280 results about "Image noise" patented technology

The invention relates to a circuit board defect identification method and system based on multi-dimensional image data, and belongs to the technical field of data identification processing, and the method comprises the following steps: obtaining synchronous image data of a circuit board to be detected in a plurality of imaging modes; performing space-spectrum joint registration on each modal image to generate a multi-dimensional image cube with a unified coordinate system and pixel alignment; inputting the multi-dimensional image cube into a pre-trained multi-branch heterogeneous fusion neural network; generating a pixel-level defect probability graph by utilizing a defect sensing context decoder, and performing geometric constraint optimization on the probability graph by combining prior information of a circuit board design layout; outputting defect types, positions and confidence coefficients, and establishing an interpretable defect fingerprint database according to the multi-dimensional response characteristics of the defects; the method has the beneficial effects that false defect signals generated by image noise and circuit board surface texture interference can be effectively inhibited, the omission ratio and the false detection ratio are greatly reduced, and pixel-level accurate defect positioning is realized.

The invention provides an ultrasonic image focus automatic identification method based on deep learning, and the method comprises the steps: obtaining an ultrasonic probe pressure and an ultrasonic image of a body surface contact region of a patient, carrying out the tissue hierarchy analysis of the ultrasonic image, and recognizing the tissue density and the three-dimensional space coordinate information of the depth position of a target organ; driving sound wave signals of the ultrasonic probe through the target probe frequency, monitoring the contact pressure change of the probe and the body surface in real time, and judging whether the current contact state meets the pressure requirement of deep imaging or not according to the contact pressure change; identifying a deep focus boundary of the enhanced ultrasonic image, and performing multi-scale feature extraction and texture analysis on the ultrasonic image to obtain contour coordinates, tissue gray level distribution and internal uniformity of a focus area; meanwhile, the image noise level and the tissue contrast difference are evaluated to obtain the definition, the contrast ratio and the boundary sharpening degree of the current image.

Systems and methods for improving the image quality of images of epithelial tissue structures are disclosed. The systems include training a first cycle-GAN model and a second cycle-GAN model simultaneously, where the first cycle-GAN model is trained to remove noise from an image and the second cycle-GAN model is trained to learn the structure of the image. Additional systems and methods include deploying the trained cycle-GAN model to identify an unknown image segment and / or generate a protocol for following the identified skin care treatment recommendation for an identified image segment.

The invention relates to the field of visual inspection, and discloses an intelligent automatic visual inspection system and method for an automobile brake disc, and the method comprises the steps: carrying out the time-sharing sampling imaging of a friction working surface of the brake disc through a multi-station imaging device when the brake disc enters a detection station along with a conveying mechanism; analyzing and separating the main texture direction, periodic distribution and continuity characteristics in the image group around regular turning textures formed in the brake disc machining process; performing stratified analysis on abnormal signs by combining distribution characteristics and gray disturbance amplitude differences of residual information under different spatial scales; non-structural fluctuation caused by local reflection or imaging noise is eliminated by analyzing the difference between the texture extension trend and the gray scale stability of adjacent areas; and comprehensively judging the distribution position and the influence range of the suspicious area on the working surface in combination with the reference position of the brake disc structure. The method has the advantage of improving the surface quality detection accuracy of the brake disc.

The invention relates to the technical field of medical images, in particular to a multi-modal medical image registration and fusion analysis method, which comprises the following steps of: eliminating image noise and artifacts based on a modal adaptive filtering strategy; constructing a pyramid type feature extraction network to realize multi-scale feature extraction, calculating feature matching degrees among different modal images, and dynamically adjusting matching weights by combining feature differences among modals; a focus area attention mask is constructed, targeted enhancement of registration image features is realized, and a hierarchical fusion strategy is adopted to evaluate the quality of a fused image; and constructing a multi-task deep learning model to complete focus automatic detection, segmentation and benign and malignant preliminary judgment on the fused image. According to the multi-modal medical image registration and fusion analysis method, a three-layer feature pyramid is constructed, a multi-feature fusion matching cost function is introduced, and cross-modal feature matching is optimized through an adaptive weight iteration nearest point ICP algorithm, so that the information richness, marginal definition and focus discrimination of a fused image reach the standard.

The present disclosure relates to systems, methods, and non-transitory computer readable media for utilizing a diffusion neural network for mask aware image and typography editing. For example, in one or more embodiments the disclosed systems utilize a text-image encoder to generate a base image embedding from a base digital image. Moreover, the disclosed systems generate a mask-segmented image by combining a shape mask with the base digital image. In one or more implementations, the disclosed systems utilize noising steps of a diffusion noising model to generate a mask-segmented image noise map from the mask-segmented image. Furthermore, the disclosed systems utilize a diffusion neural network to create a stylized image corresponding to the shape mask from the base image embedding and the mask-segmented image noise map.

This application relates to the field of medical image processing technology, and provides a method, apparatus, device, and storage medium for reconstructing ECT images. This allows for more thorough capture of subtle changes in the concentration distribution of radioactive tracers at high temporal resolution, helping to reduce image noise levels and improve image quality. This application obtains a first ECT image sequence; the first ECT image sequence is obtained by scanning with an ECT scanning device; the first ECT image sequence is input into a pre-constructed intermediate frame prediction network to obtain an estimated second ECT image sequence; using the estimated second ECT image sequence as a priori image, a target second ECT image sequence is reconstructed; the estimated second ECT image sequence and the target second ECT image sequence belong to the second ECT image sequence; the temporal resolution of the second ECT image sequence is higher than that of the first ECT image sequence.

The invention relates to the technical field of scanning electron microscope image processing, and discloses a scanning electron microscope image restoration method based on a wavelet frequency domain adjustment diffusion model, and the method comprises the steps: constructing a two-stage degradation pipeline comprising random light path disturbance and fixed circuit collection limitation, and generating a specific domain training data set; extracting multi-scale high-frequency energy characteristic modulation noise distribution by using a frequency domain prior encoder, and training a wavelet frequency domain adjustment diffusion model; and inputting a target low-quality degraded image into the pre-training model, initializing a potential noisy state, then executing reverse denoising iteration, calculating wavelet domain consistency gradient correction noise prediction, and updating the potential noisy state until a restored image is generated. According to the method, data distribution mismatch is relieved through physical degradation modeling, high-frequency detail perception is enhanced by means of a frequency domain adjustment mechanism, the structural fidelity is improved through wavelet domain gradient guidance, and balance between scanning electron microscope image noise suppression and detail recovery is achieved.

The invention provides a wake detection method based on multi-layer graph interactive guidance and a multi-head voting type Transform. According to the method, depth features are extracted through FcaNet in combination with Fourier transform, frequency domain key details are explicitly modeled, and a frequency domain-time domain joint information feature map is output; a graph structure is constructed, and through multi-layer graph interaction, the discrimination capability of a complex wake form is enhanced by using an MHVT module; an MSFM module is introduced to realize space and channel dual fusion, and then a wake and an anchor frame are positioned through a regression network, so that the interference of SAR image noise and wake form variability is effectively overcome, and efficient and accurate ship wake detection is realized.

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.

The invention discloses an elastic reverse time migration imaging method based on deviatoric stress characterization, and relates to the technical field of exploration geophysics. Deviatoric stress is introduced into a conventional elastic wave equation, and a speed-deviatoric stress elastic wave equation describing stress propagation characteristics is obtained through derivation; based on Hooke's law, P wave contribution in total partial strain is removed through partial strain decomposition, decoupling of P-S wave stress components is achieved, and correctness of amplitude, phase and physical unit of a decoupling wave field is guaranteed; and then a joint imaging condition with the average stress as a seismic source wave field and the multi-stress component as a receiving wave field is constructed, PP waves are subjected to normalization superposition through the average stress and the deviatoric stress, and PS waves are subjected to shear stress imaging. According to the method, the sensitivity to heterogeneity and local stress variation of underground media can be remarkably improved, P-S wave crosstalk and imaging noise are effectively eliminated, the imaging precision and resolution of a complex geologic structure are greatly improved, and a reliable technical means is provided for high-precision seismic exploration of complex oil and gas reservoirs.

The present application belongs to the technical field of engineering structure health monitoring, and discloses a photovoltaic flexible support cable force non-contact monitoring method and system based on video vibration. First, a cable video sequence is acquired, and a dynamic sensitive area is adaptively divided along the cable image. The time sequence average value of the pixel intensity in each area is calculated, the two-dimensional image information is converted into a one-dimensional vibration time history signal, and the light drift and image noise are suppressed through difference and wavelet denoising. After filtering the signal of each dynamic sensitive area, the initial frequency set is formed by preliminarily extracting the multi-order dominant frequency estimate value. The frequency observations from all areas are fused and analyzed, and the real frequency cluster is identified. The weighted centroid frequency of the cluster is used as the multi-order vibration frequency. Finally, the multi-order frequency is substituted into the model, and the real-time cable force value is output. The whole process of the method does not need to contact the measured cable, and provides an innovative and reliable solution for long-term online safety monitoring of the flexible support cable force in complex environments.

The invention discloses an image binarization method based on a dynamic window. The method comprises the steps of 1, target image noise suppression and gray level conversion; 2, carrying out compression processing on the dynamic range of the bright part of the image through a logarithmic function; 3, calculating a global threshold value of the grayscale image by utilizing a maximum between-class variance method; 4, dividing the grayscale image into image blocks according to the image gradient, and calculating a local threshold value for each block by using a Sauvola algorithm; 5, according to the global threshold value and the local threshold value of each image block, the threshold value of each image block is obtained through calculation according to the weight proportion; and 6, segmenting each image block by using a corresponding threshold value to obtain a final segmented image. According to the invention, the size of a local window is adaptively changed when any pixel point is binarized; and the image binarization capability under the condition of non-uniform illumination in a complex illumination environment is remarkably improved.

The application discloses a UAV autonomous obstacle avoidance method and system based on vision-laser radar fusion, belongs to the technical field of UAV autonomous flight, and comprises the following steps: S1, collecting vision image data and laser point cloud data of the environment around a UAV through a vision sensor and a laser radar synchronously; adopting a hardware trigger and a timestamp double synchronization mechanism to control the time synchronization error of the vision image data and the laser point cloud data within + / -1 mu s; respectively carrying out denoising, enhancement and distortion correction processing on the vision image data; and adopting a bilateral filtering algorithm to remove image noise. The hardware trigger and the timestamp double synchronization mechanism are adopted to control the time synchronization error of the vision and the laser radar data within + / -1 mu s, the problems of fusion feature space dislocation and obstacle calibration distortion caused by a traditional synchronization mode are solved, a high-precision data foundation is laid for multi-sensor feature fusion, and the real-time sensing demand of high-speed flight of the UAV is met.

The application relates to a multi-modal feature interaction method and device based on information filtering. The method comprises the following steps: extracting image local features and text features; constructing a text semantic condition; generating adaptive weight filtering image noise based on the condition, and obtaining filtered features; updating the text features by using the filtered features; and guiding the image features again by using the updated text features. The filtering and interaction processes are jointly optimized by using a loss function containing a regularization term. The method can effectively suppress noise in multi-modal data, and improve feature consistency and task robustness through bidirectional closed-loop interaction.

The present application relates to the technical field of ultrasound image processing, and particularly relates to an image processing method, device and system based on an interventional array ultrasound transducer. The method comprises: processing a two-dimensional ultrasound image through noise evaluation to obtain a noise evaluation image; the noise evaluation image can be used to accurately partition image noise; a first filtering algorithm and a second filtering algorithm can be used to preliminarily process the partitioned noise to obtain a preliminarily denoised image; the preliminarily denoised image is then processed accurately using a U-Net model to obtain an accurately denoised image; the accurately denoised image is regionally divided to obtain an edge image and a smooth image; corresponding algorithms are executed on the edge image and the smooth image to obtain an edge denoised image and a smooth denoised image; finally, the edge denoised image and the smooth denoised image are weighted and fused to obtain a denoised ultrasound image; and the accuracy of ultrasound image denoising is improved.

The present application belongs to the technical field of electric power construction, and particularly relates to a power transmission and distribution construction monitoring method and system based on air-ground communication. The present application obtains an initial image sequence of a construction site through image acquisition equipment deployed in cooperation with the air and the ground, first performs frequency domain and spatial domain joint adaptive denoising on the image, and then identifies the construction process node in real time; the feature anchor points of the intra-frame static ground features are extracted, rigid alignment is performed through feature anchor point matching, and a standardized image sequence that is spatially aligned throughout the construction period is generated; a monitoring target area that matches the process node is demarcated, the gray scale gradient and edge texture feature vectors in the area are extracted, and are transmitted to the management and control end through an air-ground communication link, and are compared with the standardized construction feature benchmark corresponding to the process, to complete accurate judgment of safe operation. The present application solves the pain points of construction scene image noise and spatial misplacement, and greatly improves the monitoring recognition accuracy and management and control reliability.

An information processing device (110) comprises: an optical calculation unit (112) that calculates each of the optical path of light from an imaging field of view in an imaging device that performs imaging using an optical element and the optical path of light from outside the imaging field of view that is outside the imaging field of view, calculates stray light produced by the optical element and a point spread function (PSF) of the optical element, and generates a degraded image in which the stray light and the PSF are applied to an ideal image that is handled as correct answer data in teacher data; a noise image generation unit (113) that generates a noise image including noise predicted to occur in an imaging element of the imaging device during imaging; an image calculation unit (114) that superimposes the noise image on the degraded image to generate a learning image; and a teacher data generation unit (115) that generates teacher data in which the learning image is employed as training data and an image corresponding to the imaging field of view of the imaging device within the ideal image is employed as correct answer data.

The application provides an image noise estimation method and system based on image edge characteristics. The image noise estimation method comprises: obtaining an original image, dividing the original image into a plurality of sub-images to be estimated; judging each sub-image to be estimated based on a gradient image of the sub-image to be estimated to determine whether the sub-image to be estimated is a valid sub-image to be estimated; performing convolution operation or correlation operation on the valid sub-image to be estimated by using a second-order difference filter kernel to obtain a second-order difference image; and estimating noise in the original image based on the second-order difference image to obtain a noise estimation value of the original image. The method obtains a sub-image to be estimated containing more noise and uses the valid sub-image to be estimated to estimate the noise of the original image, thereby accurately estimating the distribution of the noise in the original image. The system can realize the above method.

This invention belongs to the field of pest monitoring technology, specifically a regional pest early warning and control system and method based on visual recognition. The system includes a multispectral pest trapping and imaging module, an image noise adaptive filtering module, a pest dual-branch feature analysis module, a pest population density quantification module, a pest spread trend prediction module, a precise control instruction adaptation module, and a pest control evaluation and optimization module. This invention collects multispectral pest images and environmental parameters through trapping, denoises and fuses them, and then uses a dual-branch network to simultaneously extract morphological and spectral features to accurately identify pests. It also quantifies pest population density and, combined with environmental parameters, predicts spread trends, classifies into three levels of early warning, and generates precise control instructions. This achieves accurate pest identification, early warning, and scientific control, improving the efficiency and level of regional pest early warning and control, and is suitable for large-scale agricultural production.

The present application belongs to the field of medical image segmentation, and particularly relates to a retinal blood vessel image segmentation method. In view of the problems of low segmentation accuracy, insufficient segmentation ability of small blood vessels at the edge of eyeball, fracture at the blood vessel branch, and excessive interference of image noise in the existing retinal blood vessel image segmentation, the method comprises the steps of retinal image preprocessing and establishment of a retinal blood vessel segmentation model, wherein the preprocessing comprises converting a color retinal image into a gray image by giving different weights to the RGB three channels of the color retinal image; using a normalized and contrast-limited adaptive histogram equalization method to improve the image; using a local adaptive gamma change algorithm to adjust the retinal image; using translation, rotation, and noise increase to expand the data set; and the model establishment comprises feature extraction, feature fusion, and retinal blood vessel image segmentation.

This invention presents an enhanced micro-operation target segmentation method utilizing improved Otsu and edge operators, addressing the limitations of conventional techniques in microscopic visual environments. The proposed approach effectively mitigates accuracy constraints caused by image noise and shadow effects from uneven illumination. The methodology comprises three key phases: Applying an optimized bilateral filtering algorithm to perform noise reduction on grayscale images, producing denoised images; Segmenting micro-operation targets using the refined Otsu algorithm on these denoised images to obtain initial segmentation results; Directly applying the final segmentation result when meeting predefined criteria, or conducting iterative segmentation through the enhanced edge operator and Otsu algorithm if necessary. This innovative method demonstrates exceptional performance in micro-operation target detection applications

This invention relates to a convolution-based method for noise suppression in positron emission tomography (PET) flow field images, comprising the following steps: S1, obtaining consistent data through CFD flow field simulation and GATE detection system simulation, and reconstructing a low-resolution initial image of the PET flow field containing noise using the OSEM algorithm; S2, constructing a noise suppressor model, inputting the low-resolution initial image into the model, and outputting a noise-suppressed flow field feature image using a deep convolutional network and multi-scale feature fusion technology; S3, constructing a multi-loss fusion evaluation system, using mean squared error loss, minimum absolute value deviation loss, and perceptual loss to jointly constrain model training, and iteratively updating the weights using the Adam optimizer. This invention not only improves the enhancement model's ability to distinguish noise information, thereby enhancing the model's ability to remove noise information from PET images, but also significantly improves evaluation metrics such as PSNR and SSIM.

The application discloses an online identification method for pyrolysis and ablation interface evolution of carbonized ablation material based on X-ray image fusion enhancement. In the ground thermal test of the carbonized ablation material, a plurality of groups of images of the carbonized ablation material at different moments are obtained through X-ray imaging, an image fusion enhancement algorithm is adopted to improve the image quality, the local features of the pyrolysis and ablation interface are enhanced, the image noise is reduced through local sliding window averaging, and the pyrolysis and ablation interface is identified in real time based on a gradient algorithm. The application can realize real-time automatic measurement of the ablation amount, the interface between the pyrolysis layer and the carbonization layer, and the interface between the pyrolysis layer and the original layer in the ground thermal test of the carbonized ablation material, overcome the problems that the traditional measurement method is difficult to identify the interface between the pyrolysis layer and has low accuracy, and improve the measurement technical level of the ground thermal test of the thermal protection material, thereby providing support for the design of the ablation thermal protection system.

The application discloses a kind of based on adaptive noise reduction tool damage gray image intelligent classification method, mainly including a network consisting of adaptive noise reduction module and a tool damage image classification module;Noise reduction module and classification module are simultaneously accepted end-to-end joint training, share and optimize network parameters, while increasing a balance parameter in loss function, the parameter is according to the classification result of feedback of back propagation algorithm adaptive optimization noise reduction level, finally reach the optimal classification performance.The application can automatically identify whether the image contains noise and batch solve the classification problem of image noise for tool damage gray image, improve the prediction ability of model for difficult samples through adaptive noise reduction mode, can effectively remove image noise, while reducing the amplification effect of corresponding loss function to anti-noise, finally improve the intelligent classification ability of tool damage gray image from image processing quantity, image quality and prediction efficiency.

Provided in the present disclosure are a training method for an image generation model, and a related apparatus and a medium. The method comprises: acquiring a plurality of image-text sample pairs, wherein each image-text sample pair comprises a background template image, a noise reference image, image description information of the noise reference image, and a sample object image comprising a sample object; on the basis of the noise reference image, the background template image and a template mask image, determining sample splicing image features; on the basis of contour features of a reference object in the background template image, the image description information, and the sample object image, determining denoising network control information; on the basis of the sample splicing image features and the denoising network control information, performing noise prediction by means of an image generation model, so as to obtain a noise prediction result corresponding to the noise reference image; and on the basis of comparison results between the noise reference images in the plurality of image-text sample pairs and the noise prediction results respectively corresponding thereto, training the image generation model. The present disclosure can improve the accuracy of generating a target image.

The invention discloses an image processing method and device, equipment, a storage medium and a computer program product, and belongs to the technical field of image processing. The method comprises the following steps: mapping a target pixel point in a target image to an original image to obtain a coordinate of a target mapping point; determining a plurality of reference pixel points from the original image based on the coordinates of the target mapping point; determining the edge intensity of the target pixel point based on the gray values of the plurality of reference pixel points; based on the edge intensity of the target pixel point and the distances between the multiple reference pixel points and the target mapping point, weights corresponding to the multiple reference pixel points are determined; and determining the pixel value of the target pixel point based on the pixel values of the plurality of reference pixel points and the weights corresponding to the plurality of reference pixel points. As the edge intensity can well distinguish the image edge and noise, the weight of each reference pixel point is adjusted by adding the edge intensity of the target pixel point, the image noise can be reduced, the image edge can be enhanced, and the image contrast can be improved.

The invention discloses a load reference measurement system and method based on two-dimensional grating diffraction. The method comprises the following steps: designing a layout geometric scheme of a collimation light path and key parameters of an imaging camera according to the measuring range and precision requirements of a load reference measurement system; structural parameters of a two-dimensional circular hole grating arranged at the front end of the imaging camera are designed according to the LED light source parameters and the key parameters of the imaging camera; at the front end of the imaging camera, enabling the collimation light path to form multi-stage diffraction light spots through a two-dimensional circular hole grating, obtaining the equivalent center-of-mass coordinates of the double light paths by adopting a multi-light-spot center-of-mass averaging method, and calculating the propagation direction of each light beam in an imaging coordinate system based on the equivalent center-of-mass coordinates; and in combination with a collimation light path layout geometric scheme, a load reference under an imaging camera coordinate system is calculated through inversion. According to the invention, the influence of LED light direction fluctuation, imaging noise and single light spot positioning error on the measurement result can be effectively inhibited, and the on-orbit high-precision measurement of the load reference is realized.

The invention discloses a camera time sequence control method, electronic equipment and a driver monitoring system, and relates to the technical field of artificial intelligence. The method is suitable for a camera of an image sensor, and the exposure starting time of photosensitive pixels in all rows or columns of the camera is sequentially staggered, and according to the single exposure duration, the effective imaging time and the periodic time sequence interval of the image sensor, the exposure starting time of the photosensitive pixels in all rows or columns is calculated. Under the condition that the total energy of infrared radiation received by each exposure pixel is the same, determining the light-emitting duration of the light-supplementing light source; according to the effective imaging time, the frame blanking time and the light-emitting duration, determining a light-emitting starting moment under the condition that the starting position of a light-emitting signal of the current frame image is located at the last time slice before the end of the previous frame image; and generating a control signal corresponding to the light supplementing light source according to the light emitting starting time and the light emitting duration. According to the invention, the problems of high cost and large image noise in related technologies can be solved, the cost can be effectively reduced, and the image effect can be optimized.

The application provides an iterative-based image noise estimation method and system. The method comprises: obtaining a gradient amplitude image of an original image and an initial noise estimation value of the original image, and obtaining an initial binarization threshold based on the initial noise estimation value; performing binarization processing on the gradient amplitude image based on the binarization threshold to obtain a binarization region; calculating a gradient amplitude average value of the gradient amplitude image corresponding to the binarization region; updating the binarization threshold based on the gradient amplitude average value; sequentially repeating the binarization processing step and the binarization threshold updating step until a preset stop iteration condition is met; and taking the latest binarization threshold as the noise estimation value of the original image. The method is to firstly set or obtain an initial noise estimation value of an image, perform binarization processing on the gradient amplitude image, update the binarization threshold, and use the binarization threshold updated continuously to approximate the most accurate estimation of the noise in the image.