20results about How to "Improve image processing efficiency" patented technology

The invention discloses a high-speed photographic apparatus image data processing method and system, and the method comprises the steps: obtaining collected high-speed photographic apparatus image information, and carrying out the preprocessing of the high-speed photographic apparatus image information; combining the preprocessed image information of the high-speed photographic apparatus with a pre-constructed messy code recognition model to obtain a recognition result for converting the image into a messy code document; and performing corresponding image processing and / or reacquisition processing on the image information of the high-speed photographic apparatus according to the identification result. According to the method, the image information which cannot be successfully converted into the document can be efficiently and comprehensively identified and processed, and the image processing efficiency is improved.

The invention discloses an image processing method and device, a storage medium and an electronic device. The method comprises the steps of generating a first noise map, a first color map, a second color map and a target symbol distance field map through a preset programmed noise processing mode; generating an initial vegetation image by using the first noise map, the first color map and the target symbol distance field map; generating a first mask image based on the initial vegetation image; and performing hybrid processing on the first color map and the second color map according to the first mask map to generate a target vegetation image. According to the invention, the technical problems of low processing efficiency and huge computing resource consumption of an image processing mode provided in related technologies are solved.

The present application discloses an image threshold segmentation method, system, device and storage medium for display screen defect detection, which realizes efficient, accurate and robust defect detection, helps to improve the image segmentation effect, and can accurately segment the defective area of ​​the image, so as to facilitate the analysis of the defective image. The method of the present application includes: acquiring the initial image displayed on the display screen by a camera; downsampling the initial image to obtain a downsampled image, which is a partial image of the initial image; expanding and filling the edge of the downsampled image to obtain an extended image; pre-processing and filtering the extended image to obtain a filtered image; dynamically generating a selection window according to a window selection strategy, and selecting a target image on the filtered image through the selection window.

The invention discloses an endoscope image display optimization method, system and device and a storage medium. The method comprises the following steps: splitting an original endoscope image acquired in real time into a first image signal and a second image signal which are synchronously transmitted in real time; receiving the first image signal in real time by using the FPGA controller, and processing the first image signal in real time to obtain a first processed image; synchronously utilizing the ARM processor to receive a second image signal in real time, and performing real-time processing on the second image signal to obtain a second processed image; and receiving the second processed image in real time by using the FPGA controller, fusing the first processed image and the second processed image in real time, generating a fused image, and displaying the fused image. According to the invention, the accuracy and reliability of image fusion are ensured, the delay time of image display is effectively controlled, the image display effect is optimized, and a clearer, more accurate and more real-time operation view can be provided for doctors.

The present application belongs to the field of image processing technology, and discloses a lightweight complex scene image fusion model and a real-time image fusion method, which utilize the complementary capabilities of two modal images in the frequency domain to guide network learning, and can extract key feature information even in the case of severe pixel interference. At the same time, spatial domain branches are used to improve local detail resolution and solve the potential omission problem inherent in frequency domain analysis, so that significant pixel information of different modal images can be effectively captured under the interference of complex factors, thereby improving the quality of the fused image; in addition, the model has a simple structure, high computational efficiency, and high image processing efficiency.

The invention provides a multi-mode large model driven image color changing method and system and a medium, and the method comprises the steps: obtaining to-be-processed vehicle image information, and carrying out the preprocessing of the vehicle image information, and the preprocessing comprises the adjustment of the size proportion, background color and image quality of the vehicle image information; based on the preprocessed vehicle image information, using a skeleton extraction model to obtain a vehicle skeleton image, the vehicle skeleton image being in a line draft form; calling a preset image generation model, inputting a vehicle skeleton image and auxiliary processing information, and generating a corresponding target vehicle image by the preset image generation model according to the vehicle skeleton image and the auxiliary processing information; the auxiliary processing information at least comprises rendering color information and image quality information. According to the method, multiple deep learning technologies are fully combined, and the image processing efficiency is improved.

This specification provides an image processing method and device, wherein the image processing method includes: receiving an image style transfer instruction for an image to be processed, wherein the image style transfer instruction includes an image identifier of the image to be processed and a template identifier of at least one style image template; based on the image identifier and the template identifier, obtaining the image to be processed, a target grayscale image obtained by pre-processing the image to be processed, and a template pixel value corresponding to the at least one style image template, wherein the target grayscale image is used to characterize the degree of influence of the at least one style image template on the image style of the image to be processed; and sending the image to be processed, the target grayscale image, and the template pixel values ​​corresponding to the at least one style image template to a client. This solution can reduce the storage space occupied by the client, thereby avoiding affecting the normal operation of the client.

The application provides a model training method, an image segmentation method, an electronic device and a storage medium. The method comprises: acquiring a sample medical image, inputting the sample medical image into a first segmentation network to perform first stage segmentation, and obtaining a first segmentation result of the sample medical image for a region of interest; inputting the first segmentation result and the sample medical image into a second segmentation network respectively to perform second stage segmentation, and obtaining a second segmentation result of at least one sub-region of the region of interest of the sample medical image; updating parameters of the first segmentation network and updating parameters of the second segmentation network; and constructing an image segmentation model based on the updated first segmentation network and second segmentation network. The application automatically provides a partition positioning result of a medical image, thereby reducing the workload of doctors.

The application belongs to the technical field of image processing, and discloses an image processing method, device, electronic equipment and storage medium. The method comprises the following steps: performing data padding on target feature map data according to a current convolution kernel column index, the target feature map data being selected from a target image and being stored in a data register; performing data offset processing on the padded target feature map data according to the convolution kernel column index, to obtain offset target feature map data; and performing operation based on convolution kernel data and the offset target feature map data, to obtain a feature map processing result. In this way, the operation is performed in the data offset manner, the number of data reading and writing is reduced, and the image processing efficiency is improved.

The invention discloses a visual navigation device for artificial intelligence assisted percutaneous endoscopic lumbar intervertebral disc resection, which belongs to the technical field of medical surgical navigation and comprises an image acquisition and processing module, an intelligent navigation algorithm module and a visual interaction interface module which are in signal connection in sequence, the image acquisition and processing module is used for acquiring medical image original data of a lumbar vertebra part of a patient, outputting segmented anatomical structure data, cooperatively acquiring image data through double devices, realizing accurate image segmentation and three-dimensional reconstruction in combination with a convolutional neural network algorithm, and ensuring the presentation accuracy of an anatomical structure; according to the system, the optimal operation path planning and real-time instrument tracking functions are achieved, the operation path deviation is effectively avoided, the injury risk is reduced, partition display and color identification technologies are adopted in a visual interaction interface, the system is visual and clear, multimode human-computer interaction is supported, the operation process is simplified, the learning and operation time of doctors is shortened, and the operation efficiency is improved.

The invention relates to the technical field of image processing, and discloses a plane segmentation method and device for a three-dimensional rendering gradient multi-order plane, and the method comprises the steps: carrying out the gray conversion of a target image, and carrying out the three-dimensional projection and pixel point grouping of a first image processing result obtained through the gray conversion according to a pixel grouping condition, obtaining a second image processing result comprising a plurality of triangular surfaces; performing third image processing on the second image processing result by determining the vertex of each triangular surface, determining the intersection point between the plane where each triangular surface is located and the preset cross section and connecting the intersection points to obtain a third image processing result; and performing segmentation processing, classification processing and gradient color rendering on a non-triangular part in the third image processing result according to a pixel grouping condition to obtain a fourth image processing result including the target rendered image. Therefore, by implementing the method, the calculation data volume of three-dimensional rendering can be reduced, and the calculation efficiency of three-dimensional rendering can be improved.

The invention provides an image processing 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 the state change information of a crown block of entertainment equipment during a video recording period; the entertainment equipment is equipment for obtaining prizes by controlling a crown block, and the video is shot by a camera arranged in a gift cabin of the equipment towards one side of a user; if it is determined that the prize in the entertainment equipment is clamped based on the state change information and it is determined that the face of the user is not shielded when the prize falls based on the state change information, obtaining a video clip corresponding to the prize in a half-air state; and performing image enhancement on the plurality of picture frames in the video clip. According to the method, image enhancement processing is performed on the video clip at the critical moment (namely the prize falling moment), so that consumption of computing resources and computing power cost are reduced, and meanwhile, image processing efficiency and resource utilization rate are improved.

The application provides an image processing method and system, electronic equipment and storage medium, and relates to the technical field of automobile electronics. A lightweight model and a low-latency cache for executing an AI algorithm are configured in an ISP system, a communication channel between the low-latency cache and each module in the ISP system is established through a high-speed read-write interface, in the process of image processing, each module reads corresponding data from the cache, and cooperates with the lightweight model to complete corresponding AI operation processing to complete image processing, and finally obtained image data is output. That is, an image processing system based on AI-ISP and low-latency cache is constructed, and in image processing, the AI algorithm executed by the lightweight model is embedded in any position in the image processing process to execute one or more corresponding AI algorithms. Not only can the purpose of adapting to complex scenes be achieved, but also the purposes of improving image processing efficiency and accuracy can be achieved.

The present invention relates to the technical field of pipeline anti-corrosion, and specifically to an anti-corrosion optimization method for a gas pipeline. After the gas pipeline is installed, the reference image data of the gas pipeline is first read and the reference image data of the gas pipeline is saved. After the gas pipeline is put into operation for a preset time period, the actual image data of the gas pipeline is compared with the reference image data. If the comparison result between the actual image data and the reference image data conforms to a first type, the position conforming to the first type is marked as a first danger zone. At this time, a first processing program is used to process the first danger zone of the gas pipeline to prevent the soil from covering up the corrosion defects of the gas pipeline. Under the action of the first processing program, the first danger zone is clarified, thereby improving the accuracy of image detection, and then realizing accurate optimization of the gas pipeline.

The present invention provides an image processing method, an image processing device and a detection device. Based on the present invention, a detection mask image can be created using a detection area determined by regional calibration information of ROI and ROS. Since the detection area is defined in the non-overlapping area of ​​ROI and ROS, the detection mask image created using the detection area can have the effect of using ROS to partially shield the ROI. Thus, the detection mask image whose effective area is constrained in the detection area is used to perform regional validity filtering on the target image, and only the original image value in the local area of ​​the target image that is enclosed in the ROI and not shielded by the ROS can be retained, thereby eliminating internal interference and redundant image processing in the ROS, which helps to improve the image processing recognition accuracy and image processing efficiency of the target image. Moreover, the regional calibration information of ROI and ROS can be used to perform posture compensation with reference to the reference features, so that the targeted positioning of the local shielding effect in the target image can be more accurately achieved.

The invention discloses a method and a system for automatically cleaning dust removal equipment based on machine vision, and relates to the technical field of automatic cleaning. The method comprises the steps that all pollution sources in a to-be-cleaned area are obtained, and pollution point locations are marked; a three-dimensional coordinate system with the dust removal equipment as the center is established, the pollution point positions are converted into the coordinate system of the dust removal equipment in the to-be-cleaned area, and stain coordinates are obtained; marking cleaning coordinates corresponding to the stain coordinates according to the operation range of the dust removal equipment; the method comprises the following steps: acquiring an image shot by a camera of the dust removal equipment, extracting stain coordinates in the image, and if it is obtained through image analysis that pollutants exist at the stain coordinates, driving the corresponding dust removal equipment to reach the cleaning coordinates corresponding to the stain coordinates, and executing dust removal operation. According to the control method, fixed-point cleaning of the dust removal equipment is achieved, the labor cost is reduced, and the cleaning efficiency is improved.

The application relates to the technical field of image processing, can be applied to business scenes such as financial technology and medical health, and discloses an image processing method and device based on a task identifier, equipment and a medium, which comprises the following steps: acquiring original image data and converting the original image data into a preset image format to generate standardized image data; binding the standardized image data with a task identifier to form composite image data; selecting a perception module from a task library according to the task identifier, processing the composite image data to generate first intermediate feature data; inputting the first intermediate feature data into an image processing core network to generate second intermediate feature data; and selecting an output module according to the task identifier to process the second intermediate feature data to generate target image data. Through the module selection mechanism driven by the task identifier, the application realizes automatic adaptation of various image processing tasks in a unified image processing process, and improves the task configuration flexibility and image processing efficiency.

The invention relates to the technical field of graphic design, and discloses a graphic design graphic image processing device which comprises a display panel, a groove is formed in the bottom of the display panel, rubber blocks are fixedly connected to the four corners of the display panel, the rubber blocks are in an arc shape, and a touch pen is arranged on the back face of the display panel. In the moving process of the push block, the push block is far away from the placement groove formed in the surface of the fixed block, so that the push block relieves limitation on the balls, the touch pen is pulled out from the interior of the fixed block, the touch pen pushes the two balls making contact with the touch pen when moving in the fixed block, the two balls slide in the placement groove, and therefore fixation of the touch pen is relieved; the touch pen is stored and fixed, so that the touch pen can be effectively prevented from being damaged due to accidental falling or collision, the required touch pen can be quickly found and taken, the time spent on finding the touch pen is shortened, and the image processing efficiency is improved.

The invention relates to the technical field of image processing and computer vision, and discloses a ZYNQ-based infrared image super-resolution system and device, and the system comprises an infrared image collection module, a ZYNQ infrared image processing module which comprises an ARM processor and an FPGA hardware acceleration module, a high-resolution infrared image output module, and a high-resolution infrared image display module. The infrared image acquisition module acquires a low-resolution infrared image through an infrared camera, image data acquired by the infrared camera is transmitted to the ZYNQ infrared image processing module through a digital interface, and an image processing algorithm in the ZYNQ infrared image processing module begins to perform super-resolution processing on the image data; and the processed high-resolution image data is sent to the high-resolution infrared image output module through the digital interface, is converted into HDMI through the VGA interface, and is output to the high-resolution infrared image display module. Through the optimized super-resolution algorithm, image details are effectively recovered, and the image resolution is improved.

The present invention provides an image processing method, device, electronic device and computer-readable medium, including: extracting shallow features of a target object in an image to be processed to obtain a shallow feature map of the target object; obtaining a first instance segmentation feature map obtained when an Anchor Based instance segmentation method is used to perform instance segmentation on the target object in the image to be processed; performing a guided filtering operation on the first instance segmentation feature map based on the shallow feature map of the target object to obtain a second instance segmentation feature map; and determining a mask of the target object in the image to be processed based on the first instance segmentation feature map and the second instance segmentation feature map. The second instance segmentation feature map of the present invention carries edge information of the target object, and the edge effect of the mask of the target object in the image to be processed is finally determined to be better, thereby improving the performance and accuracy of instance segmentation, and the method of the present invention has a small amount of calculation and high image processing efficiency.