A novel low-quality Raman hyperspectral image reconstruction method for corn component mapping

Raman imaging used to obtain spatial distribution information of chemical components on the sample surface has been widely applied in the nutritional assessment and quality control of cereal grains. However, obtaining high-quality Raman hyperspectral images of cereal grain based on point scanning is time-consuming due to the limitations of the instrumental imaging principle and Raman hyperspectral images acquired in a short time are low-quality with noise interference, limiting its practical application in the visualization of cereal grain composition. To rapidly and effectively achieve high-quality component distribution mappings of cereal grains based on Raman hyperspectral images, we propose a novel Swin Transformer-based method combining spatialspectral denoising and image reconstruction (SwinSSID-IR) for low-quality Raman hyperspectral image reconstruction. The novel denoising network named SwinSSID innovatively introduces Swin Transformer blocks (STB) to effectively eliminate noise from raw low-quality Raman hyperspectral images of corn kernels. Next, the superresolution network named SwinIR rapidly generates high-quality component distribution mappings of corn with exceptional clarity and detail from the denoised low-quality Raman hyperspectral image at characteristic peak. Experimental results show the competitiveness of the proposed method compared with other similar advanced methods and its effective application in non-destructive visual analysis of corn components.