29 results about "Image averaging" patented technology

The invention discloses an SLAM front-end optimization method based on brightness grading and gradient constraint, and relates to the technical field of computer vision and vision SLAM. In order to solve the defects that adaptive grading enhancement and parameterization control based on illumination types are lacked in the prior art, and feature point quality, spatial distribution uniformity and front-end real-time performance are difficult to consider in a high-frequency input scene, the invention provides a comprehensive brightness grading and feature optimization scheme. The method comprises the following steps: firstly, calculating the average brightness of an input image, dividing the image into a dark light type, a normal type and an overexposure type, and respectively adopting gamma correction, contrast limited adaptive histogram equalization and inversion enhancement strategies for different types to realize illumination adaptive enhancement; then screening high-quality feature points with significant local curvature changes; and updating the detection area. According to the method, the feature stability, the matching precision and the real-time performance of the SLAM front end in a complex indoor environment are remarkably improved, and the method is suitable for a self-localization and mapping system.

A surface defect detection method for optically detecting a surface defect in a strip-shaped body includes an image acquisition step of detecting reflected light from the strip-shaped body obtained by illuminating a surface of the strip-shaped body and imaging while relatively scanning the surface of the strip-shaped body to acquire a plurality of images including the surface of the strip-shaped body, an average image calculation step of calculating an average image of the acquired images, an image correction step of performing shading correction on each acquired image using the average image to obtain corrected images, and a defect detection step of detecting a surface defect in the strip-shaped body based on the corrected images. The average image calculation step includes recognizing an inspection target region in which the strip-shaped body is located in each image and contributing to the average image only for pixels in the inspection target region.

A processing method of a display screen brightness compensation includes: obtaining a sample data set of the display screen, a real environment temperature and a real image average grayscale value corresponding to the real environment temperature, the sample data set including an environment temperature sample set, a image average grayscale sample set corresponding to the environment temperature sample set and a simulated temperature sample set; performing a linear regression processing on the sample data set to generate a regression coefficient set; processing the real environment temperature and the real image average grayscale value according to the regression coefficient set to generate a temperature to be compensated of the display screen; and determining a brightness compensation parameter according to the temperature to be compensated, and performing a brightness compensation processing on the display screen according to the brightness compensation parameter.

This invention belongs to the field of image processing technology and discloses a method, system, and terminal for continuous image target detection considering relative target motion. It utilizes an image target detector to detect targets in an image at time k, obtaining the corresponding detection result Y. k ; Regarding the detector's detection result Y k Y k Temporal matching is performed with the detection results obtained by the algorithm at the previous time step; based on the average brightness index g of the image at time k. k Estimate the reliability parameter p of the test results k Calculate the noise matrix R(k) and the predicted bounding box size b″ of the detection results at time k, respectively. k The Kalman filter is applied to the position of the object at time k, and the bounding box size of the object at time k is estimated by weighted fusion to form the result of image detection at time k. This invention can estimate the motion parameters and size variation patterns of targets in images based on target detection results in continuous images, providing effective data support for subsequent target localization and actual motion state estimation.

This invention provides a scene-adaptive fast automatic exposure adjustment method, comprising: S1. Acquiring a frame image and the exposure parameters of the previous frame, denoted as E_pre; S2. Calculating the average brightness Y_cur, the proportion of overexposed pixels P_sat, and the proportion of underexposed pixels P_dark of the current frame image using the current frame image; S3. Calculating the slope between the target brightness and the average brightness of the current frame, denoted as k1: k1=Y_target / Y_cur; S4. Determining whether the current brightness meets the expectation. If k1_th_dark≤k1≤k1_th_sat, where k1_th_dark is the lower limit of the k1 tolerance threshold and k1_th_sat is the upper limit of the k1 tolerance threshold, the exposure parameters remain unchanged, and the next loop is performed, returning to step S1; otherwise, if the expectation is not met, the exposure parameters are updated and the process proceeds to step S5; S5. Selecting the corresponding convergence parameters; S6. Calculating and acquiring the exposure parameters, and updating the exposure parameters. This method can quickly achieve automatic exposure control in bright, dark, or scenes with drastic brightness changes, allowing the image to converge to a reasonable brightness quickly.

The application discloses a method for judging and eliminating ground light in high-resolution images, and belongs to the field of space visible light detection, and the steps are as follows: firstly, the input visible light image is evenly divided into four areas according to rows, and the brightness sum of each area is calculated; secondly, a ground light judging factor is calculated, and if the ground light judging factor is greater than 0, it is indicated that the ground light exists; and then, the brightness of the image with the ground light is balanced according to rows, so that the purpose of eliminating the ground light is achieved.

The application discloses a method for solving color deviation of a three-color Mini LED backlight liquid crystal display device and a liquid crystal display device, and relates to the technical field of liquid crystal display, and comprises the following steps: adjusting RGB backlight currents of a white field, recording a set of RGB backlight current ratios when color coordinates of the white color reach design target values; adjusting RGB backlight currents of red, green and blue single-color fields respectively, recording a second set of RGB backlight current ratios, so that the RGB synthesis obtains the design target values, and obtaining coordinate points W, R, G and B of the white color, the red color, the green color and the blue color on a CIE chromaticity diagram respectively; performing segmentation processing on a to-be-displayed image, counting average image color coordinates of each block as to-be-displayed colors, obtaining a coordinate point C of the to-be-displayed colors on the CIE chromaticity diagram, and calculating RGB current values of backlight of the block according to relative positions of C, R, G, B and W and the two sets of RGB backlight current ratios. The application can guarantee a color gamut and solve the color deviation phenomenon.

The invention provides an image brightness adjusting method and device, electronic equipment and a storage medium, and relates to the technical field of image processing, and the method comprises the steps: obtaining an input image, and determining the image average brightness of the input image; when it is detected that the image average brightness is smaller than a brightness lower limit value, the external light intensity of the input image is determined; and performing gamma correction on the image brightness of the input image under the condition that the external light intensity is smaller than an external light intensity threshold value. According to the invention, the brightness characteristic of the image and the external light environment factor are effectively combined, so that the targeted brightness adjustment is realized, and the picture distortion caused by invalid operation and improper adjustment in a scene without adjustment is avoided.

The application provides a two-dimensional grating preparation method and a diffractive grating waveguide device, and the two-dimensional grating preparation method comprises the following steps: determining a setting scheme of two grating vectors of a two-dimensional grating based on two known first basic grating vectors and second basic grating vectors of the two-dimensional grating; determining grating design optimization parameters of the two-dimensional grating, including a grating period and a grating line direction, according to the setting scheme of the two grating vectors of the two-dimensional grating; and preparing the two-dimensional grating according to the grating design optimization parameters of the two-dimensional grating. The two-dimensional grating preparation method provided by the application is helpful to realize the balance between the coupling-out image energy efficiency and the average brightness uniformity of the coupling-out image of the two-dimensional grating, and improve the degree of freedom of the two-dimensional grating preparation process.

The invention relates to an automatic exposure method for internal imaging of an aluminum casting hole in an industrial quality inspection scene in the technical field of industrial imaging. The automatic exposure method comprises the following steps: determining the maximum exposure time corresponding to an image average gray value meeting a detection requirement; determining an exposure time sampling interval; and performing interpolation polynomial fitting according to the maximum exposure time and the exposure time sampling interval. Compared with a conventional mode of determining exposure time in industrial AOI quality inspection, the method can save a lot of manpower work; the detection step length and the exposure time are decoupled, so that the method is more flexible; the method can adapt to the influence of the inner wall roughness and the bottom roughness caused by the abrasion of a machining tool on imaging to a certain extent.

The invention relates to a nondestructive testing technology of electrical equipment, and discloses an infrared detection device for cracks of an electrical tube, which comprises a support plate, a closed detection camera obscura, a rotary moving mechanism, an infrared imaging unit and an active infrared lighting unit, and is also provided with an infrared light source intensity real-time adjusting system, the system integrates an environment state evaluation module, a system state evaluation module, an adaptation evaluation module and a motion state evaluation module, and can sense multi-dimensional parameters such as environment light intensity, air suspended matter concentration, light source working temperature, optical window transmissivity, production line operation speed and pipeline rotation angular velocity in real time; and the infrared illumination intensity is dynamically adjusted by integrating the average gray scale of the image and the target feature contrast ratio. According to the invention, intelligent self-adaptive control of the infrared light source in a complex and changeable production environment is realized, the image quality stability and the accuracy and reliability of crack detection are effectively improved, and the method is suitable for full-automatic online nondestructive detection in large-scale production of power tubes.

The present application relates to the technical field of optical coherence tomography, and discloses a retinal OCT full-automatic imaging and image enhancement method, which comprises the following steps: automatically positioning the three-dimensional space position of the pupil through a binocular vision system and aligning an imaging light beam; cooperatively determining an optimal refractive compensation value based on the joint evaluation of the interference signal intensity and the axial registration stability of a short sequence image; collecting multiple B-scan images, performing motion correction, removing false frames and image averaging to generate a final image; performing quality evaluation on the final image, and adaptively retriggering the acquisition process or re-focusing according to the evaluation result. The present application realizes the automation of the whole imaging process by introducing cooperative refractive compensation and closed-loop feedback control, enhances the anti-interference ability to the motion of the measured person, reduces the dependence on the experience of the operator, and can stably obtain high signal-to-noise ratio retinal tomographic images.

This invention discloses a two-photon microscopy imaging method and apparatus for simultaneous multi-region aberration correction, belonging to the field of microscopic imaging. The apparatus includes an aberration control module, a multifocal generation module, a scanning imaging module, an imaging objective, a fluorescence collection module, and an aberration correction module. The aberration correction module tests multiple aberration modes and fits the amplitude of the selected aberration mode based on the functional relationship between the average image intensity and the amplitude of the aberration mode. Finally, a corresponding compensation phase is applied to the phase modulator to achieve simultaneous multi-region aberration correction. This apparatus, through multifocal scanning and multifocal aberration control, achieves simultaneous aberration correction in multiple imaging regions, improving the speed of aberration correction and realizing aberration correction for large field-of-view dynamic signals.

The invention discloses an anti-counterfeiting code image scatter reflection detection method based on top-hat operation. The method comprises the following steps: 1) obtaining an original anti-counterfeiting code image sample through image acquisition equipment; 2) positioning and extracting a target image anti-counterfeiting code area in the sample, and carrying out image preprocessing; 3) binarizing the target image and performing top-hat operation, and extracting astigmatism points in the image; 4) calculating the image average gradient of the image, and judging whether the image is scatter reflection; and calculating the average gradient of the scatter result image of the top-hat operation, comparing the average gradient with a set gradient threshold value, and judging that the corresponding scatter point is scatter reflection if the average gradient exceeds the threshold value. According to the method disclosed by the invention, when scattered light reflection occurs in the image, the scattered light reflection can be effectively and accurately detected, the replacement of a shooting anti-counterfeiting code can be prompted, the false alarm phenomenon of anti-counterfeiting detection caused by blurring is reduced, the stability of an anti-counterfeiting detection algorithm is improved, and the robustness of identification is greatly improved.

This invention provides a method and system for evaluating the smoke extraction effect of a range hood, relating to the technical field of equipment detection. The method includes: after the range hood starts operating, acquiring a target image set, wherein the target image set includes instantaneous images captured by a camera within a preset time period and under conditions free from stray light interference at a preset frequency; the instantaneous images are images captured when a parallel light source of preset thickness and wavelength illuminates the oil fume thermal plume generated by the smoke-generating device; converting the instantaneous images into grayscale images; processing the grayscale images using an image averaging algorithm and an image standard deviation algorithm to determine the probability density map of the oil fume concentration distribution and the oil fume boundary distribution map of the oil fume thermal plume; and determining the smoke extraction effect of the range hood based on the probability density map of the oil fume concentration distribution and the oil fume boundary distribution map of the oil fume thermal plume, thus solving the technical problem of poor accuracy in existing range hood smoke extraction effect evaluation results.

The invention relates to the technical field of ferrous metallurgy, and discloses a vision and pressure fused intelligent detection method for the depth of an RH dip pipe inserted into molten steel. The method comprises the following steps: when an RH vacuum tank is vacuumized, starting a built-in camera to collect and preprocess a tank bottom image signal to obtain an RH tank bottom preprocessed image; extracting an average pixel gray value and a local maximum brightness gradient of each frame of image as key features; constructing a two-dimensional feature image based on the key features, constructing a model by using an artificial intelligence algorithm, and determining the moment when the molten steel overlays the bottom of the RH vacuum tank; and reading the vacuum pressure at the moment, and calculating the depth of the dip pipe inserted into the molten steel by combining the preset length of the dip pipe, the molten steel density, the slag density and the slag thickness. Through visual feature extraction and vacuum pressure data fusion, the depth of the dip pipe inserted into the molten steel in the RH production process is accurately calculated in cooperation with the neural network model, and the problem that depth detection is inaccurate due to the fact that traditional detection is prone to being disturbed by the molten steel and slag is solved.

The invention provides a highlight target identification method based on a dung beetle optimization algorithm and an adaptive threshold segmentation algorithm, and the method comprises the steps: carrying out the Gaussian filtering of continuous frames of active and passive images to remove a part of noise, and carrying out the morphological opening operation to reduce the noise and maintain the feature size of a target; then registering the images, eliminating the image average gray difference of the active image and the passive image caused by the influence of active light illumination by using image difference operation, and preliminarily extracting a possible target area; then optimizing the maximum between-cluster variance by using a dung beetle optimization algorithm to determine a difference image segmentation threshold value, binarizing the difference image, and further extracting a target candidate region; for the candidate target area, the highlight target is identified by adopting comprehensive shape measurement of circularity and eccentricity, and the method has higher detection accuracy; the efficient search mechanism of the dung beetle optimization algorithm meets the real-time requirement, and the processing speed is higher; the method is suitable for actual scenes with uneven illumination and complex noise, and has higher robustness.

The invention relates to the technical field of microscope focusing, and discloses an external microscope automatic focus following method and system, a medium and a program product, and the method comprises the steps: controlling an electromechanical actuator to move in a first direction according to a preset coarse scanning step length; when the electromechanical actuator moves by a preset coarse scanning step length, controlling the image capturing device to capture a first microscope image of the microscope, and receiving the first microscope image sent by the image capturing device; calculating a focus evaluation value based on the first microscope image; based on the focus evaluation value, controlling an electromechanical actuator to control a microscope to perform automatic focusing, and determining an optimal focus position; and based on the optimal focus position, the microscope is controlled to perform continuous focus following by using the correlation between the image average gray value and the focus position, so that the microscope is focused to the optimal focus position. Therefore, by implementing the method, intelligent automatic focus following is realized on the premise that expensive transformation and modification of internal control software are not carried out.

This invention discloses a method for detecting bad columns in an image sensor, comprising the following steps: Step 1: Calculate the average value imgMean of the image; Step 2: Calculate the column mean colMeans and column standard deviation colStds of the image; Step 3: Select any column of the image as a reference column and calculate the reference column standard deviation refStd; Step 4: Based on the average value imgMean calculated in Step 1, calculate the similarity value between each column in the image and the reference column individually. The larger the similarity value, the lower the similarity; Step 5: Traverse all columns of the image and calculate the similarity value between all columns and the reference column according to Step 4. The beneficial effects of this invention are: By combining the absolute grayscale mean of the image itself, different absolute grayscale means correspond to different bad column judgment thresholds. By selecting a fixed column in the image as a reference column and combining the two threshold factors of mean and standard deviation, bad columns in the image can be detected accurately and efficiently with low false negative and false positive rates.

A processing method for brightness compensation of a display screen, comprising: acquiring a sample data set, a real ambient temperature, and a corresponding real image average grayscale value of a display screen, the sample data set comprising an ambient temperature sample set, a corresponding image average grayscale sample set, and a simulated temperature sample set (S100); performing linear regression processing on the sample data set to generate a regression coefficient set (S200); processing the real ambient temperature and the real image average grayscale value on the basis of the regression coefficient set to generate a temperature to be compensated for the display screen (S300); and determining a brightness compensation parameter on the basis of the temperature to be compensated, and performing brightness compensation processing on the display screen on the basis of the brightness compensation parameter (S400). By means of the processing method and system for brightness compensation of a display screen, a device, and a medium, the accuracy of brightness compensation of the display screen can be improved.

The invention discloses a color image low-light enhancement method for satellite area array remote sensing, which comprises the following steps of: firstly, carrying out brightening preprocessing on pixels of a plurality of shot color images, then averagely grouping the preprocessed images, taking a first shot image as a reference image in each group, and taking a second shot image as a reference image in each group; the gray centroid values of a reference image and adjacent images are compared, then the reference image is updated in the mode that the pixel values of the images with the small difference values between the reference image and the gray centroid values are added, all the images in the group are traversed, multi-layer image superposition is achieved, and each group obtains clear and visible color images with the enhanced brightness. And then, adjusting the red, green and blue colors of each group of obtained color images, and then overlapping again to obtain a high-brightness color image. The method is simple in algorithm, does not lose the image resolution, and is suitable for earth imaging under long exposure time or severe conditions of a remote sensing camera of a satellite based on an area array detector.

A target positioning method, device, storage medium and terminal, the method comprises the following steps: acquiring a plurality of color images obtained by shooting a to-be-positioned feature point at different positions and a depth image corresponding to each color image; performing error screening on the plurality of color images to obtain a plurality of target images; extracting a depth value of the to-be-positioned feature point in a depth image corresponding to each target image, and projecting and converting the depth value based on a selected image in the plurality of target images to obtain a plurality of target depth values; calculating an average depth value of the plurality of target depth values, and determining a coordinate value of the to-be-positioned feature point in the selected image; and positioning the to-be-positioned feature point according to the selected image, the average depth value and the coordinate value. The method can effectively improve the positioning efficiency of positioning the feature point, and further improve the positioning efficiency of positioning the object itself.

The invention provides an image brightness adjusting method and device, electronic equipment and a medium, and relates to the technical field of image processing, and the method comprises the steps: obtaining an input image, and determining the image average brightness of the input image; when it is detected that the average brightness of the image is smaller than the brightness lower limit value, the texture regularity feature and the marginal definition feature of the input image are determined; and determining an image brightness adjustment strategy of the input image according to the texture regularity feature and the marginal definition feature, and performing image brightness adjustment on the input image according to the image brightness adjustment strategy. According to the method, the features of the two dimensions are combined and analyzed, so that the reason that the average brightness of the image is relatively low can be effectively distinguished, an image brightness adjustment strategy can be formulated in a targeted manner, image brightness adjustment of different low-brightness scenes is realized, and wrong brightness adjustment is effectively avoided.

The invention discloses a block unloading method and system for a super-resolution task in edge computing. The method comprises the following steps: S1, averagely dividing an image to be subjected to super-resolution processing into a plurality of blocks; s2, calculating the mPV value of each image block, and sorting all the image blocks in a descending order according to the result of the mPV values; s3, calculating to obtain a time relationship between each image block and the edge equipment; s4, on the basis of the sorting result obtained in the S2 and the time relation obtained in the S3, calculating the total delay of executing the super-resolution task by the edge device after each image block is unloaded to each edge device in sequence, selecting the edge device with the minimum total delay, and unloading the image block to the edge device; and S5, calculating the total delay of the image super-resolution task, wherein the total delay of the image super-resolution task is the longest delay for completing all the super-resolution tasks in all the edge devices obtained after the step S4. According to the method, the execution efficiency of the super-resolution task in the edge computing environment is effectively optimized.

The invention discloses a multi-sensor fusion portable intelligent digital photo frame and a control method thereof, and belongs to the technical field of intelligent display and adaptive control. The method comprises the following steps: firstly, constructing an environment quality model through environment light color temperature, uniformity and infrared intensity, and obtaining an environment quality coefficient; constructing an equipment constraint model in combination with the screen temperature and the battery capacity, and obtaining an equipment constraint coefficient; and constructing a content optimization model by using the image highlight proportion, the backlight current and the screen opening duration, and obtaining a content optimization coefficient. And then, based on the environment quality coefficient and the equipment constraint coefficient, carrying out multi-dimensional balance adaptation calculation on the environment illuminance, the image average brightness and the viewing angle to obtain a comprehensive adaptation degree. And finally, fusing the adaptation degree, the content optimization coefficient and the basic brightness, and calculating the final target display brightness through a brightness optimization model. According to the invention, the self-adaptive intelligent adjustment of the display brightness is realized, and the visual comfort, the content presentation effect and the equipment energy efficiency are effectively considered.

This invention discloses an adaptive FPGA license plate recognition system and anti-interference method for severe weather, belonging to the field of embedded digital image processing technology. To address the technical shortcomings of traditional FPGA license plate recognition systems, such as fixed image processing parameters, low recognition accuracy in severe weather, and reliance on external weather sensors, this invention sets up a weather feature discrimination module and an adaptive anti-interference preprocessing module. The weather feature discrimination module constructs an 8×8 pixel hardware sliding sampling window based on a shift register, extracting four types of features in parallel using pure hardware: average image brightness, gray-level squared cumulative dispersion, noise quantity, and highlight pixel ratio. It also distinguishes character edges from noise by combining gradient direction, and outputs a 3-bit binary weather code through a four-channel hardware comparator, achieving automatic judgment of sensorless weather conditions. The adaptive anti-interference preprocessing module integrates four mutually exclusive hardware circuits: a fog enhancement circuit, a rain filtering circuit, a strong light suppression circuit, and a nighttime noise reduction and brightness enhancement circuit. It adaptively switches the corresponding anti-interference processing link according to the weather code and uses clock gating technology to shut down idle circuits to reduce dynamic power consumption. This invention uses addition, subtraction, and shift operations to implement hardware logic, avoiding floating-point operations. It is compatible with low-end and mid-range FPGA chips, with a single-frame image processing latency of ≤6ms. Under harsh working conditions, the license plate recognition accuracy can reach over 95%, making it suitable for outdoor traffic checkpoints and embedded capture devices.

The invention discloses a robust feature fused low-illumination dynamic vision SLAM method and system. The method comprises the steps of performing fast non-local mean denoising on an input low-illumination original RGB image; then adaptively and sequentially applying a Zero-DCE network to carry out global brightness enhancement and CLAHE (contrast limited adaptive histogram equalization) to carry out local detail enhancement according to the average brightness of the image; on the basis, key points are extracted by adopting a self-adaptive FAST corner detection algorithm for dynamically adjusting a threshold value based on an image gray average value; identifying and removing feature points in dynamic areas such as pedestrians and vehicles by using a YOLOv8-Seg semantic segmentation model; and finally, the original ORB descriptor is upgraded into an enhanced descriptor with high robustness through a FeatureBooster deep learning module. According to the method, the stability and accuracy of feature extraction and matching under low illumination, motion blur and dynamic interference can be remarkably improved, and then the pose estimation precision and map construction integrity of an SLAM system are improved.

The application discloses a high-reflection high-transparency material surface flaw detection method based on improved YOLOv11, which comprises the following steps: S1, collecting mobile phone screen surface defect images, preprocessing and labeling, and generating a training data set containing three types of defects, i.e., scratches, edge collapse and cracks; S2, constructing a flaw detection model; S3, dividing the input image into four independent detection regions, using a boundary box overlap area weight distribution strategy for cross-region defects, and using the flaw detection model to count the image by region; S4, constructing a surface defect detection network meeting the real-time demand of an industrial production line, and outputting the detection result and processing frame rate; S5, performing pixel-level mask segmentation on the scratch defect, and extracting the contour coordinates and geometric features; and S6, fitting the actual damage size of the scratch defect on the surface of the high-reflection high-transparency material according to the segmentation result. The application has a faster detection speed under the condition of ensuring the accuracy, and provides a reliable solution for high-reflection high-transparency material surface flaw detection.

The invention belongs to the technical field of digital image watermarking, and provides a robust watermarking method based on multi-scale attention to solve the problem that an existing deep learning digital image robust watermarking scheme is insufficient in performance under attacks of JPEG, cutting and the like. An encoder-noise layer-decoder structure is adopted, and the encoder realizes feature extraction and cross-group fusion by designing an MSCAF module; meanwhile, constructing a space attention mask layer to guide the watermark to adaptively adjust the embedding strength according to the image features; the decoder uses the MSCAF to extract the multi-scale features so as to improve the watermark extraction precision. Experimental results show that under JPEG attack training of different quality factors, the PSNR and the watermark extraction error rate of the coded image are both superior to those of an existing comparison algorithm; under combined noise attack training, the average peak signal-to-noise ratio of a coded image generated by the method reaches 38.6738 dB, the watermark extraction error rate is lower than 0.6%, and the method is superior to an existing comparison algorithm.