field of image processing. The method comprises the following steps: acquiring an initial image and text description; based on an image resolution bucket dividing strategy, converting the initial image into a resolution image of a corresponding bucket to obtain a to-be-processed image; converting the to-be-processed image into an image Token sequence by adopting a preset image conversion model; converting the text description into a semantic vector sequence by adopting a preset text conversion model; obtaining a feature sequence based on the image Token sequence and the semantic vector sequence; performing relative position coding on the image Token in the feature sequence to obtain an input feature sequence; and inputting the input feature sequence into a preset image generation model to obtain a new image. And the consistency of the pixel relationship under different resolutions is ensured, so that the detail consistency of the high-resolution image is ensured, and the accuracy of image generation is improved.

The invention relates to a calibration device and a calibration method of a low-altitude target early warning detection system, and belongs to the technical field of equipment calibration, the calibration device of the low-altitude target early warning detection system comprises a visible light sighting telescope and an electronic eyepiece which are fixed on radar equipment, the electronic eyepiece is arranged at the exit pupil position of the visible light sighting telescope, and the electronic eyepiece is connected with the visible light sighting telescope. The image acquisition module is used for acquiring analog images acquired by the visible light sighting telescope based on the eyepiece and converting the analog images into digital images; the processor is in communication connection with the electronic eyepiece and is used for acquiring the azimuth angle of the first servo motor; the pitching angle of the visible light sighting telescope is controlled to the target angle; acquiring an azimuth angle of a second servo motor; and based on the azimuth angle of the first servo motor, the azimuth angle of the second servo motor and the target angle, carrying out space consistency calibration on the radar equipment and the photoelectric equipment. According to the invention, the calibration complexity of the low-altitude target early warning detection system is effectively reduced, and the calibration efficiency is improved.

This specification provides one or more embodiments of an attack defense and detection method, system, device, storage medium, and program product. The attack defense method includes: in response to an authentication operation, acquiring a face image to be authenticated and specified verification information; converting the face image into an image data stream of a preset format; embedding the specified verification information into the end of the image data stream to obtain an image data stream to be authenticated; and sending the image data stream to be authenticated to a server for authentication. The attack detection method includes: receiving an image data stream to be authenticated uploaded by any client to be authenticated; determining, based on the end identifier of the image data stream of the preset format, whether the specified verification information can be extracted from the image data stream to be authenticated; if yes, the authentication task is free from face image replacement attacks; if no, the authentication task is at risk of face image replacement attacks.

This application relates to a method, apparatus, device, and medium for few-sample SAR target recognition. The method includes: dividing SAR samples into support and query sets based on a meta-learning framework; converting the original SAR image into a semantic map and an attribute scattering center topology map; extracting three types of features and calculating class prototypes through three parallel feature branches; obtaining the confidence score of each branch using the Softmax function; assigning teacher and student branches according to the confidence score; minimizing KL divergence to achieve dynamic knowledge transfer; weighted fusion of predicted log odds followed by Softmax output; and backpropagation to optimize the network. This method, through multi-prototype fusion and interactive distillation, mitigates the problems of speckle noise and high intra-class variance in SAR images, maintaining high robustness and generalization ability even in extreme data-scarce scenarios.

This invention discloses an intelligent identification and positioning method for ocean internal waves based on multi-source remote sensing imagery, belonging to the field of marine remote sensing technology. The key technical solutions include the following steps: determining the geographical location and time range of the target sea area; collecting multi-temporal high-resolution remote sensing image data from synthetic aperture radar (SAR) and optical satellite imagery; labeling the internal wave features of the preprocessed multi-temporal high-resolution remote sensing image data to generate ground truth images; using a trained deep convolutional neural network model to perform pixel-level analysis on the input remote sensing images to extract ocean internal wave features and generate binarized images of the internal wave features; and converting the extracted binarized images of the internal wave features into corresponding latitude and longitude locations based on the geographical location information of the original remote sensing images. This achieves high-precision geographic positioning of internal waves, is suitable for marine research and high-precision environmental monitoring, and has broad application prospects.

This invention discloses a method, system, terminal device, and storage medium for three-dimensional reconstruction of two-dimensional images. The method includes: acquiring an image to be processed; inputting the image to be processed into a pre-created recognition model to obtain a feature map; generating a three-dimensional image based on the feature map; mapping the annotation information of the image to be processed onto the three-dimensional image to obtain a final image; and associating the image to be processed and its annotation information with the final image and its annotation information to obtain a database. This invention reduces information loss during the conversion of two-dimensional planar images to three-dimensional images, improves the accuracy of the conversion, provides more precise model recognition results, and offers users a more intuitive browsing experience.