Compressed hyperspectral imaging based on image reflection intensity and differential fusion filtering

Existing spectral imaging technology based on compressed coding requires tens of minutes or even hours to obtain higher-quality spectral data. This limits their use in real dynamic scenarios and can only be discussed theoretically. Therefore, we propose a non-iterative algorithm model based on image reflection intensity-estimation aid (IRI-EA). The algorithm studies the approximate proportional relationship between the reflection strength of the RGB diagram and the corresponding spectrum image and reconstructs high-quality spectral data within about 20 s. By solving the difference map of the corresponding spectral scene, combining it with the spectral data of the IRI method, and introducing the total guidance (TG) filter, the reconstruction error can be significantly reduced, and the spectral reconstruction quality can be improved. Compared with other advanced methods, numerous experimental results indicate the advantages of this method in reconstruction quality and efficiency. Specifically, compared with the existing advanced methods, the average efficiency of our method has improved by at least 85%. Our reconstruction model opens up the possibility of processing real-time video and accelerating other methods.