2results about How to "Guaranteed reconstruction quality" patented technology

The application discloses a real-time leakage positioning monitoring system and method based on electrical resistance tomography, relates to the technical field of process tomography and industrial safety monitoring, and solves the technical problem that it is difficult to intelligently identify, three-dimensionally position and quantitatively evaluate leakage events; the system comprises a sensor array, a multi-channel data acquisition and excitation module, a real-time data processing and image reconstruction module, a leakage intelligent identification and positioning module, a man-machine interaction and data management module; the core lies in that a multi-layer nested annular array and a sparse measurement strategy are adopted to improve data acquisition efficiency, a physical information neural network is introduced to reconstruct an image, a measurement topological coding matrix and a physical residual correction module are used to reconstruct the image, and finally, a time-space dual-domain leakage probability field model is constructed, a virtual sensor network and an improved D-S evidence reasoning framework are combined to intelligently identify and position a leakage diffusion mode.

This invention discloses a deep learning-based method for super-resolution processing of airborne optoelectronic video images. It constructs a high-order dynamic degradation model that integrates basic image degradation operations with the physical degradation mechanism of airborne optoelectronics, simulating the complex degradation process of the real imaging chain through multiple rounds of degradation loops. The dynamic degradation model generates a training dataset of low-resolution and high-resolution image pairs. A super-resolution reconstruction network is constructed based on a generative adversarial network framework. The generator uses the RRDB module for deep feature extraction and reconstructs high-resolution images through multi-stage upsampling. The discriminator adopts a U-Net structure with spectral normalization and introduces semantic consistency constraints. A two-stage course learning is performed using a domain-specific training dataset. This method can accurately simulate the complex degradation process of airborne optoelectronic imaging, improve the model's generalization ability, enhance the fidelity of tactical target details, and enable real-time inference on airborne platforms.