32 results about "Super resolution algorithm" patented technology

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.

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 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 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 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 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 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 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.

This application discloses a vehicle positioning method and related apparatus, relating to the field of vehicle positioning technology. The method is applied to a roadside unit (Roadside Unit), which communicates with an on-board unit (OV) via an Orthogonal Frequency Division Multiple Access (OFDM) side link. The method includes: simultaneously receiving multiple uplink reference signals transmitted from multiple OV units; performing channel estimation and channel separation on the multiple uplink reference signals to obtain the channel frequency response corresponding to each OV unit; processing the channel frequency response corresponding to each OV unit using a super-resolution algorithm to obtain the angle of arrival (Angle of Arrival) and distance of arrival (DAR) corresponding to each OV unit; and determining the position of the vehicle corresponding to each OV unit in the coordinate system corresponding to the Roadside Unit based on the Angle of Arrival and DAR corresponding to each OV unit. This application's method combines the multi-user interference-free access capability of OFDM with super-resolution parameter estimation technology, achieving high-precision, high-concurrency real-time positioning of multiple vehicles in complex vehicle-to-everything (V2X) environments.

The present invention relates to the field of data processing technology, and more particularly to a surveillance video compression and restoration method. The method comprises video compression processing, including extracting reference frames and reducing image quality, and video restoration processing, including image replacement and super-resolution algorithm restoration. This method, designed based on the inherent characteristics of surveillance video, reduces storage space and network transmission bandwidth, and achieves high-definition video after restoration.

The application provides a multi-modal high-precision terrain interaction system based on tactile feedback and fuzzy logic, when a user's finger touches the surface of a sensor, a piezoresistive sensor records real-time pressure position and intensity information, and a host computer filters and compensates the original data by using a super-resolution algorithm.After two-dimensional funnel effect and fuzzy logic calculation, the platform adjusts the feedback amplitude and frequency of four corners LRA in real time, and accurately simulates the terrain characteristics at the touch position.A two-dimensional interface on the host computer displays the touch point and corresponding tactile feedback parameters in real time, and the user can adjust the touch action in real time according to the feedback, and experience accurate and efficient multi-modal terrain interaction effect.

The invention discloses a three-dimensional scanning method, three-dimensional scanning equipment and a three-dimensional scanning system.The three-dimensional scanning method comprises the steps that a three-dimensional grid model and a first texture map of an object to be measured are obtained, and the first texture map and the three-dimensional grid model have a first three-dimensional mapping relation; processing the first texture map based on a super-resolution algorithm to obtain a second texture map, the second texture map and the first texture map having a two-dimensional mapping relationship; determining a second three-dimensional mapping relation between the second texture map and the three-dimensional grid model based on the first three-dimensional mapping relation and the two-dimensional mapping relation; and performing texture mapping on the three-dimensional grid model based on the second three-dimensional mapping relation and the second texture mapping. The three-dimensional scanning equipment can realize the three-dimensional scanning method. The three-dimensional scanning system can realize the three-dimensional scanning method. Through the setting, the definition of the texture map can be improved without using a camera with high resolution.

The invention relates to the technical field of real-time three-dimensional image processing, and provides a method and a device for improving the visual fidelity of an image. According to the method, on the basis of the assumption that original pixels are located between two adjacent surfaces in a source image, the positions where the original pixels are located are classified, and different intersection filters are used for processing the original pixels meeting corresponding classification standards; after the intersection filter responds, an amplification coefficient corresponding to the decomposed source image features in at least one dimension is determined according to the processing logic of the intersection filter, original pixels are amplified based on the amplification coefficient, and a good enough approximate estimate of the amplified target image is obtained; according to the image super-resolution algorithm, the specific parameterization of the image is realized, the support of a filter is reduced to the maximum extent, the image quality and the calculation complexity are balanced to the maximum extent in the limited fixed function hardware prediction, and the problems that the image super-resolution algorithm in the prior art is not suitable for hardware and is poor in effect are solved.

The invention provides an image compression and decompression method and device, a storage medium and electronic equipment. The method comprises the steps of obtaining an original image of a transaction file, performing down-sampling on the original image by adopting a bicubic interpolation algorithm to obtain a compressed image, and storing the compressed image in an object storage server; under the condition that the image of the transaction file needs to be acquired, the compressed image is read from the object storage server, a super-resolution algorithm is adopted to restore the compressed image into a high-resolution image, and the super-resolution algorithm is at least used for extracting shallow layer features, deep layer features and hierarchical features of the compressed image; the shallow-layer feature comprises texture information of the compressed image, the deep-layer feature comprises text information of the compressed image, the hierarchical feature comprises a relationship between the shallow-layer feature and the deep-layer feature, and the peak signal-to-noise ratio of the high-resolution image is greater than a first preset value. According to the technology, the problem that in the prior art, when the compressed image is decompressed, accurate recovery cannot be achieved is solved.

The invention discloses a comprehensive performance testing method for an infrared image and scene generating device. The method comprises the following steps of working wave band testing, spatial resolution testing, gray level and dynamic range testing, temperature non-uniformity testing and geometric distortion testing. According to the invention, a set of integrity performance evaluation system from working wave band, spatial resolution and gray level characteristics to temperature uniformity, response speed and geometric distortion is provided, advanced methods such as radiation calibration, a super-resolution algorithm, MTF accurate calculation and nonlinearity analysis are introduced, and new evaluation dimensions and quantitative indexes are introduced at the same time. And the test precision and reliability are obviously improved.

To improve noise resistance without using a super-resolution algorithm and to reduce a calculation load when making the phases of a received first band signal and a second band signal continuous.SOLUTION: A radar device includes a transmission unit that transmits a transmission signal and a reception unit that receives a reflection signal of the transmission signal, in which a signal in a second band different from a first band is transmitted following the signal in the first band as the transmission signal from the transmission unit, the signal in the first band and the signal in the second band are frequency-modulated pulse signals, and a calculation unit that executes processing of resampling a frequency spectrum of the signal in the second band received by the reception unit in a slow time axis direction.SELECTED DRAWING: Figure 1

The invention provides a printed pattern denoising method, device and equipment and a medium. The method comprises the following steps: collecting a printed pattern; randomly adding wrinkle noise and salt and pepper noise into the printed pattern to finally obtain a pattern noise image set; randomly amplifying, reducing and rotating the image in the pattern noise pattern set to obtain a training sample; the adopted generative adversarial network comprises a generator and a discriminator; inputting the training sample into the generative adversarial network for training, and when the value of the loss function reaches a preset threshold value, stopping training to obtain a trained generative adversarial network; and inputting an image needing to be denoised into the trained generative adversarial network to obtain a denoised image, thereby solving the problems of wrinkle noise and salt and pepper noise existing in the printed pattern, and realizing efficient denoising and high-definition restoration of the printed pattern by combining the generative adversarial network and a super-resolution algorithm.

The application relates to the technical field of remote sensing image processing, in particular to a satellite remote sensing image super-resolution method and device, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring an original image of target satellite remote sensing; enlarging the original image to a target size to obtain an enlarged image; extracting a shallow feature image and a deep feature image of the original image, performing super-resolution processing on the shallow feature image and the deep feature image to obtain a super-resolution residual image; and generating a super-resolution reconstruction image according to the super-resolution residual image and the enlarged image. The method can generate an image with higher resolution, richer information and higher accuracy by using linearly increasing calculation amount and a larger receptive field. Thus, the problems of exponential expansion of calculation amount, limited receptive field and detail restoration in the process of processing satellite remote sensing multispectral images by using a traditional attention mechanism super-resolution algorithm are solved.