624 results about "Image noise" patented technology

The invention relates to the technical field of welding defect identification, and discloses a welding defect identification method and system based on a molten pool image. According to the method, a multispectral high-speed camera is used for collecting a molten pool dynamic image sequence in the welding process, after multi-scale morphological filtering preprocessing is conducted, a fusion feature vector is extracted through a depth separable convolutional network, and then the fusion feature vector is input into a defect classification model adopting a heterogeneous graph neural network architecture to obtain a defect probability distribution matrix. Then, constructing a dynamic sparse optimization model to position defects, generating a defect space coordinate set, and finally, outputting welding defect types and position information through hierarchical verification framework processing. According to the method, the problems of welding image noise interference, complex defect characteristics and the like are effectively solved, the accuracy and reliability of welding defect identification are improved, and powerful technical support is provided for welding quality control.

The invention relates to the technical field of image processing, in particular to a road area intelligent extraction system for unmanned aerial vehicle aerial images, and the system comprises an image noise filtering module which analyzes and inputs frequency domain information of the unmanned aerial vehicle aerial images, recognizes a target noise mode, and filters the unmanned aerial vehicle aerial images. According to the method, frequency domain noise analysis and notch filtering are combined, the sharpness of the road edge is reserved, meanwhile, high-frequency noise and periodic interference are restrained, and the visibility of a low-contrast road area is improved; local pixel spectrum dispersion calculation is introduced and is dynamically fused with morphological parameters, and direction consistency and length-width ratio constraints are combined, so that the connectivity perception capability of the fragmented non-paved road is enhanced, and the fracture misjudgment under the form-changeable terrain is reduced; a normalized vegetation index and multi-band reflectivity are combined for modeling, water reflection interference is eliminated through a blue light band standard deviation, and a space mask of a non-road surface feature interference area is constructed.

The invention provides an image noise mark feature selection method and system, a storage medium and a computer. The method comprises the steps of obtaining a to-be-processed image noise mark data set; embedding a sample set in the image noise mark data set into a multi-granularity fuzzy cluster to construct a dynamic fuzzy membership evaluation matrix; dynamically evolving a multi-level high-precision granular ball cluster; obtaining mark distribution with high identification degree; constructing a rough perception feature evaluation framework based on granular ball topology driving, extracting decision equivalence classes by combining rough set upper and lower approximation and extended positive domain models, and determining and measuring the contribution degree of each feature to a decision system by fusing multi-granular-ball decision boundary information based on a dependency degree quantitative model; a particle and ball structure consistency verification mechanism is introduced, and multi-level evaluation is carried out on the importance of the features through dependency and consistency. According to the method, the optimal feature subset with strong anti-noise performance and high discrimination capability is obtained, and stable and efficient input support is provided for a subsequent image noise mark learning model.

The invention relates to the technical field of pattern recognition, in particular to an ecological restoration nursery stock quality detection system based on pattern recognition, which comprises an image acquisition module for controlling camera equipment to acquire nursery stock images under target time and illumination conditions to obtain an original image set; and performing format unification and cleaning on the original image set, removing image noise and irrelevant information, and generating a cleaned image set. According to the method, the image pickup equipment is controlled to obtain the original nursery stock image under the target time and illumination condition, the consistency and stability of image data are improved, after unified format and information cleaning are carried out on the original image, image noise and invalid information are eliminated, and the nursery stock feature extraction precision is improved. Based on multi-dimensional feature extraction of colors, textures and shapes and comparative analysis of known quality standards, accurate judgment of seedling growth quality is realized, and scientificity and reliability of seedling screening are improved.

The invention provides a dolomite multi-scale digital core reconstruction method and device, electronic equipment and a medium, and the method comprises the steps: inputting a to-be-processed CT scanning two-dimensional slice image of a dolomite sample into a reconstruction model, and obtaining a multi-view SEM style image and view parameters; inputting the style image and the visual angle parameter into a NeRF model to obtain a three-dimensional high-resolution image; the reconstruction model is obtained based on the following steps: adjusting a CT scanning two-dimensional slice image and an SEM image of a dolomite sample according to a unified standard; inputting a CT scanning two-dimensional slice image and an SEM image which are in a unified standard into a FastGAN network for preprocessing of image enhancement and augmentation to obtain a standard simulation image; and training a generator in the CycleGAN network based on the standard simulation image to obtain a reconstruction model. The method can solve the problem that a traditional digital core technology is limited by image noise and equipment cost, and nanometer and micrometer multi-scale fusion is difficult to achieve.

An image processing method, performed by a computer device, comprising: extracting sketch texture features at multiple scales from a sketch image; extracting image noise features at multiple scales from preset noise; determining color guide information corresponding to the sketch image; encoding, for each scale, a noise feature based on a sketch texture feature and the color guide information to obtain multi-scale image features; and performing multi-scale decoding on these image features to obtain a colored image comprising a sketch texture corresponding to the sketch image and a color based on the color guide information. A related training method and apparatus are also provided to develop models for this image processing technique.

The invention relates to the field of computer vision, in particular to an image feature recognition method based on computer vision, which comprises the following steps of: preprocessing an input image, removing image noise and correcting image gray deviation to obtain a preprocessed image; performing multi-scale feature extraction on the preprocessed image, obtaining image local texture features and global contour features under different scales, performing dynamic weight fusion, calculating dynamic fusion weight based on a feature response value and a local complexity factor, and performing dimension reduction processing on fusion features through a principal component analysis algorithm to obtain a target feature set; and matching the reference features, calculating the Euclidean distance between the target feature set and the reference features, determining an adaptive matching threshold, judging a matching result, and completing image feature recognition. According to the method, through adaptive filtering and gray level equalization preprocessing, Gaussian pyramid multi-scale feature extraction and dynamic fusion and adaptive threshold matching, feature extraction accuracy, characterization capability and recognition robustness are improved.

The invention discloses an intelligent scalp detection system and a detection method thereof, relates to the technical field of intelligent scalp detection, and aims at obtaining shooting conditions and definition evaluation through real-time environment perception and quality detection, solving hair occlusion and image noise by using deep learning segmentation and directional enhancement, showing a scalp deep structure by means of multi-modal fusion and splicing, and improving the detection accuracy. A diagnosis and optimization strategy is output in comprehensive analysis and self-adaptive closed-loop feedback; through software algorithm level expansion, online training and multi-label pathological expansion can be performed on multi-user long-period data, and adaptive evolution and group statistical analysis are realized; according to the method, the image acquisition stability and segmentation accuracy can be effectively improved, the adaptability of detection to complex illumination and multi-band information is enhanced, the risk of pathological misjudgment such as grease, inflammation and fungi is reduced, and personalized nursing and wide health management application are supported.

The invention relates to the technical field of machine vision and product defect visual prediction, in particular to a product defect visual prediction method and device based on X rays. According to the method, edge extraction and entropy-based analysis are utilized to enhance defect positioning, meanwhile, the challenges of image noise and non-uniform intensity change are solved, and in order to improve robustness, a self-adaptive threshold strategy combined with DBSCAN clustering is adopted to distinguish defects and noise. Therefore, the problems that in the prior art, the ratio of product defects is low, the visual prediction efficiency of edge blurring is low, and the accuracy is not high are solved. Compared with the prior art, noise reduction, edge detection and crack identification are enhanced. By integrating X-ray image processing, the method improves accuracy, reduces false positive and improves detection efficiency.

The invention relates to the field of tea disease detection, in particular to a tea disease detection method based on a lightweight YOLOv8 model. According to the technical scheme, the method comprises the steps of collecting various types of tea disease images; the method comprises the following steps: preprocessing multiple types of collected tea disease images, including illumination normalization, geometric enhancement, illumination enhancement, adaptive histogram equalization, a multi-scale Retinex algorithm, dynamic Gamma correction and image noise removal, and marking scab regions in different types of tea disease images to obtain a training data set; a lightweight YOLOv8 model is constructed; using the training data set to train the lightweight YOLOv8 model; and inputting a collected tea disease image into the trained lightweight YOLOv8 model, and outputting a tea disease type through the trained lightweight YOLOv8 model. The method is suitable for tea disease detection.

The invention discloses a processing method for capturing an object image in real time based on a computer vision technology, and relates to the technical field of computer vision, and the method comprises the following steps: 1, capturing an object image in real time through a USB camera and a network camera, and transmitting image data to a server side in real time through TCP / IP for processing; step 2, removing noise by using a Gaussian filter, enhancing contrast by histogram equalization, extracting image features through a hybrid architecture based on Inception V3 and ResNet-50, optimizing feature extraction through a multi-scale attention mechanism by the hybrid architecture, performing pixel-level segmentation by using a full convolutional network, dividing the image into different object areas, and performing image segmentation on the object areas; post-processing is carried out on the segmentation result; according to the method, the Gaussian filter and the histogram equalization technology are adopted, image noise is effectively removed, the contrast ratio is enhanced, image features are extracted through the Inception network, pixel-level segmentation is carried out in combination with the full convolutional network, and high-precision and high-efficiency object segmentation is achieved.

The invention discloses a binocular camera global ultra-sensitive depth direction vibration measurement method, and relates to the technical field of structural vibration measurement. The method comprises the following steps: completing internal reference and external reference calibration of a binocular camera based on a Zhang's calibration algorithm, and ensuring the three-dimensional reconstruction precision; in image processing, effective pixels with high dynamic response are screened by calculating pixel intensity time-history variance, a pixel intensity space-time matrix is constructed, and singular value decomposition is adopted to extract front k-order modals to reconstruct the pixel intensity matrix so as to reveal real signals originally submerged in imaging noise and rounding errors. And multi-direction cost aggregation is carried out by using a semi-global matching algorithm, the parallax calculation robustness in a low-contrast scene is improved, and a high-precision depth map is generated. And depth information is converted into three-dimensional space information in combination with camera parameters, so that non-contact measurement of the vibration time travel curve in the full-field depth direction is realized.

The invention relates to the technical field of material analysis, in particular to a method for evaluating a pore structure of a refractory material by using industrial CT, which comprises the following steps: selecting an X-ray source parameter, a detector parameter, a focal length and the like of the industrial CT to be matched with a refractory material with different thicknesses, selecting a filter to avoid image noise, identifying an internal structure of the refractory material, and evaluating the pore structure of the refractory material. And obtaining a two-dimensional projection image of the internal structure. According to the method, the image noise is reduced through the filtering technology, the internal structure of the refractory material can be identified more accurately, the data integrity is ensured, a more accurate three-dimensional model can be generated, the analysis of the pore structure is more detailed and accurate, and by quantitatively analyzing the pore size, shape and distribution of the refractory material, the analysis accuracy of the refractory material is improved. The performance change of the material can be effectively monitored, the prediction capability of the refractory material in practical application is improved, and the application safety and reliability of the refractory material in an extreme environment are enhanced.

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 particularly relates to a liquid level monitoring system for a flow battery based on machine vision. The liquid level monitoring system comprises an image acquisition module; the liquid level detection module is used for analyzing based on the liquid level image of the flow battery to obtain an analyzed and evaluated liquid level of the flow battery; the display and interaction module is used for outputting and presenting the liquid level detection result; and a system control module. According to the method, the first evaluation liquid level preliminarily locates the liquid level contour in a rapid gradient calculation mode, the second evaluation liquid level accurately extracts the edge through multi-stage processing, and the two parts complement each other; in the comprehensive analysis stage, by means of unique mean value calculation, a method for constructing a geometric model based on liquid level deviation and calculating a difference coefficient, and in combination with a preset threshold value, an additional liquid level value is matched, errors of a single detection method can be effectively eliminated, and detection deviation caused by factors such as environment interference and image noise can be corrected.

The invention discloses a multi-agent PCB (printed circuit board) design document automatic extraction and table completion method. According to the method, the table area in the PCB design document is automatically extracted by training the YOLOv11 model, and missing table lines are complemented by using an image processing algorithm, so that the extraction precision of table data is improved. A multi-agent architecture is adopted, text extraction and table data extraction and combination are carried out respectively, and a clear document is finally generated and output. According to the method, the defects of an existing OCR technology in table extraction can be overcome, especially the accuracy problem under the influence of table line missing or image noise can be solved, and the method has high practical value and innovativeness.

The invention belongs to the technical field of medical image processing, and particularly relates to a medical image processing method and system based on multi-modal fusion, and the method comprises the steps: obtaining a CT image and a corresponding digital pathological image, and carrying out the pairing of the CT image and the corresponding digital pathological image; respectively inputting the paired CT image and digital pathological image into a CT branch and a pathological branch; the CT branches extract macroscopic morphology and internal density distribution to obtain a CT feature map, and the pathological branches extract microscopic details to obtain a pathological feature map; the CT feature maps and the pathological feature maps from different layers are fused in a layer-by-layer cascade and feature splicing mode, and a comprehensive feature map is obtained. The method can still stably extract and identify key image features and modes in the face of image noise, different imaging devices or image visual differences among individual samples, and has high image analysis robustness.

The invention provides a noise modeling and denoising method for a low-light-level image intensifier. The noise modeling and denoising method comprises the following steps: step 1, establishing an imaging noise model for the low-light-level image intensifier; step 2, collecting output data of the low-light image intensifier under different illumination conditions, calibrating parameters of an imaging noise model, and superposing the calibrated imaging noise model on a noise-free image sample to construct a training data set conforming to actual imaging characteristics; step 3, designing an image denoising network based on the convolutional neural network, the input of the network being a noise-containing image, and the output being a noise-suppressed clean image; and step 4, training and optimizing the convolutional neural network based on the training data set, and verifying synthetic data and real data. According to the method, the adaptive capacity and generalization performance of the neural network to complex noise can be effectively enhanced, and finally, more accurate and stable image enhancement and denoising effects can be realized in a low-light imaging scene.

The invention relates to the technical field of image enhancement or restoration, in particular to an image restoration method based on diffusion mode and prior feature generation and a related device. In order to solve the technical problem of poor image restoration quality in the prior art, real data is utilized to train a priori feature restoration image model composed of a priori feature extraction network, a U-net-based mask combination restoration network and a mask restoration network; then, constructing a priori feature repairing and enhancing image model based on a training priori feature extraction network, an image noise adding network, an image denoising network, a priori feature estimation network and a training mask repairing network; a training image noise adding network, a training image denoising network, a training prior feature estimation network and a training mask repairing network are extracted to form an image repairing model, the model is used for processing a to-be-repaired image, and efficient and high-quality image repairing is carried out on a specific area.

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.

An optical inspection system of removing a pattern relevant to surface metallic texture on circuits from an image to be inspected includes a first light source module, an image forming module and an operation processor. The first light source module includes a first polarizer and a linear light source. The first polarizer has a first polarization axis. The linear light source emits a linear light beam which passes through the first polarizer to project polarization beam onto the circuits. The image forming module includes a second polarizer and an optical detector. The optical detector receives a reflection beam generated by the linear polarization beam being reflected from the circuits and passing through the second polarizer. The operation processor receives the image to be inspected generated by the reflection beam, and analyzes the image to be inspected for determining whether the circuits have any defect except the surface metallic texture.

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 invention provides a slit-type spectral imaging method based on filtering phase reconstruction and a grating spectrometer. The method comprises the following steps: measuring wavefront aberration of a slit-type grating imaging spectrometer before slit filtering by using a wavefront sensor; based on a slit filtering principle, reconstructing a phase function after slit filtering by using wavefront aberration before slit filtering; and capturing a slit image, and reconstructing the slit image by using the reconstructed filtered phase function of the slit to obtain a final reconstructed image. According to the method, effective recovery of the slit imaging data of the grating spectrometer based on the slit is realized, the influence of optical aberration and image noise on the spectrum and spatial resolution of the spectrometer is effectively eliminated, and the slit imaging quality is remarkably improved and is close to the diffraction limit.

The invention discloses a hyperspectral image noise detection and evaluation method and system, and relates to the technical field of image processing, and the method comprises the steps: firstly, carrying out the superpixel segmentation of an original hyperspectral image through employing an improved linear iteration clustering method, and obtaining a plurality of superpixel sub-blocks; then, calculating a de-trending cross correlation coefficient and a de-trending cross correlation coefficient for the spectral reflectivity sequences of every two superpixel sub-blocks by adopting a de-trending cross correlation method; meanwhile, obtaining a noise sequence characteristic value range according to a randomly generated Gaussian white noise sequence, and taking the noise sequence characteristic value range as a reference of a hyperspectral signal noise range; and finally, performing spectral decomposition on a correlation matrix constructed by the de-trending partial cross correlation coefficients of every two superpixel sub-blocks to obtain a partial correlation matrix characteristic value under each preset scale, and performing calculation according to a noise sequence characteristic value range to obtain a noise proportion value and a de-trending partial cross correlation coefficient matrix after de-noising. According to the invention, the accuracy of noise detection evaluation of the hyperspectral image can be optimized.

The invention relates to the technical field of artificial intelligence, and discloses a low-illumination scene-oriented unmanned aerial vehicle imaging data enhancement method, which comprises the following steps of: S1, acquiring unmanned aerial vehicle imaging data and constructing a training data set; s2, data preprocessing based on illumination partition; s3, constructing and training a data enhancement model; and S4, enhancing the imaging data of the unmanned aerial vehicle. The data preprocessing based on the illumination partition in the step S2 comprises the following steps: generating a low-illumination area mask and a medium-illumination area mask based on the brightness information of the low-illumination image; a Gaussian smooth convolution operation is applied to the low-illuminance region identified by the low-illuminance region mask to suppress noise, and an original value is reserved for the medium-illuminance region identified by the medium-illuminance region mask to generate a pre-processed image. The problems of low-illumination image noise amplification and detail loss are solved by generating low-illumination region masks and medium-illumination region masks and respectively adopting noise reduction and retention strategies, and the balance of dark region noise reduction and bright region detail protection is realized.

The invention discloses a ViT-based automatic detection and recognition system and method for Manchu ancient book characters, and belongs to the technical field of artificial intelligence and digital literature protection. The system comprises a YOLO target detection module, a positioning network module, a ViT module and a classifier module. The YOLO target detection module is used for identifying and positioning each individual character area in the whole Manchu ancient book image, the positioning network module is used for automatically adjusting the position, the rotation angle and the scaling of the character image, and the ViT module is used for extracting the deep feature of the character image and modeling the character visual representation. And the classifier module is used for mapping the feature vectors output by the ViT module into specific character tags and splicing the specific character tags into a transcription text. The invention provides an end-to-end intelligent processing system, solves the problems of character pattern diversity, image noise, multi-language mixing and the like of Manchu literatures, and realizes high-precision identification of Manchu characters and intelligent sorting and translation of Manchu and Chinese bilingual literatures.

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.

The invention discloses a multi-frame fusion fixed image noise dynamic compensation system and method, particularly relates to the technical field of image processing, and is used for solving the problem that fusion boundary noise has artifacts due to existing independent noise compensation. The method comprises the following steps of: establishing a mapping relation between pixel-level imaging parameter difference degree and fusion weight by acquiring a multi-frame image sequence of different imaging parameters and identifying a transition region of brightness gradient dramatic change; extracting a frequency domain energy skewness feature and a chroma phase change boundary feature of the transition region, and generating a noise visibility correction factor through nonlinear fusion; dynamically constraining the compensation intensity range to realize multi-frame noise compensation collaboration; and finally, performing cooperative compensation and fusion output within the constraint range. Fusion boundary noise discontinuity is effectively eliminated, and the visual quality of high-dynamic-range imaging is remarkably improved.