Scattering generalization imaging method and experimental method based on physical driving

Abstract

The invention relates to a scattering generalization imaging method and experiment method based on physical driving, and belongs to the technical field of imaging analysis, and the method comprises the steps: building a physical / data model, and employing a speckle pattern collected by a camera for imaging the speckle pattern; and taking the speckle correlation theory as a universal physical principle to constrain and guide the neural network to carry out generalized imaging in different scattering scenes. The experimental method comprises theoretical analysis and a system experiment, and the method is systematically discussed by establishing a specific system experiment. And the generalization ability and the generalization quality are improved, and more complex targets are generalized. Aphysical model based on speckle correlation and a data model based on deep learning are organically combined, so that the scattering generalization imaging effect and the imaging range of the neural network model are greatly improved.

Description

technical field [0001] The invention relates to a physics-driven scattering generalization imaging method and an experimental method, belonging to the technical field of optical imaging analysis. Background technique [0002] Taking the wavelength of light as the unit of measurement, most objects and scenes in life are rough, so light scattering exists in all aspects of life. After the target information is modulated by a strong scattering medium, the original effective target information will be seriously degraded. Scattering degradation has brought many problems and troubles to real life and scientific research observation applications, such as traffic supervision in dense fog weather in life, effective monitoring of vehicles through changing scattering scenes, including car license plate number recognition, and even drivers Face information recovery is of great significance. At the same time, in order to obtain specific target information, scattering interference is als...

AI Technical Summary

Problems solved by technology

Among them, the first three are computational imaging methods based on physical models. The recovery effect of the method based on wavefront shaping is also limited by the pixel size and number of modulation devices, and it is impossible to effectively restore and measure the scattering scene when it is not fixed; based on The specific target recovery of compressed sensing in the single-pixel imaging method is limited by the selected sparse base, and the target recovery takes a long time; the speckle correlation technology based on the memory effect can perform non-invasive imaging of the target behind the scattering medium, based on the optical The speckle correlation technology of the memory effect can restore the target behind the medium through a single frame of speckle information, but it is limited in principle by the scope of the optical memory effect, once the target cannot be effectively reconstructed, and the traditional phase recovery algorithm has no effect on the The recovery effect and imaging quality of scattering target information are also very limited

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