Spectrum reconstruction method for broadband filtering modulation spectrum detection system

By employing deep learning-based spectral reconstruction methods, combined with Transformer and KAN networks, the miniaturization and high signal-to-noise ratio issues of broadband filtered modulation spectral detection systems were addressed. This resulted in the expansion of the number of spectral channels and high-precision reconstruction of spectral information, thereby improving spectral resolution and detection sensitivity.

CN122072940APending Publication Date: 2026-05-22HANGZHOU INST FOR ADVANCED STUDY UCAS
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Patent Information

Application Number
CN202511988609.9
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-26
Publication Date
2026-05-22

AI Technical Summary

Technical Problem

Existing broadband filter-modulated spectral detection systems struggle to achieve miniaturization, high spatiotemporal resolution, and high signal-to-noise ratio during spectral reconstruction. Furthermore, spectral reconstruction algorithms suffer from optical signal aliasing, making spectral information reconstruction extremely difficult.

Method used

A deep learning-based spectral reconstruction method is adopted, which utilizes the Transformer architecture and KAN network, combined with the joint processing of spatial-spectral information, to perform spectral reconstruction by constructing a spectral reconstruction network, including data preprocessing, network training and hyperspectral image reconstruction, and uses total variational regularization and bilateral filtering to optimize the reconstruction results.

Benefits of technology

It achieves the expansion of the number of spectral channels and high-quality reconstruction of the original spectral signal, improving spectral resolution and detection sensitivity, while reducing the data set size requirement and improving reconstruction accuracy and signal-to-noise ratio.

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Abstract

The invention relates to the field of spectrum reconstruction, in particular to a spectrum reconstruction method for a broadband filtering modulation spectrum detection system, which comprises the following steps of: converting a hyperspectral data set into a training data set according to parameters of the broadband filtering modulation spectrum detection system; building a spectrum reconstruction network model based on deep learning and carrying out training; and pixel splitting point-by-point scanning reconstruction is carried out on a reconstruction target, a hyperspectral image is constructed according to original arrangement, space-spectrum joint optimization is carried out, and finally high-precision spectral image reconstruction is realized. According to the method, the demand of a spectrum reconstruction process on a data set scale is reduced through a pixel splitting point-by-point scanning reconstruction strategy, a Transform architecture is introduced into a reconstruction network, the feature information extraction capability of the network is improved, the strong nonlinear characterization capability of a KAN architecture is combined, the spectrum curve reconstruction precision is improved, and spatial spectrum combined processing is combined, so that the spectrum reconstruction efficiency is improved. The error is further reduced, and high-quality reconstruction of the spectral image is realized.
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