A Fracture Image Compression Sampling Method Based on Generative Adversarial Network

Abstract

The present invention proposes a crack image compression sampling method based on a generative adversarial network. The method includes the network architecture design of the generative adversarial network, the crack image generator modeling of the mapping relationship between the crack image and the low-dimensional vector, and the hyperparameters of the adversarial training. Tuning, design of compressed observation matrix for compressed sampling, solution of optimal low-dimensional vector, etc. The method of the present invention adopts the crack image generator trained to generate an adversarial network as a physical constraint to realize the decompression and reconstruction of the image, without requiring the sparsity of the crack image as in the traditional compression sampling method, and has a wider application range. After the generative confrontation network learns the mapping relationship between the crack image and the low-dimensional vector, the low-dimensional vector is optimized based on the gradient descent method, and the fast solution of image decompression and reconstruction is realized. The method has unique advantages in the reconstruction accuracy and reconstruction speed of the fracture image under a relatively high compression rate, and has strong robustness to noise.

Description

technical field [0001] The invention belongs to the technical field of signal processing and structural health monitoring, in particular to a crack image compression sampling method based on a generative confrontation network. Background technique [0002] At present, under the influence of long-term loads, environmental erosion and other factors, various types of infrastructure will inevitably be damaged. The continuous accumulation and development of damage will lead to the continuous decline of the bearing capacity and use function of the structure, until the safe use of the structure is endangered. Therefore, real-time monitoring of structural damage and evaluation of structural health through theoretical analysis are one of the core issues in structural health monitoring. The cracks on the surface of the structure are frequently monitored indicators, which can reflect the degree of damage to the structure and have a serious impact on the function of the structure. For ...

AI Technical Summary

Problems solved by technology

[0007] At present, the research and application of compressed sampling method based on generative adversarial network in structural health monitoring does not yet exist. It uses crack image generator to constrain decompression and reconstruction, and realizes reconstruction accuracy and noise robustness under high compression rate. The advantages of stickiness, refactoring speed, etc. have not been tapped

Images