15 results about "Super resolution algorithm" patented technology

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 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 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.

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.

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.

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.

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 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

A method of image rendering, a device and a storage medium are provided. The method includes: by a render server, receiving texture content of a 2D application sent by a 2D composite display server; determining an overlapping area between a panel of the 2D application and a central field of view of a user; acquiring an image magnification of the overlapping area, where the image magnification is greater than 1; performing image super-resolution processing on texture content in the overlapping area by using an image super-resolution algorithm, according to the image magnification, to obtain a super-resolution image corresponding to the texture content in the overlapping area; and rendering the panel of the 2D application into XR space according to the super-resolution image.