1395 results about "Image synthesis" patented technology

The invention discloses a product detection method and system based on machine vision, and relates to the technical field of machine vision, and the method comprises the steps: initializing an ambient light sensor, evaluating an initial illumination condition, and dynamically adjusting a light source parameter to verify and lock an optimal setting; selecting a preset wavelength range to configure a spectral imaging system to collect image data according to the characteristics of the building material, performing primary processing and image synthesis to obtain a comprehensive spectral image, and performing feature extraction to obtain a high-quality image data set; preprocessing an image data set on edge equipment, deploying a deep learning model to carry out preliminary defect detection, identifying and marking defect types, and extracting local and frequency domain features; and refining defect types by using a classification algorithm according to the preliminary detection result, integrating a final classification result and image data, and transmitting to a cloud to complete detection. According to the invention, high-precision and high-efficiency product defect detection is realized.

Systems and methods for image compositing are provided. An aspect of the systems and methods includes obtaining a first image and a second image, wherein the first image includes a target location and the second image includes a target element; encoding the second image using an image encoder to obtain an image embedding; generating a descriptive embedding based on the image embedding using an adapter network; and generating a composite image based on the descriptive embedding and the first image using an image generation model, wherein the composite image depicts the target element from the second image at the target location of the first image.

A low-parallax multi-camera imaging system may enable combination of images from multiple camera channels into a panoramic image. In some examples, the imaging system may be designed to include small areas of overlap between adjacent camera channels. Panoramic images may be generated by compositing image data from multiple camera channels by using techniques described herein. In some examples, contribution of each camera channel may be weighted based on factors such as distances relative to an overlap region or content within the overlap region.

Generative image synthesis conditioned on label prompts includes receiving a label prompt comprising a mapping of labels to portions of an image to be synthesized. At least some of the labels in the label prompt correspond to an object to be included in the image to be synthesized. It further includes, based on the label prompt, generating, using a generative model, a synthesized image corresponding to the label prompt.

The invention discloses a metal surface defect image controllable synthesis method based on a pre-training diffusion model, belongs to the technical field of metal surface defect data enhancement, and is a multi-granularity prompt generation scheme aiming at the problems of insufficient defect data and high labeling cost in the prior art. The method comprises the following steps: performing multi-precision labeling on a metal surface defect image, and constructing a multi-granularity training set; the method comprises the following steps: designing a precision coding mask pyramid based on a pre-trained Stable Diffusion model, generating a hierarchical control signal through multi-stage downsampling, fusing category and position information by combining CLIP semantic coding, and performing hierarchical injection to denoise UNet so as to control defect generation; a VAE module and a CLIP module are frozen by adopting a transfer learning strategy, and only a denoising network and a mask coding layer are finely tuned; and a user generates various defect images through the coarse-grained mask and category prompt. The method breaks through the dependence of a traditional generation model on fine labeling, realizes flexible control of defect positions, shapes and categories, and improves the generalization ability of a detection system.

A method and apparatus for generating photorealistic synthetic images by receiving multiple forms and multiple instances of user input corresponding to a user's visual idea, executing an iterative image search to identify pre-existing images semantically aligned with the user's visual idea, and using an image synthesis deep learning model to generate at least one synthetic image based on the multiple forms and instances of user inputs.

The invention discloses a cross-platform GPU support-free real-time digital human rendering system and method, and relates to the technical field of digital human rendering, and the system comprises an offline modeling module which constructs a mouth base space model through principal component analysis based on video data of a target person; the real-time driving module is used for mapping real-time voice features into dynamic coefficients of the mouth base space model; the image synthesis module is used for reconstructing a mouth image according to the dynamic coefficient and fusing the mouth image with a pre-stored reference face image; the cross-platform adaptation layer is configured to realize real-time rendering without GPU dependence through compiling, memory mapping and multi-thread scheduling technologies; and the dynamic resource manager is used for optimizing resource occupation and ensuring stable operation in a multi-platform environment. According to the technical scheme, dependence on high-computing-power hardware and an ideal acoustic environment can be broken through, and a complete theoretical framework and an engineering implementation path are provided for large-scale popularization of a digital human technology in a lightweight terminal.

In a computer-implemented method for supporting vision-based inspection of manufactured parts on a production line, a defect library is created in simulation utilizing real images of manufactured part samples with defects, by digitization and parameterization of enclosed defect geometries of different defect types obtained from the real images. Synthetic defects are generated by randomizing parameters defining the enclosed defect geometries of the defect types in the defect library. A 3D superimposition of the synthetic defects is carried out at randomized positions on a CAD model of a target part to generate a number of defect model objects, each including a digital representation of the target part containing one or more of the synthetic defects. Simulated camera images are then rendered using the generated defect model objects and a digital twin of an inspection environment having one or more cameras, to create a dataset for training a machine learning model.

The invention provides an industrial product defect automatic classification method and system, and the method comprises the steps: collecting original industrial product defect image data, and constructing a labeled image sample set and an unlabeled image sample set; constructing a training image sample set based on the labeled image sample set and the unlabeled image sample set in combination with a plurality of image synthesis strategies; based on the training image sample set, introducing a transfer learning strategy and fusing an attention mechanism, and constructing and optimizing an industrial product defect classification model; performing semi-supervised joint training and online learning based on the training image sample set and the real-time small-batch image sample set; and constructing an industrial product defect identification log based on the real-time image flow sample set, the classification model parameters and the corresponding classification prediction function. On the basis of multi-strategy image enhancement and semi-supervised training, transfer learning and a channel attention mechanism are fused, expansion of industrial product defect image samples and fine defect identification are achieved, and the method is suitable for an intelligent defect detection system in various industrial manufacturing fields.

To implement image synthesis without loss of surface information of SE images and shadow information of BSE images, the present disclosure proposes image processing techniques include applying data of a first quality image (low quality image) to a trained model, estimating a structural feature and a material feature of a second quality image (high quality image) corresponding to the first quality image, calculating at least one shadow datum based on the structural feature and a synthesis parameter and calculating at least one gradation datum based on the material feature and a synthesis parameter, generating a synthesized image from the at least one shadow datum and the at least one gradation datum, and outputting the synthesized image as a prediction result of the second quality image (see FIG. 8).

The invention discloses an implicit image enhancement and optical flow estimation method based on multi-mode collaborative optimization. The method comprises the following steps: firstly, acquiring an RGB image in a normal illumination scene and a depth map of a corresponding view angle; and calculating corresponding three-dimensional point cloud data according to the depth map and the camera internal reference. And synthesizing low-illumination image data by using the RGB images in the normal illumination scene. Decomposing high-frequency features and low-frequency features of the low-illumination image by using a high-low-frequency feature enhancement network to enhance the low-illumination image, and then extracting image features and context features of the low-illumination image; and extracting 2D image features and 3D point cloud features by using a 2D-3D feature fusion network, and performing alignment fusion to obtain image features and context features of a normal RGB image for supervising the feature extraction process of the high and low frequency feature enhancement network. And finally, based on the image features and context features of the low-illumination image, constructing a 4D correlation volume table, and reasoning the optical flow by using GRU.

The invention relates to the technical field of image processing, in particular to a real-time exposure control and image synthesis method for a multi-lens array, which comprises the following steps: an acquisition step: acquiring images shot by a plurality of lenses and corresponding sensor read-out rate information; a processing step comprising: determining exposure parameters through a multi-level hardware level exposure control unit based on the image and the sensor readout rate information; adjusting exposure settings of the plurality of lenses according to the exposure parameters; generating a mask based on the adjusted images shot by the plurality of lenses; performing image synthesis according to the mask and the images shot by the plurality of lenses; the output step comprises the step of outputting the synthesized image, the method can complete exposure parameter adjustment of a plurality of lenses within the millisecond level, and the real-time performance of the system is greatly improved. Meanwhile, an exposure prediction mechanism based on reinforcement learning enables the system to quickly adapt to scene changes, and the problem of response lag of a traditional method is effectively solved.

A digital steganography system comprises a message sender in electronic communication with a message receiver through a social media platform. The message sender uses a compute device configured to conceal a secret digital message in the semantic components of a digitally synthesized image which is uploaded onto the social media platform and published in a social media post. As part of the message encoding process, the compute device for the message sender coverts the digital message into binary code, applies encryption and error-correction algorithms, and then implements image synthetization operations to yield the digitally synthesized image. The message receiver is provided with a compute device configured to identify the social media post, automatically download the synthesized image, and apply an inverse set of the image synthetization operations to yield binary code which is subsequently decoded and decrypted in order to extract the original covert message.

The invention relates to the field of intelligent detection, and provides a machined surface quality detection system and method based on microscopic image.The method comprises the steps that firstly, a multi-focal-plane image sequence of a target product sample is obtained, and a full-clear image of the target product sample is generated through super-depth-of-field image synthesis; then preprocessing and calibrating the full-clear image to obtain a standardized image of the sample, then analyzing and measuring the surface characteristics of the sample based on the standardized image to generate structured data of the product processing surface quality, and finally determining the quality of the product according to a preset quality standard. And processing the structured data to form a product processing surface quality evaluation report. In this way, the comprehensiveness and accuracy of quality detection of the machined surface can be effectively improved.

The invention provides a space-spectrum fusion high-standard farmland utilization mode remote sensing monitoring method and system, and relates to the field of remote sensing monitoring, and the method comprises the steps: carrying out the multi-temporal image synthesis and preprocessing of a remote sensing image set, and obtaining a key phenological period synthesis image set, the NDVI value of the research area in each key phenological period is calculated through the key phenological period synthetic image set; phenological characteristic analysis is performed on different farmland utilization modes based on each NDVI value, farmland utilization priori knowledge is combined to construct a cultivated land utilization map, and spectrum information of the cultivated land utilization map is acquired; performing multi-scale segmentation on the remote sensing image set to obtain field images with spatial information; and assigning spectrum information of the farmland utilization map to the field images with spatial information, and judging the farmland utilization mode of the research area. According to the invention, the spectrum information of the cultivated land utilization map and the field images with spatial information are fused, the farmland utilization mode of each field is visually displayed, and remote sensing monitoring of the farmland utilization mode at the land parcel level is realized.

To provide a construction and management method of a columnar body capable of controlling a horizontal position, an inclination angle, and a height of a columnar body such as a pile with high accuracy, with a simple structure, and to provide a construction and management system of the columnar body.SOLUTION: A construction and management method of a columnar body includes: an imaging step of acquiring a photographed image of the columnar body so that two or more gauge marks capable of acquiring the coordinates are included in the photographed image, by a camera capable of acquiring the coordinates of an imaging position; a 3D data creation step of creating 3D data including the coordinates of the imaging position and the coordinates of the gauge marks, and a 3D model of the columnar body arranged at a predetermined horizontal position, an inclination angle, and a height; an image synthesizing step of generating a composite image by synthesizing the 3D model with the photographed image based on the positions of the gauge marks in the photographed image and the coordinates of the imaging positions and the coordinates of the gauge marks in the 3D data; and an adjustment step of adjusting the horizontal position, inclined angle, and height of the columnar body so that a difference between the position of the columnar body in the composite image and the position of the 3D model becomes equal to or less than a predetermined allowable error.SELECTED DRAWING: Figure 1

The invention discloses a multi-weather degraded image restoration method based on a state space model. The method comprises the following steps: acquiring multi-scene clear images, synthesizing images under rain, snow and fog weather conditions by using an image synthesis technology, and constructing a standard multi-weather data set; preprocessing the data set and then dividing the data set; constructing a multi-weather degradation image restoration model based on the state space model, and performing training optimization on the model; the method comprises the following steps: acquiring an image which is degraded due to weather influence, preprocessing the image, inputting the processed image into a pre-trained multi-weather degraded image restoration model for restoration, and outputting the image as a processed clear image. According to the method, a self-adaptive channel attention mechanism is introduced based on the state space model, the capturing capability of the network on image local features is enhanced, images of various scales can be effectively processed through overlapped block embedding and multi-scale processing strategies, and the image recovery performance is remarkably improved. The method provided by the invention can process image restoration tasks including but not limited to various weather degradation caused by rain, snow and fog, and has a wide application prospect.

The invention provides a display method and electronic equipment, and relates to the field of terminals. The invention provides a display method which is applied to electronic equipment, the electronic equipment is provided with an intelligent table setting application, and when the intelligent table setting application is started, an image synthesizer is indicated to start a display strategy of a filtering layer; when the image synthesizer receives the first image, if it is detected that the first image belongs to the image sent by the intelligent table, the first image is allowed to be displayed; otherwise, the first image is not allowed to be displayed. By adopting the method provided by the invention, when the electronic equipment starts intelligent table setting, the problem of splash screen of the electronic equipment is avoided, and the display quality of a display screen is improved.

The invention relates to the technical field of three-dimensional reconstruction, in particular to a lightweight three-dimensional reconstruction method and system based on a three-dimensional Gaussian model, and the method comprises the steps: constructing the three-dimensional Gaussian of a target scene based on a multi-view original image of the target scene through employing a 3DGS algorithm; identifying the minimum resolution of each Gaussian primitive in the three-dimensional Gaussian, setting a spherical region according to the minimum resolution, taking the intersection result of the spherical region and the Gaussian primitive as a region evaluation score, and trimming the redundancy region geometry in the three-dimensional Gaussian in combination with the opacity; and taking the maximum zoom scale of the Gaussian primitive as a spatial density proxy variable, and dynamically distributing spherical harmonic coefficient orders according to a local spatial density proxy variable so as to meet the rendering requirements of different three-dimensional Gaussian geometric regions. On the premise that the image synthesis quality is not lost, the model scale and the transmission overhead can be remarkably compressed, the resource constraint condition of the edge computing equipment can be effectively adapted, and the higher training speed and the better rendering effect are achieved.

Embodiments for colorizing images, including pixel-format images and vector-format images, include obtaining input data including an outline image and a color hint. The color hint includes a colored portion corresponding to a region of the outline image. Then, embodiments process the input data to obtain control guidance for an image generator using an outline encoder. Embodiments generate a synthesized image based on the control guidance using an image generator. The synthesized image depicts an object having a shape based on the outline image and a color based on the color hint. In some cases, embodiments also transfer the colors from the synthesized image to a base vector image to produce a colorized vector image.

The invention discloses a structure semantic prompt constraint-based text image generation method, which comprises the following steps of: taking a generative adversarial network as a generative model of a main body, and respectively learning and updating the generative model and a discrimination model by utilizing an existing sufficient training data set so as to finish model updating of the model and a training model; and on the basis of the generation model which completes model updating, a new image is generated by inputting the description text, so that the purpose of text image generation is achieved. According to the method, the consistency of the text and the image is remarkably enhanced while the image synthesis quality is improved. In order to reduce the dependence of the model on the training batch size and the training round, improve the training efficiency and optimize the resource demand, the difficult-to-load sample mining is introduced into the image generation task for the first time, and the difficult-to-load sample mining matching perception loss is proposed, so that the model can concentrate on the most challenging sample, and the image generation efficiency is improved. In practical application, good performance can be obtained even in an environment where computing resources are limited.

The invention belongs to the technical field of image processing, and discloses a maskless image synthesis method and system based on a diffusion model, and the method comprises the steps: inputting a background image and a noise image containing a target object into a pre-trained potential diffusion model, and enabling the potential diffusion model to execute a reverse denoising process under the guidance of a condition vector, the target object is synthesized in a self-adaptive mode, and a synthesized image is obtained; wherein the condition vector acquisition mode comprises the following steps: encoding a reference image containing a target object into an object embedding vector, and generating a group of learnable background cue words according to the object embedding vector; splicing with the background prompt word; and performing spatial mapping on the spliced feature representation to obtain a condition vector. The method can overcome the defects that in an existing image synthesis technology, the process is tedious, the efficiency is low, a large amount of manual intervention is needed, the synthesis result lacks the sense of reality and harmony, and especially the foreground object cannot be adaptively adjusted according to the background environment.

A system for synthesizing medical images including synthesizing medical abnormalities has multiple diffusion model based denoising stages. At a first denoising stage, a machine-learned network denoises a first noise input to obtain an abnormality spatial mask detailing positional and structural characteristics of the synthesized medical abnormality. At a second denoising stage, a machine-learned network denoises a second noise input based on the abnormality spatial mask and a pre-abnormality image to obtain a synthesized medical image that corresponds to the pre-abnormality image with the synthesized medical abnormality inserted consistent with the abnormality spatial mask.

The invention relates to the technical field of image focus detection, in particular to a fiber endoscope image focus detection method and system, and the method comprises the following steps: providing a data quality guide interface; capturing narrow-band light images of the endoscope away from a first preset position and a second preset position of the intestinal wall in the axial movement process, calculating brightness gain values of a blue light channel and a green light channel, calculating an asymmetric scattering correction coefficient, and updating indication of color information calibration integrity; tracking the pixel moving speed of the interferent in the view in the propulsion operation process, calculating the relative depth of the interferent, and updating the indication of the validity of the depth data of the interference layer; identifying the image area which is not shielded by the interferent, and updating the indication of the information coverage degree of the background area; triggering an image synthesis operation; performing color compensation on the image information of the image area which is stored in the background canvas and is not shielded by the interferent; reconstructing a clear image; and performing focus detection on the clear image. The method improves the accuracy of focus detection.

The invention discloses a CBCT (Cone Beam Computed Tomography) high-quality CT (Computed Tomography) image synthesis method based on structure prior guidance, which comprises the following construction steps of patient data collection and arrangement, space guidance deformable attention convolution, structure prior guidance type conditional diffusion image synthesis and reasoning process and quality evaluation. The method specifically comprises the steps of technical design of a space guiding module, technical design of a deformable sampling kernel generator, technical design of an attention weight modulator, contour extraction operation, diffusion modeling operation, conditional denoising network design and target training. The method provided by the invention has the advantages of structural perception, high spatial selectivity, high modulability and the like; and the reduction precision of the model on the key anatomical region is enhanced by taking a contour map as a structure priori condition. The whole diffusion generation network realizes unification of local detail reservation and global structure stability while maintaining noise robustness, and gradually generates a high-quality pseudo CT image which is close to a real CT modal and has excellent structure consistency.

The invention relates to the technical field of tunnel lighting, in particular to a tunnel portal lighting control method and system, and the method comprises the steps: S1, obtaining a first image in a short exposure time mode; obtaining a second image in a long exposure mode; s2, synthesizing the second image and the first image into a high-dynamic-range image, and dividing the high-dynamic-range image into a low-brightness area, a transition area and a high-brightness area; s3, generating a brightness transition curve of the transition area; s4, determining a brightness adjusting curve of the transition area according to the maximum brightness of the tunnel portal and the light and shade adaptive pupil area change rate; s5, determining the target brightness of each part of the transition area according to the brightness transition curve and the brightness adjustment curve; and S6, adjusting the brightness of the illumination lamp in the transition area to enable the brightness of each part of the transition area to reach the target brightness. The target brightness is determined according to the brightness transition curve in combination with the brightness adjustment curve, the influence of the brightness adaptive pupil area change rate on the sight line is fully considered, and the change of the brightness of the tunnel portal is more adaptive to the function of human eyes.

The invention discloses a 5G signal tower three-dimensional rendering and high-altitude panoramic image synthesis method based on GSII, and the method comprises the steps: firstly carrying out the frame extraction processing of data shot when an unmanned plane flies around a signal tower, and obtaining an original image set; obtaining a signal tower sparse point cloud and a camera pose through a motion structure recovery SfM algorithm, and carrying out 3DGS initialization operation; the method is characterized in that in the process of iterative optimization of a three-dimensional Gaussian point cloud, a GSII network is constructed, structural similarity comparison is carried out on an original image of the same visual angle and a deblurred rendering image, a loss function is sensed by using a high receptive field and back propagation is carried out, meanwhile, parameter updating is carried out on a 3D Gaussian ball attribute and FFC residual error repair network, and the quality of a new visual angle rendering image is improved; and finally, designing a central pixel time sequence splicing module, and converting any number of single-view-angle rendering images into a panoramic image. According to the method, the phenomena of artifacts and blurring which are easy to occur under a high-altitude view angle are solved, and the signal tower high-altitude panorama is finally obtained by synthesizing a plurality of rendering images of a single view angle.

Systems and methods in accordance with embodiments of the invention are disclosed that use super-resolution (SR) processes to use information from a plurality of low resolution (LR) images captured by an array camera to produce a synthesized higher resolution image. One embodiment includes obtaining input images using the plurality of imagers, using a microprocessor to determine an initial estimate of at least a portion of a high resolution image using a plurality of pixels from the input images, and using a microprocessor to determine a high resolution image that when mapped through the forward imaging transformation matches the input images to within at least one predetermined criterion using the initial estimate of at least a portion of the high resolution image. In addition, each forward imaging transformation corresponds to the manner in which each imager in the imaging array generate the input images, and the high resolution image synthesized by the microprocessor has a resolution that is greater than any of the input images.

The invention relates to an intelligent operation and maintenance method for power transmission network equipment based on digital twin dynamic coupling, and belongs to the technical field of intelligent detection of power systems. The method comprises the following steps: dynamically splitting a power grid panoramic frame into an equipment physical entity layer and a static background environment layer, and generating a panoramic display picture through cloud and edge parallel rendering and a terminal HMD picture synthesis technology; establishing real-time bidirectional mapping between the physical entity and the virtual twinborn body of the power grid equipment, and synchronizing sensor data to the digital twinborn model; adopting an improved permutation entropy algorithm to extract equipment operation characteristic quantity, and dynamically distributing rendering pipeline resources in combination with a Bayesian network diagnosis model and an analytic hierarchy process evaluation system; and constructing an equipment state quality index, and quantitatively evaluating the health degree of the current equipment and predicting the future degradation trend by dynamically comparing the characteristic parameters with an expected value and combining health factor correction and a time sequence nonlinear prediction model. According to the invention, full-life-cycle efficient management and control of the operation state of the power grid equipment can be realized.

The invention discloses an infrared ship scene generation method and system based on collaborative optimization of a physical prior model and a data-driven algorithm, and a storage medium, and belongs to the technical field of infrared image generation and artificial intelligence fusion. Comprising the following steps: preprocessing an acquired real infrared ship image and an original visible light ship image; different types of ship databases are established through semantic segmentation, and a visible light ship foreground image is obtained; synthesizing a new visible light ship image, and converting the new visible light ship image into a pseudo-infrared image; segmenting the new visible light ship images, classifying the new visible light ship images according to materials, covering the segmented image masks into the corresponding preprocessed real infrared ship images, extracting real infrared gray values of ship parts, establishing a linear regression model, and outputting gray values of ships of different materials; and optimizing the pseudo infrared image, correcting the gray scale of the ship area by using the gray value, and finally generating an infrared ship scene. According to the method, the contradiction between physical consistency and generation efficiency is solved, and the problems of high cost and data shortage of infrared ship image acquisition are solved. Enhancement of infrared ship data can be used for sea ship detection and identification scenes.