Hyperspectral imager and imaging method based on compressive sensing

Abstract

The invention discloses a hyperspectral imager and an imaging method based on compressive sensing, mainly solving the problem that the existing hyperspectral imager has high sampling rate and high sensor realization difficulty. The imager comprises a battery of lens, a dispersive device, a spatial light modulator, a linear detector and a peripheral circuit. The acquired linear light source is split in the space through the dispersive device to form the plane light source formed by spatial dimension and spectral dimension. The plane light source converges again in the direction of spatial dimension after being modulated by the spatial light modulator to form the linear light source formed by spectral dimension. The linear detector completes sampling and quantizing. The imaging method is characterized by utilizing the obtained hyperspectral compressive observation vector to obtain the hyperspectral images through grouping and reconstitution. The hyperspectral imager improves the average reconstitution accuracy of each spectrum by utilizing the joint sparse characteristic among the hyperspectral spectra, has the advantages of simple structure and low cost and is suitable for compressive sensing and imaging of hyperspectra.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to a hyperspectral compressed sensing data collection system and imaging method, which can be used for hyperspectral image collection and imaging. Background technique [0002] In recent years, in the fields of environmental monitoring, geology, agriculture, medicine, and military affairs, traditional full-color and color images are far from meeting people's needs, and various hyperspectral imaging technologies have been widely used. In the hyperspectral imager, the sensor detects the reflection or radiation intensity of hundreds of different wavelengths of the target, thereby forming hundreds of continuous spectral segments to form a spectral image. A hyperspectral image is defined as three-dimensional data composed of a two-dimensional spatial domain and a one-dimensional spectral domain. Unlike video sequence images, the positions in the spatial domain of each spectral s...

AI Technical Summary

Problems solved by technology

[0011] At present, the theory of compressive sensing has been applied in the low-rate sampling and reconstruction of a single image; for hyperspectral images, the spectrum is wide, the amount of data is large, and there is a strong correlation between spectral segments, the theory of compressive sensing is very suitable It is used for low-rate acquisition and reconstruction of hyperspectral images, but there is no hardware implementation structure and hyperspectral image reconstruction method based on compressed sensing theory for low-rate acquisition of hyperspectral images

Images