45 results about "Super resolution algorithm" patented technology

The invention discloses a building design drawing generation method and system based on a stable diffusion model, and the method comprises the steps: obtaining image materials of different building facades, and carrying out the screening and marking of the image materials, and obtaining a data set; a stable diffusion model based on Control-Net guidance is adopted to train and finely adjust the data set, and a building image generation model is obtained; inputting the sketch and the cue word into a building image generation model, and outputting a building design drawing; and rendering and enhancing the architectural design drawing by adopting an image super-resolution algorithm based on the generative adversarial network. According to the method, building facade image materials are acquired, a data set is constructed, stable diffusion model training guided by Control-Net is combined, so that rapid generation from a sketch to a building design drawing is realized, meanwhile, a generative adversarial network is introduced to render and enhance the generated building design drawing, the definition and detail performance of the image are remarkably improved, and the image quality is improved. And a high-fidelity visual effect picture is quickly output, and the design efficiency and quality are improved.

A video processing method and apparatus, an electronic device, and a storage medium. The video processing method includes: in a first zoom mode or a second zoom mode, obtaining a video shot through a camera lens; in the first zoom mode, processing a video image of a first exposure frame by using a super-resolution algorithm; in the first zoom mode, fusing a video image of a first exposure frame processed by using the super-resolution algorithm and a video image of a second exposure frame not processed by using the super-resolution algorithm, to obtain a fused video; and in the second zoom mode, fusing the video image of a first exposure frame and the video image of a second exposure frame, to obtain a fused video.

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 multi-mode high-precision terrain interaction system based on tactile feedback and fuzzy logic, when a user touches the surface of a sensor by fingers, a piezoresistive sensor records pressure position and intensity information in real time, and an upper computer adopts a super-resolution algorithm to filter and compensate original data. And after two-dimensional funnel effect and fuzzy logic calculation, the platform adjusts feedback amplitudes and frequencies of the LRA at four corners in real time, and accurately simulates topographic features at a touch position. A two-dimensional interface on the upper computer displays touch points and corresponding tactile feedback parameters in real time, a user can adjust touch actions in real time according to feedback, and the accurate and efficient multi-mode terrain interaction effect is experienced.

The present invention discloses a method for detecting pedestrians on highways based on super-resolution. A bicubic downsampling operation is performed on a data set to obtain a corresponding high-resolution image, and blur kernels and noise are extracted from the data set to construct a low-resolution image. The obtained high-resolution image and low-resolution image are used to train a super-resolution generative adversarial network to obtain a super-resolution generator. The low-resolution image to be identified is input into the trained super-resolution generator, and then a feature point detection network is used to extract feature points, and the total weight is calculated. If the calculated total weight is greater than the set weight threshold, it is judged to be a pedestrian. The present invention uses a super-resolution algorithm to supplement detailed information for small-sized pedestrians, and uses human feature point information for further screening, thereby improving detection accuracy, reducing false alarm rate, and adapting to different application scenarios.

A computational pupillometry system comprises an imaging device configured to capture video frames of a subject's eye and processors executing instructions to perform advanced pupillary assessment. The system employs multi-frame integration techniques, including super-resolution algorithms that utilize sub-pixel shifts between frames, temporal averaging for noise reduction, and parallax-based artifact mitigation to enhance measurement accuracy. Artificial intelligence models, including temporal neural networks, analyze the enhanced pupillary data to determine pupillary parameters and calculate a light-invariant Pupil Reactivity (PuRe) score. The system processes ambient lighting conditions through computational models that analyze video frames before and after controlled stimulation, enabling consistent scoring across varying environmental conditions. Quality assurance mechanisms provide pre-recording and post-recording validation with real-time feedback. The system integrates with electronic medical records through standardized healthcare protocols and supports synchronized, multi-device deployment across healthcare networks.

The present invention discloses a single-image super-resolution algorithm based on a feedback mechanism, which includes the following steps: (1) Obtain the DIV2K dataset as the training set for training the neural network, and use bicubic downsampling to obtain image pairs; (2) Utilize the feedback mechanism to construct a single-image super-resolution generation network model; (3) Input the image pairs into the single-image super-resolution generation network model, use the Charbonnier loss as the loss function, and optimize the model parameters through the Adam backpropagation algorithm to obtain a trained super-resolution network model. The present invention fully obtains the features of the front and back layers by adopting the feedback mechanism, continuously strengthens the shallow features by using the features of the back layer, and further reduces the number of network parameters and memory occupancy; optimizes the features of the front and back layers by using coordinate attention to allocate attention to the features of the front and back layers; in addition, by using a dense connection receptive field module, the receptive field size of the features is increased.

This disclosure describes systems, methods, and devices related to video post-processing using a single local directional pattern (LDP) for multiple post-processing steps. A method may include identifying video received by a first device from a second device and decoded by the first device; generating a LDP of a video frame of the decoded video; detecting, based on the LDP and the video frame input to a blurred region detection algorithm, a blurred region and a non-blurred region of the video frame; applying, based on the LDP and the video frame input to a super resolution algorithm, super resolution on the non-blurred region of the video frame without applying the super resolution to the blurred region; and generating, based on the LDP and the video frame input to a blended image algorithm, a blended image of a low-resolution image of the video frame and a high-resolution image of the video frame.

The invention relates to the field of image display, in particular to an infrared image display method, which comprises the following steps of: S1, performing respe operation on an input temperature array; s2, converting the temperature array into a temperature matrix; s3, performing smoothing operation by using a Laplacian algorithm; s4, normalizing the temperature matrix into a gray value; s5, colors are distributed according to the color map of each pixel; s6, processing the low-resolution image by using a super-resolution algorithm; according to the invention, the readability of the image can be improved, and the visualization of information is enhanced.

The invention discloses a near-field millimeter wave super-resolution imaging method based on nuclear adaptive filtering, and relates to the field of super-resolution imaging. The method is based on a kernel adaptive filtering operator, through a lightweight learning method, it is ensured that estimation characteristics conforming to original data to the maximum extent are met, and a high-resolution image is efficiently predicted and reconstructed. Aiming at a subsequent image processing process of a two-dimensional near-field millimeter wave imaging system, under the condition of shortening data sampling time, a high-resolution image is reconstructed from low-resolution sampling data, and redundant hardware, a large number of image data sets and a dictionary learning process are not needed. Based on the method provided by the invention, the optimal visual effect is achieved in data sets with different shape features, and detail information in an amplified view is clearest. And compared with a typical super-resolution algorithm, the effect is better. Compared with a typical super-resolution algorithm, the method provided by the invention has the highest numerical value similarity and higher structural similarity.

The invention provides a VCSEL (Vertical Cavity Surface Emitting Laser) multi-modal defect image enhancement method and a VCSEL multi-modal defect image enhancement system. The method comprises the following steps: acquiring multi-modal defect image data of a VCSEL; constructing and training an SRCNN image enhancement model; the SRCNN image enhancement model is used for recovering high-resolution details from a low-resolution multi-modal defect image by combining an electroluminescent imaging technology and utilizing an improved SRCNN image super-resolution algorithm; and inputting the multi-modal defect image data into a trained SRCNN image enhancement model to perform image enhancement on the multi-modal defect image data, and outputting high-resolution defect image data. According to the method, the EL imaging technology is combined, the improved SRCNN image super-resolution algorithm is utilized, high-resolution details are recovered from the low-resolution multi-mode defect image, the image quality is remarkably improved, and therefore the yield of VCSEL products is improved.

The method for stable downward extension of geomagnetic anomaly data based on the SRGAN deep learning network belongs to the field of geophysical magnetic field data extension. The present invention combines the characteristics of geomagnetic anomaly data extension and adds a CBAM spatial attention mechanism module to improve the network model's spatial perception of magnetic anomaly data. In combination with the characteristics that the downward extension of potential field data will amplify high-frequency signals and generate noise, the deep learning model is combined with adaptive threshold multiple wavelet transform to suppress noise data and further improve the extension accuracy. In order to enable the model to fully learn the detailed information of geomagnetic anomaly data, a training data set construction method combining a Kriging super-resolution algorithm with a sliding sub-area mode is proposed. By designing a synthetic simulation model experiment and comparing the measured data with the other three algorithms, the model of the present invention has the highest calculation accuracy and the best recovery of magnetic anomaly details.

The invention discloses a rendering method and device, and belongs to the field of data processing. The method comprises the following steps: loading texture resource data of a first scene; processing the texture resource data based on a super-resolution algorithm to obtain super-resolution texture resource data; and rendering the first scene through the super-divided texture resource data.

The invention discloses a visual sensing information preprocessing method, system and device applied to a nuclear emergency robot and a medium, relates to the technical field of image processing, and solves the technical problem that the image quality of the nuclear emergency robot is poor. The image detail reduction capability in an extreme environment is effectively improved, the structural similarity of a dark area reaches 90% or above, and meanwhile, the irradiation resistance of a visual module is improved to a certain extent on the algorithm level. The framework can be applied to a nuclear emergency robot vision system and can be popularized to other emergency low-illumination radiation field scenes at the same time, and the problem that a traditional vision system is low in returned imaging quality is solved.

The invention provides an image enhancement method and device for an X-ray security inspection machine. The method comprises the following steps: acquiring a security inspection display video output by the X-ray security inspection machine; extracting a region-of-interest map from a security check X-ray image frame sequence corresponding to the security check display video; striping the region-of-interest image with the foreground object into a plurality of striped images, and performing super-resolution processing on the plurality of striped images to obtain a plurality of super-resolution sub-images; and splicing the plurality of super-resolution sub-images to obtain a target enhanced image. According to the method, the region of interest in the security check X-ray image is extracted and striped, so that a key information region can be focused, and the calculation load of a super-resolution algorithm is reduced; according to the X-ray pseudo-color image super-resolution enhancement method and device, super-resolution processing is carried out on the striped images, accurate enhancement of the region of interest in the X-ray pseudo-color image output by the old X-ray security inspection machine is achieved, the image quality can be remarkably improved without replacing core hardware of the X-ray security inspection machine, and the networking expansion capacity is achieved.

This invention discloses a near-field millimeter-wave super-resolution imaging method based on kernel adaptive filtering, belonging to the field of super-resolution imaging. This method, based on a kernel adaptive filtering operator, uses a lightweight learning method to ensure maximum accuracy in predicting and reconstructing high-resolution images by accurately matching the estimated features of the original data. This invention addresses the subsequent image processing of two-dimensional near-field millimeter-wave imaging systems, reconstructing high-resolution images from low-resolution sampled data while shortening data sampling time, without requiring additional hardware, large image datasets, or dictionary learning processes. The proposed method exhibits optimal visual effects across datasets with varying shape features, providing the clearest detail in magnified views. It outperforms typical super-resolution algorithms. The proposed method demonstrates the highest numerical similarity and high structural similarity compared to typical super-resolution algorithms.

The invention relates to the technical field of signal analysis, and discloses a high-speed signal integrity analysis method, system and device and a medium, and the method comprises the steps: configuring a measuring instrument and a probe; amplitude-phase errors are eliminated through de-embedding calibration; acquiring original time domain or frequency domain signal data, and preprocessing to obtain a signal sequence with a high signal-to-noise ratio; constructing a channel model based on a transmission line theory, including performing noise suppression processing on the de-embedded frequency domain response and improving the time delay resolution capability of a reflection point through a super-resolution algorithm; utilizing a sparse reflection model to position impedance discontinuous points; aiming at a plurality of parallel lines, a multi-conductor transmission line model is adopted to separate crosstalk signals; carrying out jitter and phase noise modeling; the reflection time delay and the crosstalk propagation time delay estimated in the channel model are converted into physical distances, and positioning of a reflection point or a crosstalk source is achieved. According to the invention, the problem of step / pulse response distortion caused by limited bandwidth, discontinuous impedance and weak reflection of an instrument in a traditional high-speed signal integrity test is solved.

The invention discloses a crop organ phenotype analysis method for unmanned aerial vehicle RGB image super-resolution reconstruction based on semantic perception, and the method comprises the steps: collecting images at different flight heights, and constructing a semantic guidance data set and a degradation data set; constructing a degradation model for simulating a low-resolution unmanned aerial vehicle image at a high flight height and training the degradation model; performing super-resolution reconstruction on the RGB image acquired by the unmanned aerial vehicle according to a semantic perception super-resolution algorithm; organ phenotype extraction is carried out based on the super-resolution reconstruction image; compared with a traditional method, the method can effectively solve the problems of low reconstruction efficiency and unreal texture caused by the scale change of the same ground feature in different flight height images, difficulty in acquiring paired images through the unmanned aerial vehicle in the field and lack of semantic guidance, improves the spatial resolution of the RGB image of the high flight height unmanned aerial vehicle, and improves the image quality. Time and space resolutions are considered, so that the extraction precision of the organ phenotype is improved.

The present application relates to the technical field of image processing and computer vision, and discloses an infrared image super-resolution system and device based on ZYNQ, which comprises an infrared image acquisition module, a ZYNQ infrared image processing module comprising an ARM processor and a 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 low-resolution infrared images through an infrared camera, the image data collected by the infrared camera is transmitted to the ZYNQ infrared image processing module through a digital interface, the image processing algorithm in the ZYNQ infrared image processing module starts to perform super-resolution processing on the image data, the processed high-resolution image data is sent to the high-resolution infrared image output module through a digital interface, and is converted into HDMI through a VGA interface and output to the high-resolution infrared image display module. Through the optimized super-resolution algorithm, the image details are effectively restored, and the image resolution is improved.

A computational pupillometry system comprises an imaging device configured to capture video frames of a subject's eye and processors executing instructions to perform advanced pupillary assessment. The system employs multi-frame integration techniques, including super-resolution algorithms that utilize sub-pixel shifts between frames, temporal averaging for noise reduction, and parallax-based artifact mitigation to enhance measurement accuracy. Artificial intelligence models, including temporal neural networks, analyze the enhanced pupillary data to determine pupillary parameters and calculate a light-invariant Pupil Reactivity (PuRe) score. The system processes ambient lighting conditions through computational models that analyze video frames before and after controlled stimulation, enabling consistent scoring across varying environmental conditions. Quality assurance mechanisms provide pre-recording and post-recording validation with real-time feedback. The system integrates with electronic medical records through standardized healthcare protocols and supports synchronized, multi-device deployment across healthcare networks.

This invention provides an image enhancement method and apparatus for X-ray security inspection machines. The method includes: acquiring a security inspection display video output by the X-ray security inspection machine; extracting a region of interest (ROI) map from the X-ray image frame sequence corresponding to the security inspection display video; striping the ROI map containing foreground items into multiple striped images, and performing super-resolution processing on the multiple striped images to obtain multiple super-resolution sub-images; and stitching the multiple super-resolution sub-images to obtain a target enhanced image. This method, by extracting the ROI from the security X-ray image and performing striping processing, can focus on key information areas and reduce the computational load of the super-resolution algorithm. By performing super-resolution processing on multiple striped images separately, it achieves precise enhancement of the ROI in the pseudo-color X-ray images output by older X-ray security inspection machines. It significantly improves image quality without replacing the core hardware of the X-ray security inspection machine and has network expansion capabilities.

The present invention relates to the technical field of seismic damage identification of concrete structures, and particularly to a method, device, electronic device and storage medium for seismic damage identification and segmentation of RC structures. The method includes: acquiring images of damaged RC structures and components, and constructing a target detection data set and a semantic segmentation data set; constructing a damage location model and a semantic segmentation model and training them, and fine-tuning the image super-resolution algorithm using the target detection data set and the semantic segmentation data set; fusing the trained damage location model and the trained semantic segmentation model based on the fine-tuned image super-resolution algorithm to obtain a final damage identification and segmentation model. It breaks through the limitation of the traditional damage segmentation model on the shooting distance of the input image, helps to promote the practical application of advanced computer vision algorithms in actual projects, realizes the long-distance identification and segmentation of seismic damage of RC structures, and improves the efficiency of post-earthquake assessment work.

The invention relates to the technical field of computer image processing, and discloses a screen capture method and device.The method comprises the following steps that interface elements on a screen are intelligently recognized through a capture instruction input by a user, a preset capture area is automatically recommended, and meanwhile the user can manually adjust the capture area; the method comprises the following steps: after dynamic content capture is carried out on a scrolling page or a video, generating a long screenshot or recording a GIF or a video clip with a specified time length by splicing a plurality of pictures; applying a super-resolution algorithm to long screenshots, GIF or video clips to optimize image quality and remove moire and noisy visual flaws; through a real-time editing function and gesture operation, a storage path or a sharing platform is automatically recommended according to historical operation or content characteristics of a user. By integrating a multi-mode trigger recognition mechanism, including voice, gesture, shortcut key and floating button operation, the screen capture instruction accurate response of a user in different use scenes is realized, and the effects of uniform operation inlets and flexible trigger modes are obtained.

The invention relates to a DOA estimation method for eliminating mutual coupling influence based on a mixed modulus structure, and aims to solve the influence of limited number of radio frequency chains and mutual coupling effect of antenna arrays on DOA estimation precision. The method comprises a DOA estimation framework in two stages: in the first stage, a spatial covariance matrix is reconstructed by selecting a middle subarray and combining a diagonal loading technology, DOA is preliminarily estimated, and a mutual coupling effect is relieved; and in the second stage, the DOA estimation precision is further improved by using full-array data and a super-resolution algorithm, and high-resolution DOA estimation is realized by compensating a mutual coupling effect. The invention also provides a corresponding system design, which comprises a switching network, an amplifier and a phase shifter and is used for dynamically adjusting the array to realize optimized signal receiving and processing. Compared with a traditional method, the method has the advantages that high DOA estimation precision can still be achieved under low radio frequency chain configuration, and the method has remarkable performance advantages and practical application value.

The invention provides a double-domain remote sensing image super-resolution method, and belongs to the technical field of super-resolution algorithms in remote sensing image processing. According to the method, a remote sensing image is converted from an image domain to a frequency domain through a domain conversion operator module, phase and amplitude information is reserved, and feature optimization is conducted on frequency domain data through a complex value neural network module. And the optimized frequency domain data is converted back to an image domain through an inverse domain conversion operator, and meanwhile, super-resolution reconstruction is carried out in the image domain in combination with a SwinTransform architecture. And the double-domain data consistency constraint module is used for constraining the training optimization direction through the loss function design of Fourier transform. By adopting the double-domain remote sensing image super-resolution method, remote sensing image super-resolution reconstruction under a double-domain strategy is realized, the problem of low availability of low-resolution remote sensing satellite image data is effectively solved, and the application scene of the low-resolution remote sensing image data is expanded.

Embodiments of the present application provide a video playing method, an image processing method and a model training method, a device and electronic equipment. The image processing method comprises: acquiring a to-be-processed image; inputting the to-be-processed image into an image processing model to perform image processing according to a super-resolution algorithm. The image processing model comprises a first convolutional network and a second convolutional network connected to each other, convolutional parameters of the first convolutional network are trained to represent image parameters of the super-resolution algorithm, and convolutional parameters of the second convolutional network are preset to represent operator parameters of the super-resolution algorithm. The scheme of the embodiments of the present application improves the efficiency of image super-resolution processing, and at the same time, uses a simple convolutional network structure to ensure low cost of the image processing model.

The present application provides a soybean growth point detection emergence rate method fused with a super-resolution algorithm, which solves the problems of detail loss of growth points caused by low resolution of unmanned aerial vehicle high-altitude aerial image, lack of optimization of existing super-resolution technology for agricultural scenes, and low detection accuracy caused by overlapping of densely planted seedlings. The present application is realized by the following steps: obtaining soybean seedling stage image and pretreating; inputting the standardized image dataset into a super-resolution model based on RealESRGAN architecture and embedded with a unified adaptive multi-branch high-frequency enhancement module for super-resolution reconstruction to obtain a high-resolution reconstructed image; inputting the high-resolution reconstructed image into a YOLOv8 growth point detection model to output growth point detection results; eliminating false detection boxes with confidence lower than a set threshold, counting the number of effective growth points and calculating the emergence rate. The present application balances high-altitude collection efficiency and detection accuracy, has low relative error, and is suitable for automatic detection of soybean seedling emergence rate.

The present invention discloses a super-resolution effect evaluation method and device based on high-resolution measurement data. The method comprises: obtaining a low-resolution image and a high-resolution image database, selecting the high-resolution image database to obtain resolution measurement data related to the low-resolution image; processing the resolution measurement data related to the low-resolution image to obtain a ground object dataset; performing super-resolution reconstruction processing on the low-resolution image using a super-resolution algorithm to be evaluated to obtain a super-resolution processing result; and performing resolution evaluation on the super-resolution processing result based on the ground object dataset to obtain the resolution of the super-resolution processing result. The method of the present invention can not only overcome the inefficiency and uncertainty of traditional subjective evaluation methods, but also improve the defects of existing objective quantitative evaluation methods, such as poor consistency with human visual perception on the one hand, and strong correlation with training datasets on the other hand, but insufficient generalization ability. It can achieve accurate quantitative, intuitive and efficient evaluation of super-resolution result images.

The invention relates to the technical field of crutch production detection, in particular to a crutch defective product detection method and a distribution mechanism, which can perform detail enhancement and high-quality reconstruction on a crutch image by combining a super-resolution technology and a deep learning model, thereby improving the accuracy of defect identification. Particularly, the super-resolution algorithm based on attention mechanism guidance is utilized, the reconstruction key point can be dynamically adjusted according to the image content, the denoising degree is automatically adjusted, and excessive smoothing or noise amplification is avoided. In addition, through innovative model designs such as a space-time convolutional network, neural architecture search and a double-flow attention mechanism, the adaptability and classification capability of the model to different defect types are further enhanced. The high-precision automatic detection method not only improves the accuracy of defective product detection, but also greatly improves the detection speed and reduces manual intervention.

The invention relates to the technical field of cloud information, in particular to a video stream optimization method and system for desktop cloud. According to the video stream optimization method for the desktop cloud, a WebRTC retransmission feedback and congestion control mechanism is utilized, the performance and congestion condition of network connection are obtained by counting the packet loss rate and the packet transmission delay, the coding resolution and the frame rate are dynamically adjusted, a super-resolution algorithm and a frame generation algorithm are started at a client, and the video stream optimization efficiency is improved. Therefore, the resolution and the refresh rate preset by the user are kept unchanged. According to the video stream optimization method and system for the desktop cloud, the problems of jamming, frame dropping and black screen can be avoided, and the user experience of the desktop cloud in a weak network scene is greatly improved.