242 results about "Quantization (image processing)" patented technology

The invention belongs to the technical field of image processing, and discloses a miniature ultrasonic image reconstruction method, device and equipment based on deep learning. The method comprises the following steps: constructing a lightweight U-Net model by a processed signal; a mixed loss function and a quantitative perception optimization and compression lightweight U-Net model are adopted, the processed model is deployed on an FPGA, a layered mapping strategy and a double-buffering mechanism are adopted to execute reasoning, and a reconstructed image is output; an up-sampling strategy is dynamically selected through a closed-loop feedback super-resolution module to optimize a reconstructed image, the ultrasonic imaging quality is improved through a lightweight U-Net architecture, hardware collaborative optimization and an end-to-end imaging pipeline, and a depth separable convolution, multi-scale attention gating and mixed loss function optimization algorithm is adopted; the power consumption is reduced through FPGA accelerated reasoning, dynamic voltage frequency adjustment and on-chip memory optimization, the imaging effect is improved through quantitative perception training, and the system is high in resolution, low in power consumption, high in real-time performance and suitable for portable medical equipment.

The invention relates to the technical field of video coding and image processing, and discloses a code rate control method and system based on video image segmentation, and the method comprises the steps: carrying out the pixel-level semantic segmentation of a to-be-coded video frame sequence; extracting a foreground region of interest; calculating a corresponding segmentation uncertainty parameter; establishing semantic mutation parameters of the foreground region of interest; generating a semantic perception weight; executing region-level target code rate redistribution and quantization parameter mapping; and executing partition coding control. In the prior art, code rate control mainly depends on motion intensity or pixel complexity, and especially when a foreground target suddenly appears or disappears in a monitoring scene, a technical problem that a key target is blurred or a background code rate is wasted is easily caused. Due to the fact that the uncertainty modeling and semantic mutation sensing mechanism of the semantic segmentation result is introduced, priority coding of the foreground interest area is achieved under the frame-level code rate constraint condition, and the video coding quality and the code rate utilization efficiency are improved.

The invention provides a video coding method and device, electronic equipment and a storage medium, relates to the technical field of image processing, in particular to the field of video coding and the like, and can be applied to application scenes such as video live broadcast and the like. The specific implementation scheme is as follows: acquiring a reference frame which is adjacent to a current frame and has established initial ROI hierarchical division; multiplexing a motion vector generated by the reference frame in a motion compensation time domain filtering process, and determining an ROI position predicted value of the current frame; extracting feature points of the five sense organs of the current frame, and correcting an ROI position predicted value based on a feature point spatial topological relation in the initial ROI; respectively configuring differential quantization parameters for the corrected main face region, the corrected secondary face region and the corrected background region; dynamically allocating a three-layer area code rate according to a real-time network bandwidth; and outputting the coded frame of the current frame and the associated ROI level metadata. According to the scheme, the coding efficiency and quality can be improved.

The invention provides an impact deformation feature extraction method based on high-speed camera shooting and image processing, and the method comprises the steps: collecting the transient deformation data of the edge package of a remote controller housing under impact through high-speed camera shooting, recording the change speed of a strain rate, obtaining the material characteristics, the size of a package structure, and the geometric discontinuous features of an opening and closing part, generating continuous frame sequence images; performing strain field data quantification processing on the third visualization result, extracting strain field characteristic data, and judging influence weights of stress wave reflection on strain fields of different functional areas of the shell in combination with a falling height and an impact angle of the remote controller; and improving edge packaging design parameters according to the deformation buffer effect and discontinuous quantitative indexes at the opening and closing positions, verifying the improvement amplitude of the deformation buffer effect, and obtaining strain field distribution data of the optimized shell.

The invention relates to the technical field of point location labeling, in particular to a self-adaptive load balancing point location labeling method and system based on image processing, and the method comprises the following steps: obtaining an image source division region, extracting an edge pixel number texture direction number color channel standard deviation, generating region detail redundant structure distribution information, and monitoring node state data. The method comprises the following steps: constructing a node resource load state mapping set, matching according to a regional detail redundancy ratio and a node state, establishing a task allocation relationship, extracting a boundary gray derivative, screening a boundary stable candidate point location set, calling point location coordinates, sending the point location coordinates to corresponding nodes, executing target verification, writing in an image frame, and generating a labeling result. Quantitative extraction is performed on distribution among image region edge pixels, texture directions and color channels, and task assignment is accurately paired in combination with image structure complexity and node states, so that resource mismatching and processing retardation are avoided, and task distribution accuracy is improved.

The invention discloses an intelligent image processing method and device for an AI chip and a medium, and relates to the technical field of intelligent image processing, and the method comprises the steps: collecting original image data, carrying out K-MEANS filtering and regularization processing, and generating an image data matrix; based on the image data matrix, calculating a similarity score matrix through a query-key-value matrix, extracting an image key region, and generating a key region mask matrix and an attention weight matrix; calculating a quantization precision level parameter according to the attention weight matrix and the key area mask matrix, and performing error compensation in combination with residual learning to obtain a quantization compression feature vector; and based on the quantized compressed feature vector, performing multi-scale similarity matching with a sample library through hierarchical feature fusion, and calculating a confidence score in combination with an attention weight. According to the method, the identification precision is ensured, and the deployment efficiency of the AI chip on the edge equipment is improved.

The invention relates to the technical field of image processing, in particular to a damper valve system adjustment method based on an artificial intelligence technology, a product, equipment and a storage medium. The method comprises the steps that parameter combinations of all valve systems of the shock absorber are collected; performing data quantization on the parameter combination to obtain a feature combination; and inputting the feature combination into a neural network model to obtain a change curve of the damping force along with the piston speed predicted by the neural network. According to the method, the external characteristic functions of the passive hydraulic shock absorber under different valve system structures are predicted through the artificial intelligence algorithm, and the number of tests in the chassis adjustment process is reduced.

The invention discloses a medical image artifact identification and elimination method based on a big data technology, and relates to the technical field of medical image processing, and the method comprises the steps: carrying out the preprocessing of collected image data based on gray normalization, and then carrying out the semantic segmentation and ROI positioning of the image content; and performing lesion segmentation on the positioned image content, selecting lesion features for quantification and fusion, performing model verification, and performing distributed deployment on the verified lesion segmentation model. According to the method, the problems of high missed detection rate of small nodules and high missed diagnosis risk of malignant lesions are solved through the lesion detection model, the false positive rate is reduced, the recall rate of the malignant lesions is improved, and through the lesion segmentation model, the segmentation adaptability to lesions of different sizes is improved, clear segmentation boundaries are obtained, surgical planning is assisted, and boundary positioning errors are reduced.

The invention relates to the technical field of remote sensing image processing, in particular to a remote sensing image processing method and device based on algorithm hardware collaborative optimization, equipment and a storage medium, and the method comprises the steps: carrying out the sea-land segmentation of a to-be-detected remote sensing image through a pre-constructed hybrid heterogeneous calculation model, and obtaining a land mask image; carrying out lightweight processing on the YOLOv5s architecture based on a mixed precision quantification strategy and a dynamic precision matching mechanism to obtain a ship detection lightweight model; regulating and controlling the ship detection lightweight model based on a dynamic parameter self-adaptive regulation and control strategy to obtain a self-adaptive ship detection model; and processing the remote sensing image to be detected by using the land mask image and the adaptive ship detection model to obtain a ship detection result. Through an algorithm hardware collaborative optimization strategy, the characteristics of a heterogeneous hardware platform are fully utilized, remote sensing image ship detection with high efficiency, low power consumption and strong stability is realized, the detection efficiency and precision are improved, the power consumption is reduced, and the system stability is enhanced.

The invention discloses an image linear target high-precision parameter detection method, and relates to the technical field of image recognition, and the method comprises the steps: carrying out the super-resolution reconstruction of a to-be-detected original image based on a perception optimization SRGAN algorithm, and obtaining a super-resolution reconstruction image; using an adaptive Canny edge detection algorithm to obtain contour information in the ROI region of the super-resolution reconstruction image; based on contour information in the ROI region of the super-resolution reconstruction image, performing linear fitting through an RANSAC algorithm for dynamically adjusting the number of iterations and a tolerant error to obtain a preliminary detection result; and constructing a multi-error-source joint model, and performing error compensation and quantization on the preliminary detection result based on the multi-error-source joint model to obtain a final straight line detection result, thereby completing detection. The method effectively improves the accuracy and stability of straight line detection in a complex environment, and is especially suitable for image processing tasks with low contrast, high noise or significant distortion.

The invention relates to the technical field of image processing, in particular to an aeronautical part identification character recognition method and system based on image processing, and the method comprises the steps: obtaining a character image of an aeronautical part, carrying out the stroke fracture reconstruction of a stroke fracture in the character image, so as to generate a reconstructed image, counting the number of connection pixel points newly added in the stroke fracture reconstruction process; performing optical character recognition on the reconstructed image to obtain a preliminary recognition result and a corresponding matching confidence coefficient; determining a stroke integrity index of the character image based on the number of the newly added connection pixel points and the total number of pixel points of the reconstructed image; the matching confidence coefficient of optical character recognition and the stroke integrity index based on reconstruction pixel quantization are combined, double judgment is carried out, it is ensured that the recognition result has the two characteristics of similar forms and reliable sources, and the accuracy and reliability of character recognition under the complex, high-light-reflection and abrasion conditions are improved.

The invention discloses an eye fundus image quality control method and device, a storage medium and electronic equipment, and relates to the technical field of image processing. The eye fundus image quality control method comprises the steps of determining a to-be-processed eye fundus image; based on an effective area of the to-be-processed eye fundus image, determining at least one type of quality quantification data corresponding to the to-be-processed eye fundus image, the effective area being used for representing an unshielded eye fundus structure area; and determining a quality control result of the to-be-processed eye fundus image based on the at least one type of quality quantification data corresponding to the to-be-processed eye fundus image, the quality control result comprising an image quality score and / or a quality problem corresponding to the image quality score. According to the eye fundus image quality control method provided by the embodiment of the invention, the score of the eye fundus image to be processed and the quality problem corresponding to the score can be intuitively known, so that related personnel can know the score and the quality problem, the normalization of the eye fundus image is improved, and standardization of eye fundus image data is facilitated.

The invention provides an image high-definition amplification method and system for generating a planning intention graph. The method belongs to the technical field of image processing and territorial space planning. The method comprises the following steps: acquiring an original planning intention map by using image acquisition equipment, and carrying out image preprocessing on the acquired original planning intention map; adopting a feature extraction network in a deep learning model to perform feature extraction on the pre-processed planning intention graph; professional knowledge in the territorial space planning field is collected and sorted and converted into rules and models which can be processed by a computer, and a planning knowledge base is constructed; constructing a feature optimization model in combination with the feature quantification result and the planning knowledge base; by combining professional knowledge in the territorial space planning field, a planning knowledge base is constructed, and the knowledge is introduced into the training of a deep learning model, so that the generated planning intention map not only is improved in visual quality, but also can better meet the planning requirements, and the reasonability and availability of each element in the image are ensured.

The invention belongs to the technical field of optical image processing, and particularly relates to an optical interference three-dimensional shape measurement method and system based on multi-frame interference image registration. Moving the linear displacement table, and acquiring interference images of samples on the linear displacement table according to a preset sampling interval to obtain an original interference image sequence; calculating an actual sampling interval between the original adjacent frames to obtain a sampling interval error accumulation amount of each frame; performing space coordinate compensation on the horizontal coordinate of each frame to obtain a corrected discrete horizontal coordinate; based on the discrete horizontal coordinates, rearranging the original interference image sequence by adopting a linear interpolation method to obtain a registered interference image sequence; extracting pixel points for recombination to obtain interference signals; and carrying out phase calculation on the interference signal to obtain the three-dimensional surface topography of the sample. According to the method, the interference image sequence is reconstructed by adopting a sampling error cumulant quantization and linear interpolation method, so that sub-pixel-level pattern registration and high-precision and large-view-field three-dimensional shape measurement are realized.

A method for bit rate allocation, an apparatus, an electronic device and a storage medium are disclosed, which relate to the field of artificial intelligence technology, such as computer vision, image processing and video encoder. The method for bit rate allocation includes: obtaining N region of interest (ROI) detection results of an image to be processed, wherein N is a positive integer greater than one, and the N ROI detection results comprise: detection results obtained by performing ROI detection on the image to be processed using N different detection operators respectively; generating a target block quantization parameter offset mask map corresponding to the image to be processed based on the N ROI detection results; performing a bit rate allocation on each image block in the image to be processed based on the target block quantization parameter offset mask map.

The invention relates to the technical field of image data processing, in particular to a weld joint pseudo-defect recognition and filtering system based on image processing, which comprises the following steps: acquiring a surface image of a local area of a weld joint through an image acquisition module; marking the welding seam sub-region as a photosensitive dominant sub-region or a photosensitive non-dominant sub-region through a region marking module; screening defect confidence suspected sub-regions through an initial recognition module according to a comparison result of texture feature quantized values of the photosensitive dominant sub-regions and the photosensitive non-dominant sub-regions; analyzing the spatial distribution condition of the defect confidence suspected sub-regions through a feature matching module so as to judge the defect filtering type of the local region of the welding seam; and determining a defect filtering strategy for the local area of the welding seam through an identification filtering module. Furthermore, by using the difference between the pseudo defect and the real defect in the optical response rule, a targeted regional differentiation analysis mechanism is adopted, and the pertinence of the filtering strategy is improved.

The application discloses a kind of based on data center application inference big model discovers network topology fault and false alarm self-healing visualization method and system thereof, including using grid division algorithm carries out grid division and forms DeepSeek inference network topology and chip resource mark;Visual image processing is carried out in DeepSeek inference network topology, complete network topology resource re-planning and visual data annotation, output DeepSeek inference big model inference conclusion;From network topology gateway configuration file, the transmission protocol of application is used as input parameter and carried out Deepseek inference big model inference analysis, carries out power quantization;DeepSeek inference network topology node is associated, and alarm is carried out in transmission protocol through the fluctuation of power level.The application uses microfluidic chip to manage the chip that inference big model carries out operation, chip resource mark is input into inference big model, and then uses image definition position and inference big model embedded AI algorithm matching discovers network topology, improves big model inference conclusion and visual accuracy.

The invention relates to the technical field of image processing, and discloses an AI image super-resolution reconstruction method based on a multi-scale fusion mechanism. The AI image super-resolution reconstruction method based on the multi-scale fusion mechanism comprises the following steps: establishing a binocular image system; establishing a double-end collaborative model architecture; establishing a front-end model optimization mechanism; according to the method, through a scene adaptive feature labeling decision model, three-dimensional position driven quantization and spectral response calibration replace manual presetting, and parallax-pose linkage correction is combined, so that subjective interference is thoroughly eliminated, and the misalign error suppression effect is improved by more than 40%; scale intelligent selection and cross-scale feature interaction are introduced into a dynamic interactive multi-scale attention convolutional neural network (DI-MSCNN), and dynamic definition of convolution kernel parameters is matched, so that feature extraction efficiency is improved by 30%-40%, and texture density differences can be accurately adapted; a detail hierarchical perception GAN (DLP-GAN) generates a strategy through hierarchical discrimination and detail partitioning.

The application discloses a kind of crack segmentation image post-processing optimization and quantization method, device and equipment, it is related to computer vision and digital image processing technical field, this method includes obtaining the initial binary crack mask chart corresponding to the image to be processed, and carries out geometric morphological filtering operation to eliminate non-crack area, obtains preliminary filtered mask chart;Contour detection is carried out to the preliminary filtered mask chart, and the crack-containing region of interest mask chart is identified and cropped;Break point connection processing is carried out to the region of interest mask chart to repair discontinuous crack fragment, generate continuous optimized crack mask chart, to carry out the geometric size parameter calculation of crack.This application can significantly improve the accuracy, integrity of crack segmentation result, and can realize the automation, accurate quantization of key parameters.

The application relates to the field of image processing and discloses a bone metastasis detection and positioning system and method based on SPECT bone imaging. The system extracts and processes multi-scale features of a to-be-detected image through a feature pyramid network to obtain a multi-scale feature map; then extracts metabolic features, morphological features and uptake mode features from the multi-scale feature map through a bone-specific feature model, and gradually fuses the metabolic features, the morphological features and the uptake mode features to obtain a bone-specific enhanced feature map; then extracts and fuses features of the bone-specific enhanced feature map through a mixed feature fusion model to obtain mixed fusion features; finally, the mixed fusion features are subjected to probability reasoning and double-uncertainty quantization processing through a probability reasoning network to determine a bone metastasis detection result, high-precision detection and positioning of bone metastasis lesions are realized, and the problems that existing technologies cannot effectively identify different size lesions and lack global perception ability are effectively solved.

The invention belongs to the technical field of image processing, and relates to a cell microtubule array uncertainty quantitative segmentation method fused with a large visual model, and the method comprises the steps: processing a cell microtubule microscopic image through a microtubule segmentation large visual model, and obtaining a microtubule likelihood graph of a pixel level; performing topological analysis on the microtube likelihood graph to obtain a plurality of manifold structures, and forming a microtube topological skeleton by a union set of the manifold structures; calculating an uncertainty interval of each manifold structure in the topology skeleton through a manifold uncertainty interval quantization network; and prior knowledge is introduced, a'pixel-topology-prior 'three-evidence-source fusion framework based on the D-S evidence theory is constructed, explicit modeling is performed on internal uncertainty of evidence sources, and thus an accurate microtube segmentation result is obtained. According to the method, the accuracy and the reliability of a final microtube segmentation result are improved by performing uncertainty modeling and fusion analysis on microtube form extraction results obtained from a pixel level, a topological structure level and a priori knowledge level respectively.

The application provides a model quantification method, device and equipment and storage medium, the method comprises: using integer algorithm to execute a plurality of operators of transformer model for image processing, the plurality of operators of the transformer model comprises linear operator and nonlinear operator, wherein the linear operator comprises matrix multiplication operator, the matrix multiplication operator adopts symmetric quantization to quantize floating point value to integer value;The nonlinear operator comprises an activation function operator and a layer normalization operator, the activation function operator adopts polynomial fitting to quantize floating point value to integer value, and the layer normalization operator adopts the mean and standard deviation of input data in the channel dimension to quantize floating point value to integer value.The transformer operator is quantified, so that the inference work of the transformer model for natural language is based on integer operation, so that it can be truly deployed on FPAG chip for practical application.

This invention relates to the field of medical image processing, specifically to a skin scar image segmentation method for automated skin scar region identification and quantitative analysis. It addresses the problems of low segmentation accuracy, inaccurate boundary modeling, and unreliable quantization calculations caused by insufficient feature representation, weak generalization ability, and reliance on manual intervention in existing methods. The method includes: S1 acquiring a skin scar image; S2 inputting a trained improved SAM2 segmentation network; and S3 outputting a segmentation mask. The improved SAM2 network is trained by constructing and preprocessing a dataset; it includes a frozen-parameter backbone image encoder, a cue encoder, a medical image feature guidance module (extracting texture details, color contrast, and edge morphology features), a residual enhancement module (fusing general features, cue vectors, and medical features through residual connections), and a mask decoder. During training, the backbone network parameters are frozen, and only the cue encoder, medical feature guidance module, residual enhancement module, and mask decoder are fine-tuned.

The invention provides a machine vision coding method based on feature distillation, and relates to the technical field of image processing.The method comprises the steps that an image to be processed is input into a machine vision coding model, and the model extracts first potential feature representation of multiple channels through an analysis encoder; the method comprises the following steps: quantitatively dividing into basic layer quantitative features containing semantic and spatial structure features and enhancement layer quantitative features containing detail and texture features; the hyper-priori correlation module encodes hyper-priori information and generates enhanced auxiliary features and basic auxiliary features, and the conditional entropy coding network realizes encoding and decoding of the basic layer quantization features and the enhanced layer quantization features based on the enhanced auxiliary features and the basic auxiliary features. A machine vision task result is obtained through a feature transformation and task processing module, and after splicing is conducted through a splicer, a reconstructed image is output through a synthesis decoder. According to the invention, both machine vision and image reconstruction can be considered.

An image processing apparatus for forming an enhanced image is disclosed. The apparatus comprises one or more processors configured to: receive one or more input images; form, from each of the one or more input images, a respective feature representation, each feature representation representing features of the respective input image; and subject the one or more feature representations to a symmetric pooling operation to form an enhanced image from at least some of the features of the one or more feature representations identified by the symmetric pooling operation. The apparatus may generate images with increased photoreceptive dynamic range, increased bit depth and signal-to-noise ratio, with less quantization error and richer colour representation.

The embodiments of the present specification provide an image encoding method, an image decoding method and an image processing system, wherein the image encoding method comprises: obtaining an original residual of a first image encoding unit, wherein the original residual is used to reflect the difference between the original pixel and the predicted pixel of the first image encoding unit; determining a unit index of the first image encoding unit based on the original residual, wherein the unit index is used to reflect the content complexity of the first image encoding unit; and performing quantization encoding on a second image encoding unit based on the unit index to obtain an image encoding result, wherein the first image encoding unit and the second image encoding unit are image encoding units obtained by dividing the same image at different encoding stages. By using the unit index to guide the quantization encoding, the quantization precision can be dynamically adjusted during the encoding process. Finally, while maintaining the subjective visual quality, the code rate allocation is optimized, and the overall encoding performance and compression efficiency are improved.

The application relates to an NPU-based deep learning model training and deployment optimization method, relates to the technical field of image processing, and comprises the following steps: constructing a deep learning model and training; based on a comprehensive quantization precision optimization algorithm and sample images corresponding to multiple target detection tasks, determining an optimal quantization precision strategy corresponding to each sample target detection task, combining sample images corresponding to target detection tasks of a currently deployed edge device, and configuring the quantization precision of a pre-trained deep learning model deployed to the edge device; based on sample images corresponding to target detection tasks of the currently deployed edge device and a dynamic threshold adjustment mechanism corresponding to each sample target detection task, constructing the dynamic threshold adjustment mechanism corresponding to the target detection tasks of the currently deployed edge device, embedding the configured pre-trained deep learning model, and deploying the deep learning model, so that the effect of deploying the deep learning model on the edge device is improved.

This invention discloses a method and system for candidate optimization, continuous repair, and quantization of crack images in complex backgrounds, belonging to the field of computer vision and digital image processing technology. To address the problems of crack recognition in complex backgrounds being susceptible to false target interference, discontinuous cracks, and difficulty in stable quantification of engineering parameters, this invention obtains the crack response probability map and determines a linked dual threshold. It retains low-threshold regions connected to the high-threshold region to generate initial candidate regions. The ratio of skeleton length to area is calculated as a thinness index for screening. Skeleton endpoints are extracted, and bridging pixel paths are generated to achieve local repair when the endpoints of different connected domains meet preset multi-condition constraints. The repaired region is then skeletonized to construct a graph structure. The number of original skeleton segments, the total number of cracks, and the total length are statistically analyzed. Width statistics and direction parameters are calculated by sampling along the skeleton using distance transformation. This effectively suppresses false targets in complex backgrounds, accurately connects discontinuous cracks, and outputs stable, accurate, and interpretable engineering parameters.

This application provides an image encoding and compression method, apparatus, storage medium, and program product. The method relates to the field of image processing. Before image encoding, it extracts the content features of the original image, obtains at least one distorted image of the original image, and extracts the content features and distortion behavior features of each distorted image. Using a prediction model, based on the quality target, the content features of the original image, and the content features and distortion behavior features of each distorted image, it predicts the quantization step size corresponding to the quality target. The original image is then encoded according to the corresponding quantization step size. By fitting the relationship between the quantization behavior quantization step size and the quality index through the prediction model, the corresponding quantization step size can be inversely derived from the quality target of the encoding. Furthermore, the image encoding quality can be precisely controlled according to the corresponding quantization step size, improving the subjective quality of the image encoding result and ensuring that the image encoding result meets the given quality target.

The invention relates to a non-standard JPEG (Joint Photographic Experts Group) coding processing method for dynamic code rate control. The method comprises the following steps: presetting a multi-frame joint image processing scene, obtaining an original reference image, and carrying out combined processing of boundary expansion and random bias addition to obtain a preprocessed reference image; dividing macro blocks for the preprocessed reference image, and distributing an independent coding parameter configuration space after verification and compliance; frequency domain conversion is executed according to a division result, an initial quantization parameter is calculated through an index model, and a dynamic quantization table is dynamically adjusted; executing non-standard entropy coding to obtain an indexed macro block coding code stream; combining the indexed code stream and a macro block division result to construct an index table containing a macro block serial number, an initial address and a code stream length; analyzing the code stream to obtain quantized data, and performing inverse quantization by using a dynamic quantization table to obtain spatial domain data; enabling the format of the reconstructed image to be consistent with that of the preprocessed reference image through reverse preprocessing, and performing real-time processing on the linkage input image to obtain a reconstructed reference image; and non-standard JPEG coding processing of dynamic code rate control is realized.