145 results about "Gaussian blur" patented technology

The invention relates to an optimization method and device for sparse view angle three-dimensional Gaussian splash, and belongs to the technical field of three-dimensional reconstruction in computer vision, and the method comprises the steps: collecting a sparse view angle image; a multi-view stereoscopic vision model based on deep learning generates a geometrically consistent depth map for the sparse view image, converts the depth map into point clouds and fuses the point clouds to obtain dense point clouds; sampling dense point clouds by adopting voxel-guided farthest point sampling to obtain initialized point clouds, and constructing a three-dimensional Gaussian field; rendering the three-dimensional Gaussian field through an enhanced geometric renderer to obtain a rendering depth and a rendering normal; constructing a multi-level geometric regularization loss function, and optimizing the three-dimensional Gaussian field; and performing optimization adjustment on the three-dimensional Gaussian field based on a shape-scale constraint criterion and a two-stage adaptive opacity constraint strategy to obtain an optimized three-dimensional Gaussian field. According to the method, the problems of initialization failure, insufficient geometric supervision and element out-of-control of 3D Gaussian splashing under the sparse view angle are solved.

The invention discloses a virtual-real fusion exhibition display interaction system and a multi-mode perception method, and belongs to the technical field of exhibition display interaction. The system collects audience eyeball fixation points, gesture actions and ambient light data through AR / VR equipment, analyzes coordinates of a region of interest through an eyeball fixation point attention mechanism, a gesture space-time encoder and a multi-modal fusion unit, triggers holographic projection explanation and virtual exhibition stand light and shadow dynamic adjustment (including illumination intensity, color, Gaussian blur and the like) based on a threshold value, and performs real-time display on the virtual exhibition stand. And multi-user collaborative interaction is realized through federal learning. According to the method, reinforcement learning is adopted to optimize an event-driven threshold value, and virtual and real visual splitting is eliminated in combination with ambient light adaptive mapping. The problems of low participation degree, insufficient single-mode interaction information and multi-user cooperation of traditional exhibition are solved, interest analysis accuracy is improved through multi-mode fusion, personalized experience is enhanced through dynamic interaction, the method is suitable for multiple scenes such as museums and science and technology museums, and exhibition intellectualization, immersion and group interaction efficiency are effectively improved.

The invention belongs to the field of machine vision, and relates to a novel circle detection method in a complex environment. The method comprises the following steps: firstly, preprocessing an input color image, converting the color image into a grayscale image, then performing Gaussian blur processing, and applying a Canny edge detection algorithm to obtain an edge image; then, a findContours function of OpenCV is used for detecting a contour in the edge image, and traversal processing is carried out on the contour; secondly, circle detection is conducted on each contour through a findcircle () function, and circle fitting is conducted through a three-point circle determination method or a least square circle fitting method; performing accuracy verification on the fitted circle; and finally outputting the number and parameters of the identified circles, such as circle center coordinates and radiuses. The method is low in operand, can rapidly and accurately detect a circle in a complex environment with a weak light source and noise, does not need to occupy high computing resources, and has high detection speed and precision.

The invention relates to the technical field of image recognition, in particular to a packaging film defect detection system based on image recognition, which comprises a candidate region extraction module, a form enhancement module, a feature extraction module, a fuzzy evaluation module and a defect judgment module. According to the method, a self-adaptive binarization value is generated by calculating a pixel local neighborhood gray average value and a standard deviation, background texture noise and illumination non-uniform interference are effectively suppressed by using a dynamic threshold, a defect entity contour is reconstructed by combining polygonal broken line approximation and morphological filling operation, key geometric angular points are reserved, and internal fracture textures are repaired. Constructing a multi-dimensional feature vector set covering a texture energy entropy value and illumination uniformity, deeply analyzing a defect surface microscopic optical structure, introducing a Gaussian fuzzy membership function and an information entropy weight distribution mechanism, objectively quantifying the affiliation probability of feature components to different defect categories, and determining the defect surface microscopic optical structure. The limitation of a single rigid criterion is broken through, and accurate classification of fuzzy boundary defects is realized.

The invention discloses a robot imitation learning method based on a diffusion model, and belongs to the technical field of robot learning and body intelligence. Comprising the following steps: carrying out standardization processing on input image observation data and a robot state, and carrying out image enhancement by adopting gray scale transformation, random erasure and Gaussian blur; based on the enhanced image and the robot state, training a diffusion model to generate an action sequence, including forward diffusion, conditional feature fusion, noise prediction and joint loss optimization; in real-time control, features are extracted through an image enhancement network, sampling is accelerated through DDIM to generate an action sequence, noise scheduling coefficients are dynamically adjusted based on visual feature differences, and closed-loop optimization is achieved. According to the method, the image enhancement technology and the diffusion strategy are deeply fused, joint optimization of visual features and action sequences is achieved, the action generation accuracy and robustness of the robot under the complex visual interference and small sample conditions are remarkably improved, and the success rate is improved by 18% compared with a base line under 40 sample sizes.

The invention discloses a ground penetrating radar data fusion optimization method for tunnel detection. The method comprises the following steps: step 1, selecting an antenna frequency; step 2, cutting data; step 3, preprocessing the data; step 4, multi-frequency data space-time standardization; step 5, multi-frequency data fusion; step 6, Gaussian blur processing; and 7, image screening: selecting an optimized image with the best robustness. The method disclosed by the invention is specially used for tunnel detection radar data, interferences of metal davits, expansion joints and the like can be effectively removed, the fused multi-frequency data naturally transits near the boundary, the boundary and double images basically disappear, and the problems in practical application of tunnel engineering detection are effectively solved. According to the method, contradictory unification of large detection depth and high resolution is realized on the same image, targets such as steel bars and layers in the image are more easily and automatically identified by an artificial intelligence algorithm, and a technical foundation is laid for subsequent AI interpretation.

The invention discloses a panoramic image splicing method, system and device, and relates to the technical field of image processing, and the method comprises the steps: collecting images around a vehicle at different visual angles; using the MASK masks to generate overlapping region masks of the different view angle images, and performing local feature detection on the overlapping region masks to generate a transformation matrix; aligning the images of different visual angles according to the transformation matrix; a Canny edge detection operator is fused in splicing seam energy detection, the energy weight of the image edge area of the aligned image is enhanced, and a splicing seam is guided to actively avoid an object with remarkable structural features; b spline interpolation is introduced to optimize an initial splicing seam path; constructing a Laplacian pyramid along Gaussian blur of a splicing seam, and sequentially carrying out gradual fusion to generate a seamless panoramic image; according to the method, the basic function of eliminating the visual blind area is realized, a clear and visual panoramic view can be provided for a driver, and the driving safety is effectively improved.

An image edge smoothing processing method based on an ink droplet diffusion principle can be divided into three parts: a first part, performing various image preprocessing on a mask part of a specified image, including graying, binaryzation, opening operation, median filtering and connected domain analysis, so as to corrode and eliminate small noise points and convex regions; the second part is that local closed operation is performed on the preprocessed image to fill up the concave area, the local closed operation may cause significant distortion of the image, and only the part which is small in filling area and does not cause significant deformation of the image is reserved through image subtraction and connected domain analysis; and a third part, performing bilinear interpolation and Gaussian blur on the image to resist aliasing, and then applying a super-resolution algorithm to obtain a high-definition image with smooth edges. According to the method provided by the invention, the problem that the image edge is uneven in the color separation treatment process of template manufacturing in the textile field is solved, so that the working efficiency of factory template manufacturing is improved.

The invention provides a meteorological radar radial speed feature extraction method for near strong wind forecasting, which comprises the following steps: converting radial speed data into a digital image, obtaining a plurality of retreating / approaching wind speed zones by searching color block contours with different threshold values, and expanding corresponding wind speed zones. And judging whether the speed extreme value and the center distance of the two meet characteristic conditions or not, and finally obtaining radial speed characteristics conforming to convection strong wind. According to the invention, radar radial speed data is converted into a digital image, and image identification and contour extraction are carried out, so that the processing speed is greatly improved; the space continuity of the data is enhanced through a Gaussian blur method, so that the subsequent image recognition is more accurate; the retreating wind speed area and the approaching wind speed area are combined together as a group of wind speed areas for treatment, so that the characteristics of the wind field are reflected more comprehensively; by judging the contour overlapping relation and de-weighting according to the speed value, the feature with the large speed value is reserved, noise interference can be effectively removed, and the reliability of the final extraction result is improved.

The invention discloses an intelligent psychological health education system based on an education robot. The system comprises (1) AI family-school cooperation: an improved YOLOv7 algorithm captures classroom behaviors, dynamic Gaussian blur protects privacy, directionally pushes student participation images containing desensitization IDs, supports AI to generate personalized communication verbal skills, and improves the degree of participation of parents; (2) an interdisciplinary fusion engine: constructing a knowledge graph to associate psychological courses with mathematics, sports and other subjects, predicting an optimal fusion opportunity in combination with educational administration data, and generating a double-teacher collaborative teaching plan; (3) interest and emotion driven daily penetration: analyzing student interest labels based on a latent semantic model (LFM), and triggering personalized family task pushing in combination with multi-modal emotion recognition to realize intelligent penetration of psychological health education in a family scene; and (4) full-link privacy protection: adopting differential privacy, block chain and federated learning technologies to realize'all anonymity 'and'availability and invisibility' of data, and ensuring information security through three-level desensitization and quantum encryption, so that the system meets the requirements of'personal information protection method '.

The invention discloses a blurred bar code image processing method and system fused with super-resolution restoration, and relates to the technical field of bar code image restoration, and the method comprises the four steps: firstly, extracting the frequency domain and texture complexity index of a target blurred bar code image, and achieving the automatic classification of dynamic, defocusing and Gaussian blur, and exclusive denoising; building a double-branch feature extraction network, extracting a structure and texture feature map, and generating a cross-scale fusion feature map; outputting a bar code image restoration first draft; and finally, constructing a dual-objective loss function for iterative optimization, and outputting a final clear image. The beneficial effects of the method are that multi-dimensional feature classification enables fuzzy type identification to be accurate, and exclusive denoising lays a high-quality foundation for subsequent restoration; the multi-modal feature fusion is combined with the layered super-resolution technology, a double-target loss function and a closed-loop verification mechanism ensure that the final image can be stably decoded, and the method is suitable for mainstream barcode types and has high practical value in industrial barcode recognition scenes.

This invention discloses an automated anterior tooth feature parameter extraction method and evaluation system based on computer vision, comprising: acquiring and visualizing a full-mouth scan image of a patient to be processed; cropping the corresponding tooth portion image; using Gaussian blur to eliminate specular reflection; using color segmentation to set thresholds for the H, S, and V channels respectively; separating the teeth and gingiva in each tooth portion image and performing morphological processing on each; extracting the tooth and gingiva portion contours using contour extraction methods; further refining the segmentation accuracy using the Chan-Vese active contour model algorithm and the energy function within the tooth portion contour; extracting corner points using the Shi-Tomasi corner detection method and then clustering them; extracting other important evaluation feature points through curve fitting and extreme value finding methods; and finally calculating relevant objective indicators for tooth evaluation and automatically generating the corresponding tooth score, thus completing the automated anterior tooth evaluation.

The invention provides an e-commerce commodity graph differentiation generation method and device, equipment and a medium, and the method comprises the steps: obtaining an original to-be-shelved commodity graph of e-commerce, and finally obtaining the disassembly information; the method comprises the following steps: adjusting an original commodity image into 980 * 980 pixels, performing Gaussian blur processing at the same time, and then converting image pixel data into a Base64 coded character string; constructing a composite cue word according to the image data, the disassembly information and a format required by generation; inputting the composite cue word into a model to generate a new scene description; inputting the new scene description, the original commodity graph and the synthesis requirement into a model to generate a synthesis cue word, and then inputting the original commodity and the synthesis cue word into an image-text model to generate a differentiated commodity graph; setting the output resolution, the number of iterations and the learning rate of the SuperIR model, and inputting the differentiated commodity graph into the SuperIR model to generate a differentiated commodity optimization graph; the defects of low efficiency, poor scene adaptation, main body distortion and difficulty in repeated avoidance of a traditional method are overcome.

The application discloses a GAN neural network multiple distortion suppression model based on a coordinate attention mechanism, obtains an original neutron radiographic image from a neutron source; proposes a brand-new neutron radiographic image degradation model; adds Gaussian blur, Gaussian noise, Poisson noise and noise obeying gamma distribution to the clear neutron radiographic image; uses real white spot noise to train the GAN neural network, simulates white spot noise and randomly adds the white spot noise to the clear neutron radiographic image; constructs a multiple distortion suppression model of the GAN neural network based on the coordinate attention mechanism; uses Huber loss to train the constructed GAN neural network based on the coordinate attention mechanism; inputs a real neutron radiographic image containing multiple distortions into the trained multiple distortion suppression model as input, predicts the original image of the real neutron radiographic image containing multiple distortions, and obtains a target result.

The invention provides an X-ray imaging detection method and device. The method comprises the following steps: correcting original image data of X-rays to obtain a corrected original image, extracting high-frequency information of the corrected original image through Gaussian blur, and performing difference and noise suppression processing to obtain preprocessed noise reduction data; carrying out binarization processing on the preprocessed noise reduction data to obtain noise reduction data; performing edge processing on the noise reduction data through a Sober operator to obtain an edge image; and after the edge image and the corrected original image are fused, the line is identified through Hough transform, a detection part image is obtained, the device comprises a module for realizing the method, through the method and the device, the ray machine device in the DR detection system is optimized, a high-frequency constant-voltage ray machine is replaced by a conventional power frequency ray machine, and the equipment cost is reduced.

The invention provides a waterproof board laying and hanging robot path recognition method based on machine vision, and relates to the technical field of image recognition. According to the method, an industrial camera carried by a waterproof board laying and hanging robot collects a waterproof board laying and hanging area image in a tunnel, perspective transformation is performed by combining internal and external parameters of the camera, and a laying operation image under an orthographic view angle is obtained; performing Gaussian blur, contrast enhancement, brightness normalization and other preprocessing operations on the laying operation image; after the laying operation image is preprocessed, a directional entropy gradient field is constructed based on local information entropy, a disturbance response is fused to generate a structure display function, and a structure feature map of the laying operation image is extracted; a path point sequence for navigation control of the laying and hanging robot is obtained through an image skeleton extraction and path point equal-interval sampling method, noise reduction processing is conducted on the path point sequence through sliding window mean filtering, smooth fitting is conducted on the path sequence through B-spline curve fitting, and a robot motion path is obtained.

The application discloses an active interference image anti-counterfeiting method, system, device and storage medium, which are corresponding solutions, and in the solutions, the adversarial disturbance can be adaptively distributed in an area which is not sensitive to human eyes, so that the invisibility of the disturbance is significantly enhanced, the application further introduces Gaussian blur, and the ability of the disturbance to resist image transformation is improved through adversarial training, so that the application has high robustness; in addition, the application does not depend on prompt words, an attacker is difficult to bypass the protection by using multiple prompt words, and the application only fine-tunes the weight of a variational autoencoder, so that the training resources consumed are far less than those of a traditional active interference method for a U-Net.

The invention relates to a building group contour automatic generation method based on TLS point cloud, and the method comprises the following steps: S1, obtaining the point cloud data of a building at different site positions, and carrying out the point cloud registration of the point cloud data, and obtaining a complete point cloud model of a building group; s2, carrying out downsampling, noise reduction and cutting on the complete point cloud model to obtain a preprocessed complete point cloud model; s3, carrying out point cloud slicing on the preprocessed complete point cloud model, and intercepting a middle section point cloud; s4, performing point cloud projection on the point cloud of the middle section to obtain a projection two-dimensional plane; s5, dividing the projection two-dimensional plane into grids, then converting the grids into a grey-scale map, and carrying out binarization processing; and S6, performing Gaussian blur, Canny edge detection and Hough transform processing on the binarized grey-scale map to obtain a point cloud contour. Compared with the prior art, the method has the advantages of realizing rapid and accurate building group wall line extraction and the like.

1. Name of this design product: Function setting graphical user interface for smart watch. 2. Purpose of this design product: a smart watch. 3. The key design point of this design product lies in the graphical user interface. 4. The picture or photo that best illustrates the key points of the design: main view. 5.Purpose of the graphical user interface: used to display the function setting interface. 6. The area of ​​the graphical user interface in the product: display area. 7. Human-computer interaction mode of the graphical user interface: the main view is the default interface. Swipe down in the main view to enter the interface change status diagram, where multiple function setting icons appear and the background presents a Gaussian blur effect. 8. Other situations that require explanation Other explanations: X in the interface indicates variable time.

The present application belongs to the field of terahertz imaging and image processing, and particularly relates to a terahertz image data expansion method based on a space-frequency degradation model. The method mainly comprises the following steps: pre-screening and pre-processing a high-resolution image database to establish a high-resolution image library; simulating a terahertz Gaussian beam to perform fuzzy degradation processing on the high-resolution image using a Gaussian fuzzy kernel; simulating terahertz wave source fluctuation and sensor noise to add Gaussian noise; simulating low resolution of terahertz imaging, and reducing the image resolution using a bicubic downsampling method; simulating mutual interference of multiple terahertz waves during imaging, and using a scheme of adding a frequency domain mask to change the frequency domain features; simulating the case that low image contrast is caused by low terahertz wave source power, and using a gray scale compression method to compress the gray scale histogram of the image to obtain a final low-resolution image data set. The present application can effectively expand the super-resolution training set of terahertz images, thereby improving the training effect of the terahertz image super-resolution reconstruction task based on the deep learning method.

The invention belongs to the field of flame detection, particularly relates to a flame detection method and system based on flame dynamic characteristics and electronic equipment, and aims to solve the problems of unstable detection effect and false alarm in the existing method. The method comprises the following steps: inputting a flame picture into a flame detection model to obtain a flame detection frame area; in the current frame, a flame detection frame area is cut out to serve as a first picture; cutting a flame detection frame area detected by a previous frame or a next frame corresponding to the frame as a second picture; respectively carrying out Gaussian blur processing on the grey-scale map of the first picture and the grey-scale map of the second picture to obtain a first image and a second image; respectively carrying out binary image processing on the first image and the second image based on the gray value threshold K; and judging whether flames exist in the field or not based on the image contour area after binarization image processing. And the final detection effect, the detection efficiency and the accuracy of the detection result are improved.

The present invention provides a method and system for detecting abnormalities on the wall of a metal thermos cup based on homogenization of reflective areas, relating to the field of thermos cup detection technology. The method comprises: obtaining an original image of the metal thermos cup; preprocessing the original image of the metal thermos cup to obtain an optimized thermos cup base image; calculating the image's average grayscale value and longitudinal pixel mean based on the optimized thermos cup base image; comparing the longitudinal pixel mean of each column with the average grayscale value of the entire image to calculate the difference; performing brightness correction on the original image based on the difference, and processing the brightness-corrected image using a Gaussian blur method to obtain a homogenized image; and analyzing the homogenized image to obtain detection results of abnormal areas. By integrating preprocessing, brightness correction, Gaussian blurring, and image analysis techniques, the present invention can efficiently and accurately detect abnormalities on the thermos cup wall, thereby improving the accuracy and efficiency of product quality inspection.

The application relates to a fast wideband signal detection method, device and equipment based on a time-frequency graph and a medium. The method determines the starting and ending frequencies of a wideband signal by performing Gaussian blurring, time domain averaging, gradient estimation and gradient matching on a time-frequency graph of an electromagnetic signal; verifies and scores the starting and ending frequency positions of each row on the time-frequency graph, and if the score passes, the row contains a wideband signal, otherwise, the row does not contain a wideband signal; determines whether the wideband signal is a continuous signal, and if it is not, calculates the starting and ending times of a wideband signal pulse in the time-frequency graph according to the number of rows containing the wideband signal; and obtains the characteristics of the wideband signal in the time-frequency graph according to the starting and ending frequencies of the wideband signal and the starting and ending times of the wideband signal pulse. The method proposes different feature extraction processes and detection decision mechanisms, guarantees the timeliness of detection, and provides theoretical support for fast wideband signal detection in complex environments.

This invention discloses an automatic computer protection system and method, relating to the field of computer file security protection. The technical solution includes: detecting user idle time; identifying sensitive tabs in the browser when the user is about to leave, and extracting the ORB feature point set of their content area as a content fingerprint; creating a Gaussian blur overlay window when the user has left, tracking the positional changes of the content area through feature point matching and affine transformation, and dynamically adjusting the position and size of the overlay window so that the overlay always follows the movement of the file content; and destroying the overlay window when the user returns, restoring the file to normal display. This invention, through content area tracking based on visual features and progressive blurring, solves the problem of sensitive file content being exposed and background tasks being interrupted when a user is briefly away, achieving fine-grained protection at the file and tab levels, and helping to improve the security of computer files in the office environment.

The invention discloses a current real-time measurement method and device based on tunnel magnetoresistive sensors and a medium, and belongs to the field of non-contact current sensing, and the method comprises the following steps: at least three tunnel magnetoresistive sensors are linearly arranged along the sensitivity direction of the tunnel magnetoresistive sensors to form a tunnel magnetoresistive sensor linear array; the sensor is used for collecting magnetic field intensity vectors excited by to-be-measured current; the method comprises the following steps: acquiring magnetic field intensity signals through an embedded processing unit, performing Gaussian blur processing, eliminating interference signals in an initial transient process, identifying effective wave crest positions in a stable section, performing average processing on the magnetic field intensity signals at the effective wave crest positions to obtain Hpeakavg, substituting the magnetic field intensity signals into a current solution equation, and calculating the current of the stable section according to the Hpeakavg. And calculating current intensity I through a Newton iteration method, and substituting Hpeakavg and I into a transmission coefficient K to simplify calculation. The device comprises a tunnel magnetoresistive sensor array module and an embedded processing unit, and real-time current magnitude and frequency measurement is completed in a microcontroller with limited computing resources.

The invention provides a protection pressing plate state judgment method based on OpenCV, and the method comprises the steps: A, sequentially carrying out the gray conversion, Gaussian blur contour detection, Canny edge extraction, structural element denoising and contour refinement description of a protection pressing plate opening and closing state template image, and obtaining a template refinement contour; step B, executing the processing flow of the step A on the to-be-identified protection pressing plate image to obtain a to-be-identified refined contour; and step C, calculating the 1-7-order Hu invariant moment similarity of the refined contour of the template and the refined contour to be identified, and judging the opening or closing state of the protection pressing plate based on the 1-7-order Hu invariant moment similarity and a preset threshold value. According to the invention, the problems of low efficiency and insufficient accuracy during substation secondary screen cabinet inspection are solved.

The invention provides a scatter diagram generation method based on an object contour diagram, and belongs to the technical field of graphic processing, and the method comprises the steps: contour preprocessing: carrying out the smooth denoising processing of a contour diagram, carrying out the Gaussian blurring of the contour diagram, converting the contour diagram into a binary image, extracting a contour set of a connected region in the binary image, and carrying out the Gaussian blurring of the contour image; calculating the surrounding area of each contour and carrying out noise filtering; uniform sampling and initial scatter point generation: constructing a pixel point set, and calculating a maximum distance and a minimum distance of sampling; and scatter-contour matching optimization: traversing the contour line and the scatter set, and eliminating interference scatters to obtain a final scatter diagram of the object contour. Through the methods of uniform sampling, contour point matching, repeated point de-duplication and the like, a specified number of uniformly distributed scatter points can be quickly generated for a contour map. According to the method, the defect of scatter point generation in the current method is overcome, model training is not needed, generalization is high, and the method can be widely used in profile diagrams of various graphs.

This application belongs to the field of image processing technology and discloses a method and apparatus for generating hairstyle replacement videos based on a diffusion model. The method includes: acquiring a hairstyle image dataset and training a diffusion model to obtain a hairstyle generation model; acquiring a user-inputted image of a person and a target hairstyle prompt; using skeletal keypoint detection technology to identify the person image and obtain skeletal pose data; using an image segmentation algorithm to divide the hair region in the person image to obtain a hair mask; performing smoothing and feathering processing on the hair mask using Gaussian blur and feathering algorithms; inputting the hair mask and the target hairstyle prompt into the hairstyle generation model, and using the skeletal pose data as constraints for the hairstyle generation model to obtain a hairstyle replacement image; and generating a hairstyle replacement video based on the person image and the hairstyle replacement image. This application can improve the realism and naturalness of hairstyle replacement images and videos.

The invention discloses an image deblurring method and device based on blind deconvolution, and relates to the technical field of engine turbine blade strain measurement image deblurring. In order to solve the defect that the dual requirements for image quality and processing efficiency in engine blade strain detection are difficult to meet in the prior art, the technical scheme provided by the invention comprises the following steps: acquiring images of a blade in a static state and at different rotating speeds, taking a static image as a reference, and taking a dynamic image as a to-be-processed image; initializing a blind deconvolution parameter based on a Gaussian blur kernel; performing blind deconvolution iterative optimization under the constraint of a still image, and jointly estimating a clear image and a blurred kernel to obtain a preliminary deblurred image; de-noising is carried out; and adaptively selecting a gamma value according to the histogram mean value difference between the de-noised image and the still image, and executing gamma correction to enhance the contrast and details. According to the method, a deblurring-denoising-enhancing link is formed, the image definition can be effectively recovered under the condition of high-speed rotation, and the strain detection precision and stability are remarkably improved.

This invention relates to a privacy protection method for baby care cameras based on instance segmentation detection, comprising: Step 1, region detection, using an instance segmentation detection algorithm to identify and locate the adult region, the baby region, and the background region; Step 2, background region processing, the identified background region is first reduced in size, then Gaussian blurred, and then enlarged back to its original size to obtain a Gaussian blurred background region, which is then filled back into the original background region. The privacy protection method for baby care cameras based on instance segmentation detection obtained by this invention has the following advantages: the algorithm segments the camera image into adult, baby, and background regions, providing a foundation for subsequent processing; reducing the background region saves computational power required for subsequent processing, improving processing efficiency; and by combining modern image processing technology and artificial intelligence algorithms, it achieves efficient and aesthetically pleasing privacy protection measures.