4results about How to "Hard to get" patented technology

This invention discloses a method for restoring turbulent degradation images based on complex amplitude detection, belonging to the field of computational optics imaging technology. This method constructs a computational optics imaging system without a hardware correction unit. It utilizes a wavefront sensor and an imaging sensor to simultaneously acquire Hartmann images and target degradation images. A computational unit performs wavefront complex amplitude reconstruction and aberration correction. Based on this, a physically constrained end-to-end neural network architecture is designed. By reconstructing the wavefront complex amplitude, it achieves accurate mapping and inference from the Hartmann image to the point spread function of the imaging system. Finally, a multi-scale deconvolutional network is combined to complete high-quality restoration of the turbulent degradation image. This invention eliminates the need for a hardware corrector, overcomes the physical limitations of traditional adaptive optics, effectively solves the problem of insufficient point spread function accuracy, and enables time-delay-free and highly efficient turbulent image restoration under low-cost conditions.

The application discloses a ship radiation noise line spectrum estimation method based on multi-snapshot sparse Bayesian learning, and belongs to the field of underwater acoustic signal processing; the method utilizes the sparse characteristics of the ship radiation noise line spectrum, that is, the line spectrum is not uniformly distributed in the whole frequency range, but is discrete and sparse, converts the line spectrum estimation problem into a sparse signal recovery problem, so as to solve the problems in the background technology, under the condition that the data sample length is limited and the sparsity is unknown, the Bayesian framework utilizes probability modeling to adaptively adjust the hyperparameters from the global optimization angle, can effectively reduce the sidelobe, realizes high resolution, reduces the background noise fluctuation variance through multiple observation snapshots statistical average, enhances the signal-to-noise ratio of the line spectrum output, and improves the line spectrum feature detection performance.

This invention belongs to the field of wireless communication, specifically relating to a method for calculating the spatial polarization domain information of received signals based on a three-element circular array receiver. This invention utilizes the similarities and differences between known steering vector configurations to solve the problem of rapidly obtaining parameters when the spatial polarization domain parameters of the received signal are unknown. Using the received signals at each point of the three-element circular array receiver, a model is constructed in pairs. Using the rotation-invariant subspace algorithm and the least squares method, the pairwise differences of the received signals at each point of the three-element circular array receiver are calculated. Given the known steering vector configuration, the elevation and azimuth angles are solved by solving equations. Using the calculated elevation and azimuth angles, and the known steering vector configuration, the phase angle and phase difference in the polarization domain are directly calculated by solving the matrix correlation using the least squares method. Finally, all state estimation results are obtained, and the root mean square error of each angle at different snapshot numbers and signal-to-noise ratios is calculated.

This application provides a training method, apparatus, electronic device, and storage medium for a lesion detection model, belonging to the field of image processing technology. It involves acquiring a first medical image and a second medical image, where the first image is unlabeled and the second image is labeled. A preset teacher model is used to detect lesions in the first medical image, and a preset student model is used to detect lesions in both the first and second medical images. Based on the lesion detection results, a loss calculation is performed to obtain target loss data. The first network parameters of the preset student model are adjusted based on the target loss data, and the second network parameters of the preset teacher model are adjusted based on the first network parameters to train the preset student model, thus obtaining a lesion detection model. This lesion detection model is then used to detect lesions in the target medical image, obtaining the location and type of the target lesion, thereby improving the accuracy of lesion detection.