Scattering generalization imaging method and experimental method based on physical driving

An imaging method and physical technology, applied in neural learning methods, design optimization/simulation, biological neural network models, etc., can solve the problems of limitation, limited recovery, limited recovery effect of scattered target information and limited imaging quality, etc. Scope of work, good learning and the effect of extraction

Active Publication Date: 2021-01-29
NANJING UNIV OF SCI & TECH
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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 m

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  • Scattering generalization imaging method and experimental method based on physical driving
  • Scattering generalization imaging method and experimental method based on physical driving
  • Scattering generalization imaging method and experimental method based on physical driving

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Embodiment Construction

[0040] The present invention is described in further detail now in conjunction with accompanying drawing.

[0041] The physical-driven scattering generalization imaging method of the present invention, as shown in Figure 1(a)-Figure 1(c), specifically includes the following steps:

[0042] Step 1: The physical / data model built, see figure 2 ,

[0043] Step 1.1: Only use the traditional U-Net convolutional neural network structure and weaken the requirements for the network structure. The physical constraint network model consists of an autocorrelation physical constraint layer and a convolutional neural network layer. The physical constraint layer mainly includes speckle The autocorrelation calculation of I(x,y) is adjusted, and the speckle autocorrelation is obtained by the two-dimensional inverse Fourier transform of the energy spectrum

[0044] (3),

[0045] Equation (3) is the main operation of the physical constraint layer, which adjusts and reconstructs the speckle ...

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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...

Claims

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Application Information

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IPC IPC(8): G06F30/20G06N3/04G06N3/08
CPCG06F30/20G06N3/08G06N3/045
Inventor 韩静柏连发郭恩来朱硕顾杰崔倩莹周晨寅
Owner NANJING UNIV OF SCI & TECH
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