7results about How to "Meet real-time processing needs" patented technology

The application provides a kind of spaceborne super high speed image intelligent processing method and device, including: high speed data interface unit, intelligent image processing unit, main control unit;The high speed data interface unit is connected respectively external data input unit, intelligent image processing unit and main control unit.This application keeps original data on front end FPGA, GPU module is only used as the position calculation of slice, avoids the repeated transmission of internal mass data, so as to cause the pressure of transmission bandwidth.

This application relates to an image processing method, apparatus, and computer device. The method includes: downsampling an image to be processed to obtain a downsampled image; generating an initial illumination image based on pixel information of each pixel in the downsampled image; performing FGS filtering on the initial illumination image to obtain an intermediate illumination image; performing brightness transformation and upsampling processing on the intermediate illumination image to obtain a target illumination image of the same size as the image to be processed; and using the target illumination image to enhance the brightness of the image to be processed to obtain the target image. This method can reduce the computational load in the image enhancement process without affecting the image enhancement effect, making it suitable for computer devices with lower computing performance and expanding its applicability.

This invention discloses an interpretable low-light image enhancement method. It constructs an unpaired learning framework, EDC-Net, with enhancement and degradation branches forming a closed loop. The enhancement branch adaptively enhances the brightness and contrast of the low-light image through pixel-level affine mapping, while the degradation branch predicts physically meaningful degradation parameters such as color gain and exposure scaling. The enhanced image is then reconstructed into a low-light image through a chain of explicit degradation operators, forming a cyclic consistency constraint. Training employs a three-stage progressive optimization strategy: degradation warm-up, master adversarial training, and fine-tuning. During the inference phase, only the lightweight enhancement branch is run. This invention achieves physical interpretability and controllability of the enhancement process, avoids overexposure and artifacts, improves training convergence stability, and significantly increases inference efficiency. It is suitable for real-time image processing scenarios such as nighttime surveillance and autonomous driving.

This invention discloses a piecewise adaptive Kalman track tracking method and apparatus based on residual classification, relating to the field of radar track tracking technology. Addressing the problems of poor adaptability to non-stationary measurement noise environments, parameter dependence on human experience, and complex FPGA implementation in existing track tracking methods, this invention proposes introducing a residual classification decision and piecewise adaptive noise adjustment mechanism into the Kalman filter recursive framework. First, a residual vector is calculated based on the predicted state and measurement data. Then, a classification decision is made using the residual vector, the predicted covariance, and the measurement noise covariance of the previous cycle, outputting the measurement quality level. Next, the measurement noise covariance is updated piecewise adaptively based on the measurement quality level. Finally, the filter gain is calculated based on the updated measurement noise covariance and the predicted covariance to complete the correction and update of the state and covariance.

This invention proposes a test-time optimized method for super-resolution reconstruction of UAV aerial infrared images, belonging to the fields of image processing and computer vision. A test-time optimization framework based on unfamiliar infrared degradation perception is constructed: a teacher-student model fusion mechanism is introduced to synthesize degraded reference images, and the optimization amplitude is dynamically controlled to prevent catastrophic forgetting; a frequency domain degradation estimation network calculates the blur kernel and noise parameters to generate pseudo-label images to guide model optimization; a multi-scale linear layer parameter optimization strategy is designed, updating only linear layer parameters to achieve rapid adaptation; and a joint constraint optimization process using reconstruction loss and feature consistency loss ensures pixel-level reconstruction accuracy and the preservation of pre-trained feature extraction capabilities. This method effectively solves the problem of super-resolution quality degradation caused by unknown degradation conditions in UAV aerial photography scenarios, significantly improves generalization ability under complex degradation migration conditions, and is suitable for applications such as nighttime search and rescue, security patrol, and agricultural assessment.

This invention discloses an air-ground collaborative stereoscopic electromagnetic spectrum mapping device and method. The device includes an airborne platform, a vehicle-mounted platform, and a ground station. First, it receives mapping parameters, plans a collaborative acquisition path between the airborne and ground platforms, and simultaneously collects, frames, and stores spectrum and spatiotemporal information. Second, after compensating the spectrum data from each platform, it performs three-dimensional fusion of air, time, and frequency. Based on the platform's real-time location and the fused data, it delineates dynamically complete areas and completes missing grids using an online optimization algorithm with a forgetting factor. Finally, it calculates the map coverage in real time; if the coverage exceeds a threshold, it replans the supplementary measurement path until a complete stereoscopic spectrum map is generated. This invention combines the advantages of airborne and ground platforms to achieve high-precision three-dimensional spectrum perception in large-area, complex scenes, improving completion accuracy and real-time performance.

The application discloses a space-based infrared dim small target detection method based on a vector signal, in order to overcome the low timeliness and large resource consumption bottleneck of the traditional "imaging-caching-preprocessing-detection" link, skips the image caching and preprocessing link, and directly takes the DN value vector signal read out by the detector as input. Wherein, the two-dimensional or higher-dimensional features of the target are recovered from the one-dimensional vector signal through the network based on the CNN and BERT mechanisms and with the help of the Markov random field model; the network based on the self-supervised U-Net structure is used to decouple the target and background clutter features, and the multi-scale detection mechanism is combined to selectively enhance the target features to complete the detection; the network is lightened through the multi-teacher knowledge distillation architecture, and pruning and quantization are combined to obtain a lightened detection model suitable for the resource limited environment on the satellite. The application realizes the "detection and calculation integration", and significantly improves the timeliness and resource efficiency of the space-based infrared dim small target detection.