Multi-camera hyperspectral imaging method and system based on deep learning, and medium

Abstract

The invention discloses a multi-camera hyperspectral imaging method and system based on deep learning, and a medium. The multi-camera hyperspectral imaging method provided by the invention comprises the following steps of performing hyperspectral imaging on multiple cameras; constructing a color back projection network module according to spectral response characteristics of the color cameras; constructing a gray scale back projection network module according to the spectral response characteristics of the gray scale camera, constructing an iterative back projection hyperspectral reconstruction network by using the color back projection network module and the gray scale back projection network module, and respectively obtaining a color image and a gray scale image obtained by the color cameras and the gray scale camera for the same imaging target; and inputting the color image and the grayscale image into the trained iterative back projection hyperspectral reconstruction network to obtain a hyperspectral image of an imaging target. The method can effectively guarantee the imaging quality, greatly shortens the hyperspectral imaging time, does not need an additional auxiliary deviceor hardware, greatly improves convenience of hyperspectral imaging, and is wide in application range.

Description

technical field [0001] The present invention relates to a hyperspectral imaging method, in particular to a multi-camera hyperspectral imaging method, system and medium based on deep learning. Use product authenticity identification. Background technique [0002] Hyperspectral images usually contain dozens or hundreds of spectral bands, and have rich and differential spectral information. They have been widely used in remote sensing ground feature classification, medical aided diagnosis, agricultural pest identification, agricultural product drug residue detection, and authenticity identification of daily products. and other fields. However, limited by imaging components and sensor technology, current hyperspectral imaging devices often have disadvantages such as slow imaging time, large volume, and inconvenient portability. However, existing portable hyperspectral imaging solutions, such as multi-lens multispectral imaging systems, mobile phone electric rotating multispect...

AI Technical Summary

Problems solved by technology

However, limited by imaging components and sensor technology, current hyperspectral imaging devices often have disadvantages such as slow imaging time, large volume, and inconvenient portability.

However, existing portable hyperspectral imaging solutions, such as multi-lens multispectral imaging systems, mobile phone electric rotating multispectral imaging components, etc., often require expensive imaging devices or auxiliary hardware, and it is difficult to achieve hyperspectral imaging without auxiliary hardware. Greatly limit the scope of application of these programs

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