Hyperspectral full-polarization image compression and reconstruction method based on machine learning optimization sparse basis

A fully polarized, sparse-based technology, applied in spectrometry/spectrophotometry/monochromator, polarization spectrum, instruments, etc., can solve the problem of long sampling time, information redundancy, circular polarization information increasing system complexity and optical Energy loss and other issues, to achieve fast optimization speed, improve reconstruction accuracy, and good adaptability

Active Publication Date: 2022-05-10
北京理工大学重庆创新中心 +1
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Problems solved by technology

However, the measurement of circular polarization information greatly increases the complexity of the system and the loss of light energy, and in the process of reconstructing the four Stokes parameters, the sampling time is long and the information is redundant due to the limitation of sparse basis selection

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  • Hyperspectral full-polarization image compression and reconstruction method based on machine learning optimization sparse basis
  • Hyperspectral full-polarization image compression and reconstruction method based on machine learning optimization sparse basis
  • Hyperspectral full-polarization image compression and reconstruction method based on machine learning optimization sparse basis

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[0023] The present invention will be described in detail below with reference to the accompanying drawings and examples.

[0024] The present invention provides a method for compressing and reconstructing hyperspectral fully polarized images based on machine learning optimization of sparse bases. The process is as follows: figure 1 shown. It is realized in two steps: according to the known full-polarization partial image of any band, based on the compressive sensing theory and machine learning algorithm, the sparse basis of the four Stokes parameters is obtained; according to the hyperspectral full-polarization image to be measured, based on the compressive sensing Theoretical and optimized sparse basis to reconstruct the four Stokes parameters of hyperspectral fully polarized images.

[0025] According to the known full-polarization local image of any band, the compression reconstruction of the full Stokes parameters is carried out in a certain polarization modulation mode, ...

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Abstract

The invention discloses a hyperspectral full-polarization image compression and reconstruction method optimized by machine learning to optimize the sparse basis. The image is imaged on the detector by combining a quarter-wave plate and a device with linear polarization characteristics. The fast axis angle of the wave plate and / or the light transmission axis angle of the device with linear polarization characteristics realize different full polarization modulation modes; use the full polarization modulation mode to process the full polarization partial image of any waveband to obtain compressed information; The particle swarm optimization algorithm is used to optimize the sparse basis, and the optimized sparse basis makes the fully polarized local image reconstructed by compressed information approach its original image. In application, the above-mentioned full polarization modulation method is used to perform polarization modulation on the hyperspectral full polarization image to obtain compressed information, and the reconstructed hyperspectral full polarization image is obtained by using the optimized sparse basis. The invention can realize the reconstruction of the four Stokes parameters of the hyperspectral image, and improve the reconstruction accuracy of the four Stokes parameters.

Description

technical field [0001] The invention belongs to the technical field of hyperspectral polarization imaging, and in particular relates to a method for compressing and reconstructing hyperspectral full-polarization images based on machine learning to optimize sparse bases, so as to realize the reconstruction of full Stokes parameters of hyperspectral images. Background technique [0002] Hyperspectral full-polarization imaging technology is an advanced technology used to obtain four-dimensional data of the target scene, which includes two-dimensional spatial information, one-dimensional spectral information and polarization information represented by four Stokes parameters. Compared with traditional hyperspectral imaging methods, the additional polarization information can characterize surface roughness, electrical conductivity, molecular distribution and material composition, etc. Because hyperspectral full-polarization imaging technology has the advantage of identifying subtl...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01J3/447G06N3/00
CPCG01J3/2823G01J3/447G06N3/006
Inventor 许廷发樊阿馨王茜张宇寒潘晨光郝建华
Owner 北京理工大学重庆创新中心
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