Method for reconstructing binary optical image spectrum

Abstract

A method for reconstructing a binary optical image spectrum includes the following detailed steps that (1), original binary optical image spectrum data are read in; (2), according to parameters and the structure of an optical system, whether the optical system is an equal magnification system or not is judged, and if the optical system is an unequal magnification system, images of all wave bands with unequal magnifications are converted into equal magnification images; (3) standard errors of point spread functions of all the wave bands are calculated; (4), a noise level is analyzed, and if noise does not exist, a traditional inverse filtering method is used for restoring spatial dimension images; (5), for the images with the noise and the small standard errors of the point spread functions, a Jansson-VanCitter algorithm is used for restoring the spatial dimension images; for the images with the noise and the large stand errors of the point spread functions, a Norbert wiener inverse filtering method is adopted for restoring the spatial dimension images; (6), a linear deconvolution method is adopted for reconstructing spectrum dimension data; (7), the result of image spectrum reconstruction is obtained.

Description

(1) Technical field [0001] The invention relates to a reconstruction method of a binary optical image spectrum, which belongs to the technical field of hyperspectral data reconstruction and processing, and is applicable to the theoretical method and application technology research of binary optical system data processing. (2) Background technology [0002] Hyperspectral images have achieved a breakthrough improvement in spectral resolution and realized "spectral integration". However, there are too many bands for hyperspectral images, resulting in less energy obtained by each band, resulting in the problem of low signal-to-noise ratio. The binary optical element is dispersed along the optical axis, and can be imaged in the spatial dimension and the spectral dimension at the same time. The system does not need to split light through a slit, which greatly enhances the incident energy, thereby improving the signal-to-noise ratio. But at the same time, this causes the light of ...

AI Technical Summary

Problems solved by technology

For example, the nearest neighbor, inverse filtering, and Wiener inverse filtering algorithms are faster, but the restoration quality is not high; while the algorithm that requires iteration is slower, but the restoration results are generally better

[0006] At present, the image spectral data reconstruction method based on binary optics mainly has the following shortcomings: (1) It is generally assumed that the optical system magnification of the system is the same, and the situation of systems with different magnifications is not considered, making the reconstruction method not suitable for different magnifications System; (2) The reconstruction method is not selected according to the different noise levels of the data, which is prone to problems such as noise amplification and submersion of the useful information of the image spectrum; (3) Different methods are not selected according to the standard deviation of the image spectrum data, which is prone to problems The ringing effect thus affects the quality of the reconstructed image spectral data

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